FOREWORD

Work zones have significant impacts on the mobility and safety of transportation network. It is 

important to analyze and understand the anticipated extent and severity of work zone impacts 

associated with various project alternatives before implementing them. A work zone traffic 

analysis also enables practitioners to include appropriate mitigation strategies during project 

planning, design, and in the development of effective transportation management plans (TMP). 

This document is the third volume in the FHWA Traffic Analysis Toolbox: Work Zone Analysis

series. The first volume provides guidance to decision-makers at agencies and jurisdictions 

considering the role of analytical tools in work zone planning and management, and the second 

volume provides specific guidance to the analyst, researcher, or manager in charge of conducting 

a specific work zone analysis project. This document, the third volume in the Work Zone 

Analysis series, focuses on key considerations when applying various modeling tools for work 

zone traffic analysis and provides a decision framework on how to select the best alternatives 

based on a set of performance measures. It serves as a useful resource for practitioners, 

researchers, or managers in understanding the analytical methods and decision framework 

involved in conducting a work zone traffic analysis. It contains step-by-step guidance on 

determining the most suitable tools to perform the work zone analysis. It also presents the 

essential components of a work zone traffic analysis report. To illustrate the process and 

decision framework contained in this document, a variety of case studies are presented to 

demonstrate a diverse set of work zone traffic analysis applications. 

This document serves as Volume XII in the FHWA Traffic Analysis Toolbox. Preceding 

volumes in the toolbox include: Volume I: Traffic Analysis Tools Primer, Volume II: Decision 

Support Methodology for Selecting Traffic Analysis Tools, Volume III: Guidelines for Applying 

Traffic Microsimulation Modeling Software, Volume IV: Guidelines for Applying CORSIM 

Microsimulation Modeling Software, Volume V: Traffic Analysis Tools Case Studies – Benefits 

and Best Practices, Volume VI: Definition, Interpretation, and Calculation of Traffic Analysis 

Tools Measures of Effectiveness, Volume VII: Predicting Performance with Traffic Analysis 

Tools: Case Studies, Volume VIII: Work Zone Modeling and Simulation – A Guide for DecisionMakers, Volume IX: Work Zone Modeling and Simulation – A Guide for Analysis, Volume X: 

Localized Bottleneck Congestion Analysis Focusing on What Analysis Tools Are Available, 

Necessary and Productive for Localized Congestion Remediation, and Volume XI: Weather and 

Traffic Analysis, Modeling and Simulation. 

Mark Kehrli

Director

Office of Transportation Operations 
Notice

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes 

no liability for the use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential 

to the objective of the document. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, 

and integrity of its information. FHWA periodically reviews quality issues and adjusts 

its programs and processes to ensure continuous quality improvement. 
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HOP-12-009

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Traffic Analysis Toolbox Volume XII: 

Work Zone Traffic Analysis – Applications and Decision 

Framework

5. Report Date

April 2012

6. Performing Organization Code: 

7. Author(s)

Lin Zhang, Dorothy Morallos, 

Krista Jeannotte, Jennifer Strasser

8. Performing Organization Report No. 

9. Performing Organization Name and Address

Cambridge Systematics, Inc.

555 12th Street, Suite 1600

Oakland, CA 94607

10. Work Unit No.

11. Contract or Grant No.

DTFH-61-06-D-00004

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

Office of Operations (HOP) 

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period Covered

Final Report

September 2010 to April 2012

14. Sponsoring Agency Code

HOTO

15. Supplementary Notes

FHWA Contracting Officer’s Task Manager (COTM): Jawad Paracha

16. Abstract

Work Zone Traffic Analysis (WZTA) is the process of evaluating and determining the safety and mobility 

impacts within a construction, maintenance, or rehabilitation project. Establishing a procedure for 

analyzing work zone mobility and safety impacts aids agencies in the planning, decision-making, design, 

and financial aspects of the project. This report provides guidance on WZTA applications and presents the 

steps involved in the application of maintenance of traffic alternatives analysis (MOTAA) and decision 

framework. It contains step-by-step guidance to assist the analyst in determining the most suitable tools to 

perform the work zone analysis and presents a procedure for developing and applying models to work zone 

analysis. To illustrate the MOTAA process and decision framework contained in this guidebook, a variety 

of WZTA case studies are presented to demonstrate a diverse set of MOTAA applications.

17. Key Words

Work zone, mobility, traffic analysis, simulation, maintenance of traffic, alternatives 

analysis, tool selection, decision framework, 

MOTAA

18. Distribution Statement

No restrictions. 

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

328

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS 

1.0 BACKGROUND AND OBJECTIVES OF THIS GUIDEBOOK ..................................... 1

1.1 OVERVIEW OF WORK ZONE TRAFFIC ANALYSIS GUIDANCE ..................... 1

1.2 ROLE OF TRAFFIC ANALYSIS TOOLS FOR WORK ZONE APPLICATIONS 2

1.3 ORGANIZATION OF THIS REPORT ........................................................................ 4

2.0 ESTABLISHING A METHODOLOGY FOR WORK ZONE TRAFFIC ANALYSIS . 5

2.1 IDENTIFYING WORK ZONE ANALYSIS GOALS AND OBJECTIVES .............. 5

2.2 SELECTING MEASURES OF EFFECTIVENESS (MOE) AND THRESHOLDS 10

2.3 IDENTIFYING MOT ALTERNATIVE STRATEGIES ........................................... 12

2.4 CONDUCTING FATAL FLAW ANALYSIS ............................................................. 13

2.5 IDENTIFYING NEEDED COORDINATION BETWEEN PROJECTS ................ 13

2.6 STEPS IN THE MAINTENANCE OF TRAFFIC ALTERNATIVES ANALYSIS 

(MOTAA) AND DECISION FRAMEWORK PROCESS ................................................ 14

3.0 SELECTING THE APPROPRIATE TYPE OF TRAFFIC ANALYSIS TOOL .......... 18

3.1 ANALYSIS TOOL CATEGORIES ............................................................................. 18

3.2 KEY FACTORS FOR SELECTING THE ANALYSIS TOOL(S) ........................... 33

3.3 SELECTING THE ANALYSIS TOOL(S) .................................................................. 49

3.4 CHALLENGES IN SELECTING TRAFFIC ANALYSIS TOOLS FOR WORK

ZONE APPLICATIONS ...................................................................................................... 62

4.0 KEY CONSIDERATIONS FOR MODELING APPROACH ......................................... 65

4.1 INTRODUCTION.......................................................................................................... 65

4.2 USING SKETCH-PLANNING AND ANALYTICAL/DETERMINISTIC 

ANALYSIS ............................................................................................................................ 77

4.3 USING TRAVEL DEMAND MODEL ANALYSIS ................................................... 82

4.4 USING TRAFFIC SIGNAL OPTIMIZATION ANALYSIS ..................................... 89

4.5 USING MACROSCOPIC ANALYSIS ........................................................................ 94

4.6 USING MESOSCOPIC ANALYSIS ............................................................................ 98

4.7 USING MICROSCOPIC ANALYSIS ....................................................................... 108

5.0 ESTABLISHING AN MOTAA DECISION FRAMEWORK ....................................... 116

5.1 OVERVIEW OF AN MOTAA DECISION FRAMEWORK .................................. 116

5.2 APPROACHES TO FACTOR PRIORITIZATION ................................................ 119

5.3 WEIGHTING/SCORING TECHNIQUES ............................................................... 132
iv

5.4 DECISION-MAKING TOOLS FOR WORK ZONE ALTERNATIVES ANALYSIS

............................................................................................................................................... 150

6.0 RECONCILING INCONSISTENCIES AND CONDUCTING SENSITIVITY 

ANALYSIS ................................................................................................................................ 163

6.1 VARIATIONS IN THE MEASURES OF EFFECTIVENESS, TOOLS, AND 

OUTPUTS ............................................................................................................................ 163

6.2 ACHIEVING CONSISTENCY OF RESULTS WHEN USING MULTIPLE 

TOOLS ................................................................................................................................. 167

6.3 SENSITIVITY ANALYSIS......................................................................................... 173

7.0 EVALUATING FACTORS FOR MOTAA RECOMMENDATIONS ......................... 184

7.1 WORK ZONE KEY FACTORS ................................................................................ 184

7.2 WORK ZONE SAFETY ANALYSIS ........................................................................ 195

8.0 ANALYSIS REPORT STRUCTURE .............................................................................. 202

8.1 INTRODUCTION AND PROJECT BACKGROUND ............................................ 202

8.2 EXISTING AND FUTURE CONDITIONS .............................................................. 202

8.3 ALTERNATIVES CONSIDERED ............................................................................ 203

8.4 ALTERNATIVES ANALYSIS AND DECISION FRAMEWORK 

METHODOLOGY ............................................................................................................. 203

8.5 ANALYSIS RESULTS, RECOMMENDED ALTERNATIVE, AND MITIGATION 

STRATEGIES ..................................................................................................................... 204

8.6 POST-IMPLEMENTATION AND MONITORING PLAN ................................... 204

9.0 WORK ZONE CASE STUDIES ...................................................................................... 206

9.1 SIMPLE FLAGGING WORK ZONE PROJECTS USING TRAFFIC SIGNAL 

OPTIMIZATION TOOLS – SYNCHRO ......................................................................... 207

9.2 RURAL FREEWAY LANE CLOSURES USING SKETCH PLANNING – 

MARYLAND STATE HIGHWAY ADMINISTRATION (SHA) LANE CLOSURE 

ANALYSIS PROGRAM (LCAP) ...................................................................................... 212

9.3 RURAL FREEWAY LANE CLOSURES USING MESOSCOPIC SIMULATION – 

DYNUST .............................................................................................................................. 218

9.4 RURAL FREEWAY LANE CLOSURES USING MICROSIMULATION – 

CORSIM .............................................................................................................................. 221

9.5 URBAN FREEWAY LANE CLOSURES USING HCM/DETERMINISTIC 

TOOLS –FREEVAL ........................................................................................................... 231

9.6 URBAN FREEWAY LANE CLOSURES USING SKETCH-PLANNING TOOLS – 

ITS DEPLOYMENT ANALYSIS SYSTEM (IDAS)....................................................... 243
v 

9.7 TEMPORARY TWO-WAY ONE-LANE OPERATION ON BRIDGES USING 

TRAFFIC SIGNAL OPTIMIZATION TOOLS – SYNCHRO ...................................... 250

9.8 SIGNALIZED CORRIDOR LANE CLOSURES USING TRAFFIC SIGNAL 

OPTIMIZATION TOOLS – SYNCHRO/SIMTRAFFIC .............................................. 255

9.9 RAMP CLOSURES AT MAJOR INTERCHANGES USING TRAFFIC SIGNAL

OPTIMIZATION AND MICROSIMULATION – SYNCHRO AND PARAMICS .... 264

9.10 CORRIDOR RECONSTRUCTION WITH COMPLEX NETWORK-WIDE 

IMPACTS USING TRAVEL DEMAND MODELING TOOLS – TRANPLAN ......... 280

9.11 CORRIDOR RECONSTRUCTION WITH COMPLEX NETWORK-WIDE 

IMPACTS USING MESOSCOPIC SIMULATION – INTEGRATION ...................... 292

REFERENCES .......................................................................................................................... 307
vi 

LIST OF FIGURES

FIGURE 1. WORK ZONE ANALYSIS STRATEGIES DECISION-MAKING ENGINE . 4

FIGURE 2. MARYLAND STRATEGIC EMPHASIS AREAS FOR WORK ZONE 

SAFETY AND MOBILITY ......................................................................................................... 7

FIGURE 3. NEW JERSEY DOT PROJECT IMPACT MATRIX ......................................... 9

FIGURE 4. MOTAA AND DECISION FRAMEWORK FLOWCHART ........................... 15

FIGURE 5. ANALYTICAL WORK ZONE DECISION FRAMEWORK – MODELING 

APPROACHES ........................................................................................................................... 19

FIGURE 6. WORK ZONE ANALYSIS SCOPING WORK SHEET ................................... 35

FIGURE 7. FRAMEWORK FOR IDENTIFYING APPROPRIATE MODELING 

APPROACH FOR WORK ZONES .......................................................................................... 36

FIGURE 8. WORK ZONE DECISION-MAKING PROCESS ............................................. 37

FIGURE 9. ANALYSIS TOOL SELECTION EXAMPLE: I-123 RECONSTRUCTION 

PROJECT .................................................................................................................................... 57

FIGURE 10. WORK ZONE MODELING SPECTRUM ...................................................... 62

FIGURE 11. LOCATIONS OF QUEUE LENGTH EVALUATION ................................... 66

FIGURE 12. TYPICAL MODEL DEVELOPMENT AND APPLICATION PROCESS 

FOR WORK ZONE ANALYSIS .............................................................................................. 75

FIGURE 13. FREEVAL-2010................................................................................................... 79

FIGURE 14. EXPERIENCED TRAVEL TIME VERSUS INSTANTANEOUS TRAVEL 

TIME .......................................................................................................................................... 104

FIGURE 15. O-D MATRIX CALIBRATION IN MESOSCOPIC MODELS ................... 106

FIGURE 16. EXPECTED LEVEL OF EFFORT BY PROJECT TASKS FOR 

MICROSIMULATION PROJECTS ...................................................................................... 109

FIGURE 17. EXAMPLE OF A TRADITIONAL DECISION-FRAMEWORK ............... 117

FIGURE 18. WEIGHTING/SCORING TECHNIQUE FRAMEWORK .......................... 133

FIGURE 19. AHP HIERARCHICAL TREE EXAMPLE ................................................... 136

FIGURE 20. ILLUSTRATION OF RELATIONSHIPS AMONG FACTORS ................. 142

FIGURE 21. BENEFIT/COST ANALYSIS SUMMARY RESULTS EXAMPLE'........... 149

FIGURE 22. ELEMENTS OF CBR ....................................................................................... 151

FIGURE 23. FOUR SET CASE MODEL – WORK ZONE EXAMPLE ........................... 152

FIGURE 24. WORK ZONE TRAFFIC CONTROL CBR SYSTEM ................................. 154

FIGURE 25. CBR SYSTEM GENERAL SET ...................................................................... 155

FIGURE 26. CBR SYSTEM PROBLEM SET ..................................................................... 155
vii 

FIGURE 27. CBR SYSTEM EFFECTS OBJECT ............................................................... 156

FIGURE 28. CBR SYSTEM SOLUTION SET .................................................................... 157

FIGURE 29. PRELIMINARY STRATEGY SELECTION MATRIX ............................... 160

FIGURE 30. EXAMPLE SECONDARY STRATEGY MATRIX: CONSTRUCTION .. 161

FIGURE 31. LEVEL OF INTERDEPENDENCE MATRIX .............................................. 162

FIGURE 32. I-15 ANALYSIS PROCESS ............................................................................. 171

FIGURE 33. LANE DISTRIBUTION AT 1,000 FEET UPSTREAM OF TAPER FOR 

FIVE PERCENT TRUCKS IN A TWO TO ONE CONFIGURATION ............................. 179

FIGURE 34. PARAMETER COMBINATIONS FOR TWO- TO ONE-LANE 

CONFIGURATION .................................................................................................................. 180

FIGURE 35. SUMMARY OF COST COMPARISON FOR SCENARIOS AND 

MAINTENANCE CREWS ...................................................................................................... 181

FIGURE 36. SENSITIVITY ANALYSIS RESULTS FOR CREWS 1 AND 2 .................. 182

FIGURE 37. SENSITIVITY ANALYSIS RESULTS FOR CREWS 3 AND 4 .................. 183

FIGURE 38. WORK ZONE TRAVEL DELAY ANALYSIS .............................................. 192

FIGURE 39. SSAM SCREENSHOT ..................................................................................... 198

FIGURE 40. FOUR-WAY FLAGGING WORK ZONE LAYOUT ................................... 210

FIGURE 41. THREE-WAY FLAGGING WORK ZONE CONFIGURATION ............... 211

FIGURE 42. I-95 HOWARD COUNTY, MARYLAND STUDY AREA MAP ................. 213

FIGURE 43. I-95 NB, HOWARD COUNTY, LCAP OUTPUT TRIAL 1 – CLOSURE 

FROM 10:00 P.M. WEDNESDAY TO 5:00 A.M. THURSDAY.......................................... 216

FIGURE 44. I-95 NB, HOWARD COUNTY, LCAP OUTPUT TRIAL 2 – CLOSURE 

FROM 11:00 P.M. WEDNESDAY TO 5:00 A.M. THURSDAY.......................................... 217

FIGURE 45. I-95 HOWARD COUNTY, LCAP MODEL OUTPUTS ............................... 217

FIGURE 46. I-10/LOOP 375 CASE STUDY PROJECT AREA MAP .............................. 218

FIGURE 47. I-10/LOOP 375 CASE STUDY – LANE-MILES WITH LOS CHANGES 

FOR SCENARIO 2.0 ................................................................................................................ 221

FIGURE 48. I-80 WORK ZONE STUDY AREA ................................................................. 222

FIGURE 49. DELAY ASSOCIATED WITH VARYING HEADWAY EXPANSION 

FACTORS ................................................................................................................................. 227

FIGURE 50. SUMMARY OF BENEFITS AND COSTS FOR EACH ALTERNATIVES

..................................................................................................................................................... 230

FIGURE 51. I-40 CASE STUDY PROJECT OVERVIEW................................................. 232

FIGURE 52. WORK ZONE IMPACTS AND ANALYSIS TOOLS .................................. 234
viii

FIGURE 53. HCM 2010 FREEVAL-WZ, I-40 CASE STUDY SCENARIOS ................... 236

FIGURE 54. I-40 WORK ZONE SCENARIOS DEMAND PROFILES ........................... 237

FIGURE 55. SPEED CONTOUR PLOT COMPARISONS ................................................ 239

FIGURE 56. I-40 CASE STUDY WORK ZONE SCENARIOS TRAVEL TIME 

COMPARISONS....................................................................................................................... 240

FIGURE 57. I-40 CASE STUDY WORK ZONE SCENARIOS CAPACITY 

COMPARISONS....................................................................................................................... 241

FIGURE 58. I-40 CASE STUDY WORK ZONE SCENARIOS SPEED CONTOUR 

COMPARISONS....................................................................................................................... 242

FIGURE 59. I-496 CASE STUDY PROJECT AREA MAP ................................................ 243

FIGURE 60. I-496 RECONSTRUCTION PROJECT PHASING ...................................... 244

FIGURE 61. I-496 CASE STUDY – TTMS BENEFITS SUMMARY (PHASE 1) ........... 249

FIGURE 62. I-496 CASE STUDY – ARTERIAL UPGRADES BENEFITS SUMMARY 

(PHASE 1).................................................................................................................................. 250

FIGURE 63. MD 23 CASE STUDY PROJECT AREA MAP ............................................. 251

FIGURE 64. MD 23 CASE STUDY – ONE-LANE BRIDGE OPERATIONS LAYOUT 254

FIGURE 65. SHADY GROVE ROAD WORK ZONE AREA MAP .................................. 256

FIGURE 66. SHADY GROVE ROAD – EXTENT OF ANALYSIS .................................. 259

FIGURE 67. MD SHA WORK ZONE ANALYSIS GUIDE MOBILITY THRESHOLDS 

FOR ARTERIALS .................................................................................................................... 263

FIGURE 68. I-465 WEST LEG RECONSTRUCTION, INDIANAPOLIS OVERVIEW 265

FIGURE 69. I-465 CASE STUDY ROCKVILLE ROAD INTERCHANGE 

RECONSTRUCTION STUDY AREA ................................................................................... 266

FIGURE 70. ROCKVILLE ROAD/U.S. 36 INTERCHANGE RAMP CLOSURES ........ 267

FIGURE 71. ROCKVILLE ROAD CASE STUDY – EXAMPLE MODEL EXTENTS .. 271

FIGURE 72. ROCKVILLE ROAD INTERCHANGE CASE STUDY – LOCATIONS OF 

ZONES FOR O-D TABLE ....................................................................................................... 272

FIGURE 73. ROCKVILLE ROAD INTERCHANGE CASE STUDY – EXAMPLE AM 

PEAK PERIOD EXISTING CONDITIONS O-D TABLE ................................................... 272

FIGURE 74. ROCKVILLE ROAD INTERCHANGE – EXISTING CONDITIONS 

MODEL 24-HOUR DEMAND PROFILE ............................................................................. 273

FIGURE 75. ROCKVILLE ROAD INTERCHANGE – AVERAGE NETWORK SPEED

..................................................................................................................................................... 276

FIGURE 76. ROCKVILLE ROAD INTERCHANGE – AVERAGE NUMBER OF 

VEHICLES ................................................................................................................................ 276
ix

FIGURE 77. ROCKVILLE ROAD INTERCHANGE – AVERAGE NETWORK DELAYS

..................................................................................................................................................... 277

FIGURE 78. ROCKVILLE ROAD INTERCHANGE – CUMULATIVE NETWORK 

DELAYS .................................................................................................................................... 277

FIGURE 79. ROCKVILLE ROAD INTERCHANGE RECONSTRUCTION – 

CUMULATIVE NETWORK DELAYS DURING THE CONSTRUCTION PERIOD ... 278

FIGURE 80. I-465 WEST LEG, ROCKVILLE ROAD INTERCHANGE

RECONSTRUCTION DECISION FRAMEWORK EXAMPLE ....................................... 279

FIGURE 81. CLEVELAND INNERBELT PROJECT ........................................................ 281

FIGURE 82. CLEVELAND INNERBELT CORRIDOR SECTIONS ............................... 282

FIGURE 83. OHIO DOT PERMITTED LANE CLOSURE MAP (PLCM) ..................... 284

FIGURE 84. CLEVELAND INNERBELT STUDY: V/C RATIO THEMATIC MAP 

(ALTERNATIVE 1 – FULL CLOSURE)............................................................................... 288

FIGURE 85. CLEVELAND INNERBELT STUDY: V/C RATIO THEMATIC MAP 

(ALTERNATIVE 2 – PARTIAL CLOSURE) ....................................................................... 289

FIGURE 86. CLEVELAND INNERBELT STUDY: MAINTENANCE OF TRAFFIC 

CORRIDORS MAP .................................................................................................................. 291

FIGURE 87. I-80 EASTBOUND EXISTING CONDITIONS ............................................. 295

FIGURE 88. I-80 EASTBOUND CONTRACT STAGING PLAN ..................................... 297

FIGURE 89. I-80 EASTBOUND CONTRACTOR STAGING ALTERNATIVE (THREELANE STAGING PLAN) ......................................................................................................... 298

FIGURE 90. I-80 EASTBOUND ALTERNATIVES ANALYSIS RESULTS ................... 302
x 

LIST OF TABLES

TABLE 1. PROS AND CONS OF SKETCH-PLANNING TOOLS FOR WZTA .............. 20

TABLE 2. PROS AND CONS OF ANALYTICAL/DETERMINISTIC TOOLS FOR 

WZTA .......................................................................................................................................... 22

TABLE 3. PROS AND CONS OF TRAVEL DEMAND MODELS FOR WZTA .............. 23

TABLE 4. PROS AND CONS OF TRAFFIC SIGNAL OPTIMIZATION TOOLS FOR 

WZTA .......................................................................................................................................... 25

TABLE 5. PROS AND CONS OF MACROSCOPIC SIMULATION MODELS FOR 

WZTA .......................................................................................................................................... 26

TABLE 6. PROS AND CONS OF MESOSCOPIC SIMULATION MODELS FOR WZTA

....................................................................................................................................................... 28

TABLE 7. PROS AND CONS OF MICROSCOPIC SIMULATION MODELS FOR 

WZTA .......................................................................................................................................... 31

TABLE 8. PROJECT STAGES ............................................................................................... 38

TABLE 9. FACILITY TYPES.................................................................................................. 39

TABLE 10. TOOL FEATURES ............................................................................................... 40

TABLE 11. AGENCY RESOURCES REQUIRED FOR ANALYSIS ................................. 42

TABLE 12. WORK ZONE CHARACTERISTICS ................................................................ 44

TABLE 13. TMP STRATEGIES ............................................................................................. 46

TABLE 14. PERFORMANCE MEASURES .......................................................................... 48

TABLE 15. EXAMPLE CRITERIA RELEVANCE WEIGHT ............................................ 49

TABLE 16. TOOL RELEVANCE VALUES – PROJECT STAGES ................................... 50

TABLE 17. TOOL RELEVANCE VALUES – FACILITY TYPES ..................................... 50

TABLE 18. TOOL RELEVANCE VALUES – TOOL FEATURES .................................... 50

TABLE 19. TOOL RELEVANCE VALUES – AGENCY RESOURCES REQUIRED FOR 

ANALYSIS .................................................................................................................................. 50

TABLE 20. TOOL RELEVANCE VALUES – WORK ZONE CHARACTERISTICS ..... 51

TABLE 21. TOOL RELEVANCE VALUES – TMP STRATEGIES ................................... 51

TABLE 22. TOOL RELEVANCE VALUES – PERFORMANCE MEASURES ............... 52

TABLE 23. EXAMPLE CRITERIA SCORE CALCULATION .......................................... 53

TABLE 24. EXAMPLE TOTAL SCORE CALCULATION ................................................ 53

TABLE 25. WORK ZONE ANALYSIS SCORING SHEET ................................................ 54
xi

TABLE 26. EXAMPLE WORK ZONE ANALYSIS SCORING SHEET: I-123 

CORRIDOR RECONSTRUCTION PROJECT ..................................................................... 60

TABLE 27. TABLE WORK ZONE LANE CAPACITY DEFAULTS THAT CAN BE 

USED IN THE ABSENCE OF BETTER DATA: SHORT-TERM WORK ZONES ......... 67

TABLE 28. WORK ZONE LANE CAPACITY DEFAULTS THAT CAN BE USED IN 

THE ABSENCE OF BETTER DATA: LONG-TERM WORK ZONES ............................ 68

TABLE 29. WORK ZONE DATA COLLECTION – COMMON DATA ........................... 72

TABLE 30. PARAMETERS TO ADJUST FOR WORK ZONE ANALYSIS IN 

COMMON SKETCH- PLANNING AND ANALYTICAL/DETERMINISTIC ANALYSIS 

TOOLS ......................................................................................................................................... 80

TABLE 31. PARAMETERS TO ADJUST FOR WORK ZONE ANALYSIS IN 

COMMON SIGNAL OPTIMIZATION SOFTWARE .......................................................... 90

TABLE 32. MOBILITY THRESHOLDS FOR ARTERIALS (MD SHA) .......................... 92

TABLE 33. PARAMETERS TO ADJUST FOR WORK ZONE ANALYSIS IN 

COMMON MACROSCOPIC SIMULATION SOFTWARE ............................................... 96

TABLE 34. PARAMETERS TO ADJUST FOR WORK ZONE ANALYSIS IN 

COMMON MESOSCOPIC SIMULATION SOFTWARE ................................................. 100

TABLE 35. LEVEL OF EFFORT FOR VARIOUS MICROSIMULATION PROJECTS

..................................................................................................................................................... 109

TABLE 36. PARAMETERS TO ADJUST FOR WORK ZONE ANALYSIS IN 

COMMON MICROSIMULATION SOFTWARE ................................................................ 111

TABLE 37. FACTOR PRIORITIZATION METHODOLOGIES SUMMARY ............... 120

TABLE 38. PROS AND CONS OF THE DELPHI METHOD ........................................... 121

TABLE 39. DELPHI TECHNIQUE EXAMPLE ................................................................. 122

TABLE 40. DELPHI EXAMPLE SCALE REFERENCE ................................................... 122

TABLE 41. DELPHI EXAMPLE – ROUND 2 QUESTIONNAIRE .................................. 122

TABLE 42. DELPHI EXAMPLE – ROUND 3 ..................................................................... 123

TABLE 43. PROS AND CONS OF FACTOR ANALYSIS ................................................. 125

TABLE 44. LIST OF POTENTIAL VARIABLES .............................................................. 125

TABLE 45. EXAMPLE FACTOR MATRIX ....................................................................... 126

TABLE 46. EXAMPLE FACTOR SCORES ........................................................................ 127

TABLE 47. PROS AND CONS OF THE RANKING SYSTEM ......................................... 127

TABLE 48. PROS AND CONS OF THE RATIO SYSTEM ............................................... 128

TABLE 49. EXAMPLE OF A PAIRED COMPARISON ANALYSIS .............................. 129

TABLE 50. PROS AND CONS OF THE PAIRED COMPARISON ANALYSIS ............ 129
xii

TABLE 51. PROS AND CONS OF THE 100-POINT DISTRIBUTION ........................... 130

TABLE 52. ASSIGNING CRITERIA WEIGHTS – RANKING METHOD ..................... 130

TABLE 53. ASSIGNING CRITERIA WEIGHTS – RATIO METHOD ........................... 131

TABLE 54. ASSIGNING CRITERIA WEIGHTS – PAIRED COMPARISON METHOD

..................................................................................................................................................... 131

TABLE 55. ASSIGNING CRITERIA WEIGHTS – 100-POINT DISTRIBUTION ......... 132

TABLE 56. EVALUATION TOOLS AND METHODS FOR COMPARING 

ALTERNATIVES ..................................................................................................................... 134

TABLE 57. EXAMPLE SCORE FOR ALTERNATIVES .................................................. 135

TABLE 58. SAW EXAMPLE ................................................................................................. 135

TABLE 59. PROS AND CONS OF SAW .............................................................................. 135

TABLE 60. PAIRWISE COMPARISON MATRIX ............................................................ 136

TABLE 61. PAIRWISE MATRIX IN DECIMAL FORM .................................................. 136

TABLE 62. GENERATING THE EIGENVECTOR ........................................................... 137

TABLE 63. CRITERIA RANKINGS/WEIGHTS ................................................................ 137

TABLE 64. PAIRWISE COMPARISON OF ALTERNATIVES ....................................... 137

TABLE 65. AHP FINAL SCORE AND RECOMMENDATION ....................................... 138

TABLE 66. PROS AND CONS OF AHP............................................................................... 138

TABLE 67. PROS AND CONS OF KEPNER-TREGOE METHOD ................................. 147

TABLE 68. PROS AND CONS OF BENEFIT/COST ANALYSIS .................................... 149

TABLE 69. PROS AND CONS OF THE CBR TOOL......................................................... 153

TABLE 70. PROS AND CONS OF THE MATRIX-BASED DECISION SUPPORT TOOL

..................................................................................................................................................... 159

TABLE 71. PERFORMANCE MEASURES AND OUTPUTS BY TOOL TYPE ............ 163

TABLE 72. MOES, TOOLS, AND OUTPUTS UTILIZED FOR I-15 PAVEMENT 

RECONSTRUCTION CASE STUDY .................................................................................... 171

TABLE 73. I-15 PRECONSTRUCTION ANALYSIS RESULTS ...................................... 172

TABLE 74. EXAMPLE SCENARIO DEMAND ADJUSTMENT FACTORS ................. 176

TABLE 75. EXAMPLE SCENARIO CAPACITY ADJUSTMENT FACTORS .............. 176

TABLE 76. NETZONE EXAMPLE GENERAL STATISTICS ......................................... 177

TABLE 77. CLOSURE OPTIONS FOR I-15 DEVORE PROJECT ................................. 195

TABLE 78. QUADRO ROAD USER COST OUTPUT EXAMPLE .................................. 196

TABLE 79. SUMMARY OF SAFETY SURROGATE MEASURES ................................. 200
xiii

TABLE 80. MOTAA REPORTING COMPONENTS CHECKLIST ................................ 204

TABLE 81. WORK ZONE CASE STUDIES SUMMARY ................................................. 206

TABLE 82. SUMMARY OF DATA AND ASSUMPTIONS ............................................... 210

TABLE 83. ALTERNATIVE 1 – SYNCHRO MODEL OUTPUTS ................................... 211

TABLE 84. ALTERNATIVE 2 – SYNCHRO MODEL OUTPUTS ................................... 212

TABLE 85. RECOMMENDED WORK ZONE ALTERNATIVE ..................................... 212

TABLE 86. SUMMARY OF ALTERNATIVES ANALYSIS MODELING RESULTS .. 228

TABLE 87. VALUE OF TRAVEL TIME SAVINGS .......................................................... 229

TABLE 88. INCREMENTAL BENEFITS AND COSTS FOR EACH ALTERNATIVE (IN

THOUSANDS) .......................................................................................................................... 229

TABLE 89. MD 23 CASE STUDY – CLEARANCE INTERVAL CALCULATIONS .... 254

TABLE 90. MD 23 ONE-LANE BRIDGE OPERATIONS – SYNCHRO MODEL 

OUTPUTS .................................................................................................................................. 255

TABLE 91. SHADY GROVE EXISTING CONDITIONS MODEL OUTPUTS .............. 260

TABLE 92. SHADY GROVE ROAD – WORK ZONE BASE MODEL OUTPUTS ........ 262

TABLE 93. SHADY GROVE ROAD – EXISTING (WEEKEND WORK ZONE) MODEL 

OUTPUTS .................................................................................................................................. 263
1 

1.0 BACKGROUND AND OBJECTIVES OF THIS GUIDEBOOK

1.1 OVERVIEW OF WORK ZONE TRAFFIC ANALYSIS GUIDANCE

Work Zone Traffic Analysis (WZTA) is the process of evaluating and determining the mobility 

and safety impacts within a transportation construction, maintenance, or rehabilitation project. 

Establishing a procedure for analyzing work zone mobility and safety impacts aids agencies in 

the planning, decision-making, design, and financial aspects of the project. In order to establish 

an effective and comprehensive process, the work zone traffic analysis plan should address 

broader considerations of mobility and safety impacts that go beyond the work zone itself. 

An agency’s WZTA procedure should, therefore, include the following considerations: 

• Impacts on mobility and safety of the project at corridor, network, and regional levels; 

• Impacts on nearby facilities;

• Impacts on emergency management/incident management;

• Impacts on neighborhoods; 

• Impacts on public property and services; 

• Impacts on affected businesses and developments; 

• Impacts on the environment; and 

• Impacts of concurrent projects located near project. 

Components of a Work Zone Traffic Analysis 

The structure of a WZTA procedure will vary depending on the agency performing the analysis, 

as well as the size and complexity of the project. A WZTA plan will typically include the 

components listed below. More detailed guidance on developing a methodology for analyzing 

work zone traffic impacts is provided in Chapter 2 of this document. 

• Goals and objectives of the Analysis Plan. 

• Identification of construction design, staging, and phasing plans and alternatives.

• Analysis of work zone impacts. This section of the WZTA plan defines how the mobility 

and safety impacts of the alternatives will be calculated. This section will, therefore, 

define the analysis outputs, performance measures, and thresholds used for the analysis.

• Impact mitigation strategies that will address or minimize mobility and safety impacts of 

alternatives. These strategies can include public information, traffic control devices, 

travel demand management, and traffic operations measures.

• Decision framework for choosing a recommended alternative. The criterion or process of 

choosing the recommended alternative or combination of alternatives using data and 

information from the traffic impact analysis results and mitigation strategies assessment.

Role of Work Zone Traffic Analysis 

As previously mentioned, the development of a WZTA can have multiple benefits for agency 

staff as they proceed through the planning, development, and implementation of a project. It 
2 

enables agencies to understand the potential mobility and safety impacts of the project and 

identify the optimal alternative or combination of alternatives that will minimize impacts while 

keeping cost low and construction process efficient. The following details other benefits and 

uses of the WZTA process: 

• Assisting agency staff in allocating resources more effectively and gaining the best value 

for the money. 

• Improving construction management and scheduling. WZTA has the potential to 

improve the coordination and management of multiple projects and construction 

schedules to minimize overall impacts.

• Developing ways of monitoring and managing work zone impacts during construction. 

• Conducting performance assessment pre- and post-construction. Evaluating the 

performance measures of a work zone prior to, during, and post – implementation helps 

provide data and information that aids agency staff in improving and updating work zone 

policies, procedures, and practices in the future. 

Purpose of the Work Zone Traffic Analysis Guide

The purpose of this document is to provide a WZTA applications manual. This guide will be a 

useful resource for practitioners in understanding the analytical methods involved in conducting 

and developing a WZTA. The prior Federal Highway Administration (FHWA) work zone 

guidance, Traffic Analysis Tools Volume VIII and Traffic Analysis Toolbox Volume IX, provided 

guidance on the appropriate consideration of analytical tools in work zone planning and 

management, and the selection of a modeling approach based on these tools.(1,2) This particular 

guide will focus on the work zone applications of these tools. It will provide a step-by-step 

guide customized to the selected tool and type of work zone project. 

1.2 ROLE OF TRAFFIC ANALYSIS TOOLS FOR WORK ZONE APPLICATIONS

Work zone project types can span from simple flagging to complex corridor reconstruction 

projects. Additionally, work zones are temporal and can often take different forms over the 

project duration, depending on the construction staging plans. Measuring mobility and safety 

impacts may be dynamic, depending on these factors, therefore, making calculations and 

analyses more complex. There are a variety of different analysis tools that vary in complexity of 

features and functions. The sophistication of the analysis should be matched to the complexity 

of the road projects and the potential impacts. There is no one tool that can fit all work zone 

types. Traffic analysis tools can provide work zone mobility performance measures, such as 

duration and severity of congestion, queue lengths, and estimated volumes, which can be used to 

further analyze safety, economic, environmental impacts.

The FHWA’s work zone analysis framework is presented in Chapter 3 of the Traffic Analysis 

Tools Volume VIII.

(1) The components of the framework are reflective of the program 

delivery process: 1) System Planning; 2) Preliminary Engineering (PE)/Design; and

3) Construction; however, the types of decisions that need to be made are more important than 

specifically where in the program delivery process it is. These decisions are represented by three 
3 

interrelated decision types that drive the overall work zone decision-making process as a 

decision-making engine:

1. Scheduling Decisions – Decisions impacting when work zone activity will occur, ranging 

from the selection of time of day to days of week to time of year. Analysis tools can be used 

to determine the optimal construction phasing/staging plan that will be cost-effective and 

time efficient.

2. Application Decisions – Decisions pertaining to the construction technique to be used within 

the work zone (e.g., a decision to use cast-in-place techniques rather than a precast 

approach). Analysis tools can play a role in determining which construction design strategies 

can minimize both mobility and safety impacts while improving construction time efficiency 

and cost-effectiveness. Analysis tools also can provide insight on impacts to road users, 

neighboring developments, and nearby projects. 

3. Transportation Management Plan Decisions – Decisions that determine how traffic will be 

managed while work zones are in place. This includes issues of temporary traffic control, 

public information, and transportation operations. Analysis tools can determine how to best 

manage traffic in work zones by aiding agencies in identifying the optimal use and 

combinations of transportation management strategies. 

The decision-making engine concept is visually represented in Figure 1, with each decision type 

represented by one of the three circles.(1) All of the three circles are connected, indicating each 

decision type does not operate in isolation, but is influenced by decisions made in other areas. 

Thus, a decision made about the application (e.g., cast-in-place concrete) may dictate the 

scheduling of the work (e.g., to work in warmer weather months), which, in turn, impacts the 

Transportation Management Plan (TMP) that could be implemented. 

S – Scheduling; A – Application, and TMP – Transportation Management Plan. Adjacent to 

each circle is a smaller circle used to indicate a relative level of finality regarding the decisions 

within each category.
4 

Figure 1. Work Zone Analysis Strategies Decision-Making Engine

(Source: Hardy and Wunderlich, 2008.) 

1.3 ORGANIZATION OF THIS REPORT

The purpose of this guide is to provide a useful resource for practitioners in understanding the 

analytical methods involved in conducting and developing a work zone traffic analysis. This 

document is organized as follows: 

• Chapter 1 provides background and objectives of this guidebook and introduces the rest 

of the document; 

• Chapter 2 describes the methodology for developing and implementing a work zone 

traffic analysis;

• Chapter 3 presents a process on how to select the appropriate type of traffic analysis tool 

to determine the impacts of a work zone; 

• Chapter 4 presents key considerations when applying various modeling tools for work 

zone traffic analysis;

• Chapter 5 provides guidance on developing and applying a Maintenance of Traffic 

Alternatives Analysis (MOTAA) decision framework; 

• Chapter 6 presents guidance on how to reconcile inconsistencies and conduct sensitivity 

analysis;

• Chapter 7 presents a mix of quantitative and qualitative factors that may be considered in 

an MOTAA process;

• Chapter 8 presents the essential components of a MOTAA Report; and 

• Chapter 9 presents case studies to demonstrate the MOTAA process. 
5 

2.0 ESTABLISHING A METHODOLOGY FOR WORK ZONE TRAFFIC ANALYSIS

A WZTA can serve as a critical tool for work zone management and decision-making. This type 

of analysis provides a valuable resource for understanding the various impacts of particular work 

zone strategies. Evaluation of these impacts aids an agency in creating the optimal combination 

of work zone strategies and construction project management decisions that can optimize the 

efficiency of the construction work and minimize costs to the agency, road user, and society.

This chapter describes the methodology for developing and implementing a WZTA. The 

sections provide a sequential order for approaching WZTA, including the following: 

1. The first section defines the WZTA’s goals and objectives;

2. The second section defines or sets performance targets that serve as a measure of the work 

zone strategies’ effectiveness towards achieving the established goals and objectives;

3. The third section provides some suggested work zone alternatives or strategies that are 

commonly used to mitigate work zone impacts on safety, mobility, and constructability of a 

project;

4. The fourth section provides a methodology for narrowing down alternatives that will best 

serve the goals and objectives of the project; 

5. The fifth section provides project coordination for work zone traffic management; and 

6. The last section discusses typical steps in an MOTAA and decision framework. 

2.1 IDENTIFYING WORK ZONE ANALYSIS GOALS AND OBJECTIVES

One of the main components of developing a work zone traffic analysis plan is to define the 

goals and objectives of the analysis. Setting goals and objectives serves several purposes. By 

establishing a set of goals and objectives, an agency is forced to evaluate the traffic impacts, 

benefits, and costs of their work zone project. Using these results, they can direct their resources 

to those strategies that will help them minimize costs or maximize benefits, mitigate traffic 

impacts, and improve safety within the work zones. Second, it provides a direction to guide the 

analysis and the decisions made towards a set of expected and desired results. Third, it sets an 

established target to determine the effectiveness of potential work zone strategies.

Vision

Typically, an agency will have an overall mission statement or policy in regards to work zone 

traffic management, which explains the agency’s vision with respect to maintaining the safety, 

mobility, and quality of work within the work zones. An agency’s goals and objectives will 

typically be guided by this vision statement. Safety, mobility, and constructability are often at 

the core of an agency’s vision statement.

• Safety – An agency’s work zone policy or vision statement for safety will include 

considerations for both users and workers within and around the work zone. Examples of 

safety goals can include reducing crashes and/or reducing worker fatalities and injuries at 

the construction site. 
6 

• Mobility – This pertains to facilitating efficient travel conditions through or around a 

work zone area with a minimum delay compared to baseline travel (preconstruction) 

conditions, while maintaining optimal construction duration/timeframe and not 

compromising safety. 

• Constructability – This refers to efficient and effective planning, designing, and 

building of projects while minimizing impacts on mobility and safety. For agencies, such 

goals may include optimizing construction duration, minimizing construction costs, or 

reducing negative economic impacts on the surrounding community. 

Examples of Agency Vision Statements 

Wisconsin DOT

Creating a work zone traffic impact mitigation strategy requires finding the most cost-effective 

strategy.

New York State DOT

• To provide a high level of safety; 

• To minimize congestion and community impacts by maintaining levels of service close to 

or at preconstruction levels; and 

• To provide contractors with adequate access to the roadway to complete the work 

efficiently while meeting quality requirements of the contract.

Maryland DOT 

The Maryland State Highway Administration (SHA) is committed to maintaining optimum 

worker safety while having traffic traveling smoothly and safely through and around work areas 

at all times.(3) Careful consideration of work zone impacts should begin during planning and 

continue through design, construction, and post-construction review. 
7 

Figure 2. Maryland Strategic Emphasis Areas for Work Zone Safety and Mobility 

(Source: Maryland State Highway Administration, 2012.) 

Developing Project Goals and Objectives

Strategic goals and performance objectives should be established in accordance with the 

work zone vision. The goals should directly correlate to the work zone safety, mobility, and 

constructability concerns and needs of the agency. The performance objectives then drive the 

selection of work zone alternatives or strategies that are designed to meet the established goals.

Before setting the specific goals and performance objectives, the agency should review project 

details, such as the design, plans, scope, and environmental documents for important safety, 

mobility, and constructability constraints and considerations that help form the basis of the goals 

and objectives. Examples of safety, mobility, and constructability considerations include: 

• Safety:

• Worker and road user safety; 

• Impacts to emergency response; and 

• Injury/fatality crash rates.
8 

• Mobility:

• Road user costs; 

• Average Daily Traffic (ADT);

• Delay or queue length;

• Truck percentages;

• Lane closure hours; 

• Transit operational impacts;

• Bike/pedestrian access; and

• Detour availability and impacts.

• Constructability:

• Duration of construction; 

• Number of construction stages; 

• Maintenance considerations;

• Environmental constraints; and 

• Business and residential impacts.

The goals and objectives can be qualitative and/or quantitative. A qualitative goal may be to 

promote public awareness and motorist travel information of work zone traffic conditions. A 

quantitative goal may be to reduce work zone delays by X percent within a certain time period. 

If quantitative, it also may be necessary for an agency to conduct data collection efforts and a 

preliminary analysis to identify the specific areas of concern that may potentially need 

mitigation. There may be several areas of concern the agency may like to analyze or mitigate. 

However, the number of concerns may exceed the agency’s monetary and time resources, thus it 

may be necessary for the agency to prioritize which specific project design or work zone 

strategies should be analyzed. The agency also can qualitatively evaluate the impacts of the 

project’s particular work zone characteristics as a way of prioritizing. For instance, the New 

Jersey DOT Traffic Mitigation Guidelines for Work Zone Safety and Mobility rate project 

characteristics based on if they will have high, medium, or low impacts. The impact rating helps 

New Jersey DOT determine the particular mitigation strategies needed to achieve their goals of 

maintaining work zone mobility and safety. Figure 3 shows a sample impact matrix developed 

by the agency.(4)
9 

Project Overall Impact Assessment

Project Characteristic Project Data (Qualitative) Project Impact Level

Primary Project Characteristics

Road User Cost

Retail Business Disruption

Neighborhood Disruption

Disruption to Employers/Activity

Political Sensitivity

Impacts of Local/Developer Projects

Secondary Project Characteristics

Duration of Contract

Number of Construction

Capacity Reduction (Percent)

Roadway Type

ADT

Seasonal Traffic Increase

Expected Delay

Project Length

Urbanization

Truck Volume

Public/Media Attention

Figure 3. New Jersey DOT Project Impact Matrix

(Source: New Jersey Department of Transportation, 2008.) 

Example Goals and Objectives 

Once the project details and plans have been reviewed and any preliminary analysis has been 

completed, the particular goals and associated performance objectives should be established. 

Goals and performance objectives will vary depending on project characteristics, agency 

policies, and resources. The following serves as example goals and corresponding performance 

objectives:

• Goal 1 – Keep work zone congestion at preconstruction levels. 

• Objective 1 – Optimize lane closure strategies; evaluate impact of detours; 

alternative work schedules (i.e., nighttime, weekend work). 

• Goal 2 – Reduce work zone-related crashes by X percent.

• Objective 2 – Determine impacts of traffic incident management strategies, 

intelligent transportation systems (ITS) strategies, enhanced monitoring on crash rate. 

• Goal 3 – Promote public awareness.

• Objective 3 – Keep the public apprised of Maintenance of Traffic (MOT) plans and 

their projected impacts.

New Jersey identified a potential list of work zone goals and objectives:(4)

• Provide work zone speed reductions of 10 mph compared to preconstruction speed limit; 

• Reduce traffic volumes by X percent;
10 

• Decrease average daily traffic by Y vehicles during peak period; 

• Maintain level of service (LOS) D or better;

• Reduce expected delay by Z percent; 

• Keep average delay per vehicle to less than X minutes during the peak period; 

• Maintain vehicle or person throughput by increasing auto occupancy and transit 

ridership; 

• Promote public awareness;

• Keep congestion no worse during construction than before;

• Mitigate community impact;

• Maximize safety of workers and the traveling public; and

• Keep road user costs below X dollars per day. 

Concurrent with setting goals and objectives is selecting the appropriate analysis tool, discussed 

in Chapter 3. The type of analysis tool can vary from simple to complex and also will depend on 

the type of project, the goals, and available data and resources. 

2.2 SELECTING MEASURES OF EFFECTIVENESS (MOE) AND THRESHOLDS

Once goals and objectives have been established, measures of effectiveness (MOE) or thresholds 

should be set to determine the performance standards for the safety, mobility, and 

constructability of the work zone. The MOEs or thresholds selected should be customized to the 

agency’s goals and project characteristics. The MOEs and thresholds set a minimum 

performance standard that the objectives must meet or exceed. They also will help in evaluating 

the different objectives or alternatives as a way of determining which strategies best fit the goals 

of the project. For instance, for Goal 1 listed in the Example Goals and Objectives from 

Section 2.1, “keep work zone congestion at preconstruction levels,” an MOE could be LOS, 

delay, travel time, or road user costs. Based on the MOE(s) selected, the agency will select an 

analysis tool to determine the LOS, delays, travel time, or road user costs of the work zone area 

prior to construction. Then, each of the proposed alternatives can be analyzed using the selected 

analysis tool and compared against the results of the preconstruction scenario. 

Selecting an MOE will depend on the goals, project characteristics, and agency resources. 

However, determining an appropriate MOE or threshold also will require some analysis of the 

existing conditions within the work zone area. For instance, the agency should have some data 

supported knowledge regarding the transportation facility’s average daily traffic (or hourly 

volumes), applicable intersection counts, ramp metering and signal control timings, travel times, 

delays/queue lengths, bottleneck locations and severity and roadway capacity. These provide 

background information regarding the current performance of the roadway facility. It also 

provides a way of determining the performance standards the agency may want to meet or 

improve upon prior to construction. 

The FHWA recently released a primer on work zone safety and mobility performance 

measurement.(5) It describes various work zone MOEs, and provides guidance to help agencies 

select and implement measures that make sense for their own work zone programs. 
11 

Example MOEs and/or Thresholds

The following provides some examples of performance measures used to determine the 

appropriate traffic mitigation strategies to use in work zone projects.

Maryland State Highway Administration (MD SHA)

MD SHA uses mobility thresholds that are calculated according to their Work Zone Lane Closure 

Analysis Guide. MOEs, according to the guide, include LOS, intersection control delay, and 

arterial travel time based on the type of facility being analyzed.

Ohio DOT

The Ohio DOT has two levels of MOEs that particular strategies are measured against. The first 

is defined by lane closure duration and the other uses queue thresholds. 

• Permitted Lane Closure Map (PLCM) – Each Ohio district is required to prepare a 

permitted lane closure map (PLCM) that specifies the allowable times a lane may be 

closed on a freeway within that district. The predefined times are determined based on 

work zone capacity. For any proposed lane closures that do not meet the PLCM, the 

district must analyze the impacts on motorists.

• Queue Thresholds Analysis – A quantitative queue analysis should be performed for 

those lane closures proposed outside of PLCM allowable times. This analysis compares 

existing queues with expected queues caused by the lane closure. A vehicle will be 

considered part of a queue if its average operating speed is approximately 10 mph or less. 

The district may use analysis tools, such as QUEWZ-92, Synchro/SimTraffic, CORSIM 

or similar programs to model the expected queues. The allowable queue thresholds are 

defined below: 

• For queues less than 0.75 mile, the work zone impacts are acceptable.

• For queues greater than 0.75 mile and less than 1.5 miles, the work zone impacts are 

acceptable if the queue exceeds 0.75 mile for 2 hours or less. Where queues are 

expected to exceed 0.75 mile for any period of time, additional advanced work zone 

warning signing should be specified. 

• For queues longer than 0.75 mile for more than 2 hours or longer than 1.5 miles for 

any period of time, the work zone impacts are unacceptable.

Wisconsin DOT

For any individual project, the most cost-effective mitigation strategy (or combination of 

strategies) should be selected. To determine the most cost-effective mitigation strategy, 

Wisconsin DOT uses Road User Costs (RUC) as the MOE. The agency also sets a particular 

RUC threshold that the project’s cost for mitigations strategies must meet. For any individual 

project, the agency costs for mitigation should be less than 10 percent of the monetary value of 

the RUC associated with the construction. This is equivalent to a benefit/cost ratio of 10 or 

higher for mitigation activities.
12 

2.3 IDENTIFYING MOT ALTERNATIVE STRATEGIES

MOT alternative strategies can be evaluated and fit several categories, including the following: 

• Construction Approach – These strategies minimize traffic disruption by optimizing 

construction duration or by ensuring that the road has sufficient capacity during peak 

travel periods. Strategies within this category include:

• Staging/sequencing of construction phases; 

• Lane closure and ramp closure alternatives;

• Alternative work schedules, such as night work or weekend work; and 

• Innovative contracting. 

• Traffic Control Operations – These strategies increase safety and capacity. Strategies 

within this category include:

• Speed limit reductions;

• Truck restrictions; 

• Signal timing, coordination, and phasing improvements; 

• Reversible lanes; and

• Physical barriers.

• Public Information – These strategies can include public outreach efforts to inform the 

community about the project, as well as the provision of traveler information regarding 

travel conditions at the work zone. Strategies within this category include: 

• Public outreach efforts through community meetings, newsletters/pamphlets, and 

project web sites;

• Dynamic message signs;

• Highway advisory radio; 

• Telephone hotlines (i.e., 511); and 

• Closed-circuit television (CCTV).

• Incident Management and Enforcement – These strategies work to efficiently plan for, 

detect, respond, and clear incidents that can occur within the work zone area. These 

strategies can include:

• Incident management plans; 

• Traffic management centers and the use of ITS technologies for advanced detection 

and response technologies; 

• Emergency service patrols; and

• Enhanced police enforcement. 

• Travel Demand Management (TDM) – This includes strategies that shift motorists 

from driving alone to multi-occupant modes in order to reduce congestion along the work 

zone, especially during critical travel periods. TDM strategies for work zones include: 

• Rideshare incentives;

• Transit incentives and improvements; 

• High-occupancy vehicle (HOV) facility provisions and improvements; and 

• Park-and-ride provisions. 

The selected MOE and related threshold(s) can be used to filter out MOT alternatives that 

do not meet certain criteria, will not minimize traffic impacts, or will make traffic 

conditions worse. 
13 

2.4 CONDUCTING FATAL FLAW ANALYSIS

Fatal flaw analysis determines if an individual alternative has one or more defects that 

prevent it from being successfully implemented. One of the key inputs to a fatal flaw analysis 

is the establishment of clear goals and objectives for the work zone project. Using these as a 

guide, a multidisciplinary team reviews the proposed alternatives and performs an initial 

screening of the identified alternatives. This screening identifies those alternatives that deviate 

from the stated goals in a significant manner, and determines if that deviation is substantial 

enough to remove the alternative from further consideration before more detailed analyses are 

completed. While this review may result in the elimination of alternatives, it also can result in 

refinement of the proposed alternatives. However, care must be taken not to cut too deeply with 

the fatal flaw analysis process, as some of the more innovative strategies that may utilize 

unconventional approaches could be lost in this manner. 

2.5 IDENTIFYING NEEDED COORDINATION BETWEEN PROJECTS

An important step that should be considered during the planning and design stages of the project 

development process is the identification of coordination between projects, other infrastructure 

improvements or maintenance activities, and agencies. Project coordination strategies are a 

critical part of the work zone traffic analysis process, as they can contribute to the reduction 

of traffic impacts and project duration. Project coordination can begin with discussions with 

other transportation and public works agencies and other stakeholders that may be impacted by 

the work zone. While coordination makes intuitive sense, the reality of work zone projects often 

involves a number of different agencies, contractors, funding sources, stakeholders, etc. that may 

pose logistical challenges for coordination efforts. In creating a work zone traffic analysis plan 

or a transportation management plan for a particular highway maintenance, reconstruction, 

and/or rehabilitation project, the following coordination strategies and considerations may be 

employed:

• Coordination between Construction Projects – This coordination strategy involves 

considerations for nearby state, local, and regional projects that may be occurring 

concurrently. This strategy involves coordinating, phasing, and scheduling these multiple 

projects in a way that will minimize delays and impacts on motorists, businesses, and 

neighborhoods, while reducing potential construction delays for all projects. This 

coordination effort also may involve assessing the potential combined impacts of the 

multiple projects at the corridor level in order to identify potential conflicts between 

projects and other coordination opportunities. 

• Coordination with Special Events and Major Traffic Generators – Considerations for 

special events and major traffic generators that may be impacted by or could impact the 

project under consideration also are essential for reducing mobility and safety impacts. 

Construction schedules and sequencing, alternate routes, and other work zone strategies 

can be structured and coordinated with these events and traffic generators in mind in 

order to minimize disruption to motorists, businesses, and neighborhoods. 

• Utilities Coordination – Coordinating and scheduling utility work within and around the 

work zone project is critical in minimizing disruptions to utility work and the overall 

construction duration. Coordinating the construction work with utility work also can 
14 

identify additional utility improvements, roadway work and utility and traffic device 

installations (i.e., detectors, cameras, metering) that can be done concurrently with the 

work zone project. Consolidating the various improvements can reduce the need for 

future work zones and minimize road user disruptions. 

• Right-of-Way and Permit Coordination – This type of coordination strategy involves 

considerations for right-of-way needs and issues that may impact construction duration. 

• Coordination with Other Transportation Infrastructure Improvements and 

Maintenance Activities – This involves coordination with non-highway transportation 

facilities and associated agencies. Such facilities would include transit, railroads and 

intermodal facilities. As with other coordination efforts, this also will help to minimize 

mobility and safety impacts network- or regionwide. 

2.6 STEPS IN THE MAINTENANCE OF TRAFFIC ALTERNATIVES ANALYSIS 

(MOTAA) AND DECISION FRAMEWORK PROCESS

Figure 4 presents the steps involved in the application of a maintenance of traffic alternatives 

analysis (MOTAA) and decision framework. They vary depending on the agency work zone 

policies and resources. While work zone traffic analysis can vary in complexity, there are 

common components applied by various agencies in their work zone alternatives analysis and 

decision-making procedures. These common elements are described below. The common 

components or steps in an MOTAA and decision framework have been categorized into the 

various project stages that may occur in project planning and data collection, impact analysis, 

decision-making criteria, impact mitigation, and monitoring and post-implementation.
15 

 

Conduct Post 

Implementation Review

Begin 

Work Zone 

Analysis

Conduct Fatal 

Flaw Analysis

Determine Criteria, 

MOEs, and Thresholds

Define the Problem

Scope the Project

Identify Alternative

Determine Analysis 

Tool(s)

Continue 

Development

Are Additional 

Alternatives 

Needed?

Develop Baseline 

Model

Identify Data Needs

Collect Data

Obtain Model Output

Develop Alternative 

Model

Calibrate Model

Validate 

Model

Does Model 

Output Match 

Field Data?

Continue 

Analysis

Does Another 

Feasible 

Alternative 

Exist?

Evaluate 

Alternatives

Does Alternative 

Meet Needed 

Threshold?

Evaluate 

Implementation

Will Alternative 

Be Used in 

Construction?

Determine Mitigation 

Strategy

Determine Optimal 

Alternative Using 

Decision Framework

Monitor Work Zone

Reject 

Alternative

Recommend Alternative

Analysis 

Complete

Fail

Yes

No

Pass

Yes

No

Yes No

No

Yes

No

Figure 4. MOTAA and Decision Framework Flowchart
16 

Planning and Data Collection 

During this stage of the alternatives analysis, agency staff develops an analysis approach using 

existing conditions information, project characteristics, and traffic and/or economic data within 

and around the work zone. Considerations involved in this stage could include: 

• Determination of Goals and Objectives – This step answers questions such as what is 

the purpose of the analysis or what are the priority impacts that need to be mitigated? 

Section 2.1 provides examples and further information on setting work zone goals and 

objectives.

• Project Scoping – This step involves reviewing the project characteristics and work zone 

strategies in order to gain a better understanding of the potential mobility and safety 

impacts within the work zone, as well as impacts on nearby projects, businesses, and 

neighborhoods. 

• Determine Criteria, Thresholds, or Measures of Effectiveness – This step involves 

determining performance measures by which to compare proposed alternatives against. 

The selected thresholds vary based on the goals and objectives of the agency. Section 2.2 

provides further information and examples of how to select and develop the thresholds or 

measures.

• Determine appropriate analysis tool(s) – Using the information gathered from the data 

collection efforts and existing conditions data, an appropriate analysis tool that matches 

the level of complexity of the project could be selected at this stage of the project. 

Chapter 3 of this guide provides detailed information on how to select the appropriate 

analysis tool based on the work zone’s characteristics.

Impact Analysis 

Most highway projects go through some type of impact analysis to determine any negative 

impacts the project may have on the mobility, safety, and environmental conditions of the study 

area, as well as the surrounding community, corridor, or region. At this stage of the project, the 

various work zone alternatives also can be analyzed for their mobility, safety, environmental, and 

financial impacts within and beyond the work zone area. This analysis will contribute to 

selecting the appropriate set of work zone alternatives or strategies that will minimize impacts 

while maintaining or reducing the project duration and costs. The steps involved in the impact 

analysis may vary depending on the selected analysis tool. Common components may be as 

follows: 

• Perform Analysis – How the analysis is performed will depend on the type of tool 

utilized. Chapter 4 of this document provides further information on performing the 

analysis.

• Calibration and Validation of Model – If the selected analysis tool requires modeling, 

an existing condition or preconstruction model is typically created. This preconstruction 

model, often called the “no-build” model, provides the baseline for the mobility and 

safety performance of the roadway facility. The calibration and validation process 
17 

ensures that this baseline model accurately reflects the existing travel conditions and 

patterns on the ground. 

• Obtaining Model Outputs – The model outputs also will vary depending on the type of 

tools and analysis selected. For instance, simulation tools may produce outputs, such as 

delay, travel time, and volumes. Sketch-planning tools may include measures, such as 

LOS or a benefit/cost ratio.

Decision-Making Criteria

The decision-making criteria an agency may choose to apply in determining the recommended 

alternative (or combination of alternatives) may be greatly influenced by the goals and 

objectives, the data collection efforts, and the level and complexity of their impact analysis. The 

typical steps involved in this stage of the project often include the following: 

• Determining whether the alternative(s) meets the thresholds or decision framework 

criteria; and

• Selecting the alternative(s) with the least impacts or the best fit for the criteria 

established.

For further information on developing a decision-making framework, refer to Chapter 5 of this 

document. 

Impact Mitigation 

This stage of the project and the alternatives analysis depends on the results of the impact 

analysis and the decision-making criteria stage. The alternatives analysis indicates the types of 

impacts to expect from the recommended alternative or combination of strategies. The impact 

mitigation stage identifies the potential mitigation strategies that can aid in further reducing the 

impacts. Further analysis may be conducted to determine which combination of mitigation 

strategies may prove to be most efficient and cost-effective. Types of mitigation strategies can 

include public information, traffic control devices, travel demand management, and traffic 

operations measures. 

Monitoring and Post-Implementation

The monitoring and post-implementation efforts include monitoring the performance of the work 

zone and the effectiveness of the recommended alternatives and/or mitigation strategies during 

the duration of the construction period and after. Developing a monitoring plan can serve as a 

way to determine the effectiveness of the work zone traffic analysis and the alternatives analysis 

process by determining impacts and mitigating mobility and safety issues. The results of the 

monitoring efforts and post-implementation analyses also can serve as feedback to improve the 

agency’s current analyses and decision-making methodologies. 
18 

3.0 SELECTING THE APPROPRIATE TYPE OF TRAFFIC ANALYSIS TOOL

For the purpose of assessing work zone impacts, different transportation agencies have used a 

variety of transportation analysis tools. Each tool is unique and has its own set of capabilities 

and limitations. After an overview of the various categories of traffic analysis tools, this chapter 

discusses the key factors, challenges, strengths, and limitations to consider before selecting a 

suitable analysis tool to determine the impacts of a work zone. The following presents these 

considerations through a step by step approach that is adapted from the tool selection procedures 

described in the Traffic Analysis Toolbox Volume IX: Work Zone Modeling and Simulation – A 

Guide for Analysts(2) and Traffic Analysis Toolbox Volume II: Decision Support Methodology 

for Selecting Traffic Analysis Tools.(7)

3.1 ANALYSIS TOOL CATEGORIES

This section provides an overview of the various categories of traffic analysis tools in order to 

provide guidance to users in selecting the appropriate analysis tool for their project. The section 

below will describe the typical features of each tool category as well as each tool class’s 

strengths and limitations. Guidance on what inputs to consider adjusting and how to make tools 

function better for work zone traffic analysis projects are provided in Chapter 4. 

As outlined in the Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer and Volume 

VIII: Work Zone Modeling and Simulation – A Guide for Decision-Makers, numerous traffic 

analysis methodologies and tools have been developed by public agencies, vendors, research 

organizations, and consultants.(1,8) These traffic analysis tools can be categorized into seven 

types, in order of increasing complexity: 

1. Sketch-planning tools; 

2. Analytical/deterministic tools (HCM-based);

3. Travel demand models; 

4. Traffic signal optimization tools;

5. Macroscopic simulation models; 

6. Mesoscopic simulation models; and 

7. Microscopic simulation models.

For the purpose of the guidance following this chapter, sketch-planning and analytical/

deterministic tools will be combined into a single category referred to as sketch-planning and 

analytical/deterministic tools.

There are many factors that go into determining which tool is right for the job, and there is no 

“one-size-fits-all” approach. More than one tool might be needed to address the analysis needs, 

particularly as the project progresses from planning to design to construction. As shown in 

Figure 5, the higher the level of detail needed from the analysis, the higher the level of resources 

are required to make successful use of a particular tool.(2)
19 

Figure 5. Analytical Work Zone Decision Framework – Modeling Approaches

(Source: Hardy and Wunderlich, 2009.) 

Sketch-Planning Tools 

Sketch-planning methodologies and tools produce general order-of-magnitude estimates of travel 

demand and traffic operations in response to transportation changes. Often, sketch-planning 

tools are based upon simple queuing techniques or volume-to-capacity relationships from the 

Highway Capacity Manual (HCM). Sketch-planning tools perform some or all of the functions 

of other analytical tool types, using simplified analyses techniques and highly aggregated data. 

They typically are the simplest and least costly of the traffic analysis tools.

The strength of sketch-planning tools rests upon their relative ease of use and ability to 

facilitate a rapid analysis. Typically, a sketch-planning tool requires fewer resources and less 

staff training to deploy than a mesoscopic or microscopic simulation model, since they are 

simpler in terms of data requirements, calibration, and interpretation of the results. Regarding 

work zones, an analysis using a sketch-planning tool is normally quite rapid, including both the 

input of the data and the model run time. This is important in cases where a decision needs to be 

made quickly; or the agency desires a less resource-intensive analysis (e.g., for a project with a 

modest level of expected work zone impacts). 

The weaknesses of sketch-planning tools are their limited network complexity and inability to 

generate potential network impacts, inability to consider vehicle interactions, and high-level 

analysis. For example, QUEWZ-98 only allows simple “pipeline” analysis without the ability to 

model cross-streets and detour routes. However, some of the more complex sketch-planning 

tools (e.g., QuickZone) do include the ability to model a detour route. In the end, the results 

from any sketch-planning tool will be relatively high-level (e.g., average or maximum queue).
20 

Table 1 lists some general pros and cons of using sketch-planning tools for work zone traffic 

analysis.

Table 1. Pros and Cons of Sketch-Planning Tools for WZTA 

Pros Cons

• Easy to use

• Low cost

• Quick results

• Low complexity

• Less data intensive

• Reduced technical skill set

• Easy interpretation of results

• Order-of-magnitude output

• Limited network complexity

• Limited presentation capabilities

• Low-analytical robustness

• Traffic diversion typically not modeled explicitly

A list of sketch-planning tools can be found in the Traffic Analysis Toolbox Volume I: Traffic 

Analysis Tools Primer.

(8)
21 

Analytical/Deterministic Tools (HCM-Based)

HCM procedures are capable of analyzing facility performance (capacity, density, speed, delay, 

and queuing) as a function of demand, geometry, and traffic controls. These methods and tools 

usually provide quick results. They are reliable for predicting whether a facility will be 

operating above or below capacity and they have been well tested through significant fieldvalidation efforts. HCM procedures are generally limited in their ability to evaluate system 

Sketch Planning in Work Zone Traffic Analysis – QuickZone

Maryland/Virginia Woodrow Wilson Bridge: I-95(9)

(Source: Mitretek Systems, 2004.) 

The Woodrow Wilson Bridge Project involved the replacement of the existing six-lane bridge with a dual-span 

bridge that increases the number of traffic lanes. The original construction staging plan included lane closures 

during overnight hours (12:00 a.m.-4:00 a.m.) and two temporary openings in the median barrier to divert traffic 

around the construction area. This work zone plan would result in a construction duration of four to six months. 

However, the contractor noted that this original closure duration was insufficient. The contractors requested a 

larger construction time window for set-up, as well as production time. Therefore, alternative staging plans were 

evaluated.

QuickZone was used to analyze various construction staging scenarios to choose an optimal alternative that 

could take place without causing severe mobility impacts. QuickZone was chosen because: 1) it could analyze 

multiple scenarios quickly; 2) MD SHA already had their own customized version of the tool; and 3) it generates 

the exact performance measures the agency needed for the analysis. The results of the analysis showed that time 

for lane closures can be extended to hours between 9:00 p.m. and 5:00 a.m. without significant impacts to 

motorists.

Woodrow Wilson Bridge QuickZone Network

(Source: Mitretek Systems, 2004.) 
22 

effects and vehicle interactions. Most of the HCM methods and models assume that the operation 

of one intersection or road segment is not adversely affected by conditions on the adjacent 

roadway. Therefore, HCM procedures are of limited value in analyzing the effects of queuing. 

Table 2 lists some general pros and cons of using analytical/deterministic tools for work zone 

traffic analysis.

Table 2. Pros and Cons of Analytical/Deterministic Tools for WZTA 

Pros Cons

• Widely used and trusted

• Typically can be performed by in-house staff

• Quick analysis time

• Reduced technical skill set

• Reduced analysis costs

• Cannot assess systemwide impacts

• Not well suited for high levels of congestion

• Unable to reflect atypical roadway geometries

• Unable to analyze transitions in operations between one 

system state and another (e.g., unable to analyze 

transition from noncongested to congested conditions)

A list of HCM methodologies can be found in the Traffic Analysis Toolbox Volume I: Traffic 

Analysis Tools Primer.

(8)

Analytical/Deterministic Tools in Work Zone Traffic Analysis – FREEVAL

I-40, Wake County, North Carolina(10)

FREEVAL was the selected tool used in the I-40 project in Wake County, North Carolina. This project 

evaluated the impacts of a work zone where a lane was added in each direction along four miles of the Interstate 

between State Road 1728 and the interchange with I-440/U.S. I-64. In planning for this project, two work zone 

configurations were considered: 1) a two-lane pattern representing day-time operations during the work zone 

construction where work will be completed behind barriers while maintaining all travel lanes open for traffic; 

and 2) an off-peak lane closure with nighttime construction that would require full closure of several travel 

lanes, leaving only one lane open to traffic. A total of six scenarios were modeled using these two work zone 

configurations in conjunction with other strategies.

FREEVAL was used to evaluate the mobility impacts of the scenarios. FREEVAL was chosen due to its ability 

to measure the impacts of freeway ramps and weaving segments, ease of use and accessibility, and its ability to 

generate performance measures, as well as graphical outputs helpful for analysis. The analysis measures 

reported were average travel time, mainline travel speed, total system delay, maximum queue length, and 

number of un-served vehicles left on the facility after the analysis time period. The FREEVAL analysis showed 

the impacts of work zone speed reductions, barriers, and nighttime lane closures on speeds, travel times, and 

vehicle queues.

FREEVAL (HCM 2000) Network

(Source: Schroeder and Rouphail, 2010.)
23 

Travel Demand Models

Travel demand models have specific analytical capabilities, such as the prediction of travel 

demand and the consideration of destination choice, mode choice, time-of-day travel choice, and 

route choice. These are mathematical models that forecast future travel demand based on current 

conditions, and future projections of household and employment characteristics. Traditionally, 

they are large regional planning models used by metropolitan planning organizations (MPO) and 

were originally developed to determine the benefits and impacts of major highway improvements 

in metropolitan areas. A distinguishing feature of travel demand models is their geographic 

coverage, which generally includes an entire metropolitan area: a city, its suburbs, and the 

adjacent counties.

For work zone analysis, a strength offered by travel demand models is their ability to predict 

areawide traffic redistribution. For example, if an agency is considering closing an important 

urban, freeway-to-freeway interchange for several months while it is rebuilt, a travel demand 

model could help evaluate the overall changes in total daily traffic volumes on various roadways 

throughout the region. Another important consideration is that most major metropolitan areas 

already have an established travel demand model, which could be used either as the sole analysis 

tool, or as a foundation for developing a transportation network for another model type. 

However, travel demand models have limited capabilities to estimate accurate changes in 

operational characteristics (such as speed, delay, and queuing), resulting from the 

implementation of operational strategies and changes (including the effects associated with 

roadwork construction). Because these models are prepared at a broad regional scale, they lack 

detail that may be critical to the analysis objective. For example, they may model only one or 

two time periods (such as the AM peak hour or the daily average), which may not be sufficient 

for analyzing time-specific work zone traffic management strategies. 

Table 3 lists some general pros and cons of using travel demand models for work zone traffic 

analysis.

Table 3. Pros and Cons of Travel Demand Models for WZTA 

Pros Cons

• Low cost (assuming readily available 

regional model)

• Able to predict areawide traffic redistribution

• Able to predict mode choice, destination choice, 

time-of-day travel choice, route choice, 

trip generation

• Useful for large-scale, long-term projects

• Only accounts for recurring congestion conditions

• Unable to accurately evaluate operational strategies 

such as ITS

• Less useful for small-scale, short-term work zones

• Limited accuracy for intersection 

turning movements

• Not suitable for time-specific work zone analysis

• High-technical skill set

• Assumes network is operating in equilibrium
24 

A list of travel demand models can be found in the Traffic Analysis Toolbox Volume I: Traffic 

Analysis Tools Primer.

(8)

Traffic Signal Optimization Tools

Traffic signal optimization tools are primarily designed to develop optimal signal phasing and 

timing plans for signalized intersections, arterial streets, or signal networks. This may include 

capacity calculations; cycle length; splits optimization, including left turns; and 

coordination/offset plans. With respect to work zones, traffic signal optimization tools are useful 

when developing a signal plan for a temporary traffic signal, or analyzing signal plans when a 

detour route directs traffic to an existing signalized arterial roadway. 

The primary limitation of traffic signal optimization tools is their single focus. Traffic signal 

optimization tools are typically used to provide supplementary analysis when analyzing the 

overall mobility impacts of a work zone. 

Table 4 lists some general pros and cons of using traffic signal optimization tools for work zone 

traffic analysis.

Travel Demand Modeling in Work Zone Traffic Analysis – TRANPLAN

Cleveland Innerbelt Project

The Cleveland Innerbelt Project was a rehabilitation and reconstruction project of the Innerbelt Freeway system, 

which includes Interstates 71 and 90. A maintenance of traffic alternatives analysis was conducted in order to 

evaluate traffic detour routes and a full closure work zone configuration during the rehabilitation/reconstruction 

effort. Several alternatives were analyzed such as reduction in the capacity of travel lanes along various 

segments of the Innerbelt, as well as the full closure of the Central Viaduct Bridge.

TRANPLAN, the regional travel demand model was used to simulate and measure the impacts of the traffic 

assignments and diversion that would take place as a result of the various work zone alternatives considered. 

TRANPLAN was used for a variety of reasons including its ease of use, accessibility (the model was already 

developed) and ability to generate the required performance measures needed for the analysis.

Portion of Cleveland Innerbelt TRANPLAN Network
25 

Table 4. Pros and Cons of Traffic Signal Optimization Tools for WZTA 

Pros Cons

• Useful for temporary and permanent traffic 

signal design

• Relatively quick analysis

• Widely used and trusted

• Capable of signal optimization

• Requires detailed traffic signal design expertise

• Not well suited for freeway operations

• Not well suited to analyze oversaturated conditions

Traffic Signal Optimization Tools in Work Zone Traffic Analysis – Synchro/SimTraffic

Eastern Avenue Bridge Reconstruction over Kenilworth Avenue(11)

Synchro/SimTraffic was used to evaluate various detour options for the reconstruction of the Eastern Avenue 

Bridge over Kenilworth Avenue, N.E. in Washington, D.C. The bridge needed to be rebuilt in order to provide 

additional clearance height for trucks and to provide a safer travel route for all vehicles.

Initially, the Maryland State Highway Administration’s Lane Closure Analysis Program (LCAP) was used to 

evaluate the queues of the lane closures along Kenilworth Avenue. However, the tool could not capture the 

impacts of diversions/detours to other routes. Therefore, Synchro/SimTraffic was used in order to incorporate 

the potential for diversion when assessing the mobility impacts of the work zone. The preferred alternative in 

this project was to detour one of the three Kenilworth Avenue mainline lanes in each direction onto the service 

road and keep two shifted lanes in each direction. This alternative required closing the bridge. The bridge 

closures required detours via the “local” lanes of the service roads via Nannie Helen Burroughs Avenue and via 

the U.S. 50 interchange. A U-turn lane and special signal timing schemes were evaluated for the Nannie Helen 

Burroughs Avenue interchange using Synchro and SimTraffic. Using the results from the analysis, each 

alternative was compared economically for construction and user costs.

Diversion Routes on Kenilworth Avenue

(Source: Bhajandas, Mallela, and Littleton, 2011.)
26 

A list of traffic signal optimization tools can be found in the Traffic Analysis Toolbox Volume I: 

Traffic Analysis Tools Primer.(8)

Macroscopic Simulation Models

Macroscopic simulation models are based on the deterministic relationships of the flow, speed, 

and density of the traffic stream. The simulation in a macroscopic model takes place on a 

section-by-section basis, meaning they treat traffic flows as an aggregated quantity; they do not 

model the movement of individual vehicles on a network. 

Similar to travel demand models, macroscopic models have the ability to model a large 

geographic area. This is particularly useful when the work zone impacts may affect a larger 

corridor or region, such as in the case of a full closure. They also can be set up and run fairly 

quickly due to the fact that they simulate aggregate flows. The primary limitation of 

macroscopic models is their simple representation of traffic movement, which limits the fidelity 

of the results. 

Table 5 lists some general pros and cons of using macroscopic simulation models for work zone 

traffic analysis.

Table 5. Pros and Cons of Macroscopic Simulation Models for WZTA 

Pros Cons

• Able to model large geographic areas

• Short simulation runtime

• Easier to calibrate than mesoscopic and 

microscopic models

• Medium technical skill set

• Simple representation of traffic movement

• Limited network complexity

• Traffic diversion not modeled explicitly

• Unable to accurately evaluate operational strategies 

like ITS
27 

A list of macroscopic simulation models can be found in the Traffic Analysis Toolbox Volume I: 

Traffic Analysis Tools Primer.

(8)

Mesoscopic Simulation Models

Mesoscopic simulation models combine the properties of both microscopic and macroscopic 

simulation models. As in microscopic models, the mesoscopic models’ unit of traffic flow is 

the individual vehicle. Their movement, however, follows the approach of the macroscopic 

models and is typically governed by the average speed on the travel link. Mesoscopic model 

Macroscopic Simulation in Work Zone Traffic Analysis – NetZone

SR 41 Fresno Corridor Construction Work Zone Traffic Impact Study(12)

NetZone was used on a case study along SR 41 in Fresno, California. This project involved weekend, daytime 

construction where two of the three lanes along the highway and one on-ramp upstream of the work zone would 

be closed to traffic. The work zone length was about four miles and the construction duration would last from 

3:35 to 4:35 p.m. when construction efforts were active. The impacts of three scenarios were evaluated. The 

first scenario was the precondition or base case scenario. The second assessed the mobility impacts of the work 

zone if there were no traffic management plans. The third scenario assessed the impacts when pre-trip 

information and a media campaign were implemented as part of the work zone.

NetZone was used to evaluate measures such as travel time, delay, queues, and traffic diversion. The advantages 

of NetZone includes its user-friendliness, versatility to account for various factors and strategies (including 

demand changes, route diversions, ITS strategies, and traveler information), and detailed statistics. Because it is 

a macroscopic simulation tool, it requires less computation time and resources than more complex tools such as 

microsimulation, while still providing a dynamic modeling option to capture peak spreading and queuing within 

the network.

SR 41 Network

(Source: Zhang, 2007.)
28 

travel simulation takes place on an aggregate level and does not consider dynamic speed/volume 

relationships. As such, mesoscopic models provide less fidelity than the microsimulation tools, 

but are superior to the typical planning analysis techniques. The primary strength of mesoscopic 

simulation models, when analyzing work zones, includes the ability to model both large 

geographic areas and corridors. In addition, they are capable of dynamic traffic assignment 

(DTA) and can readily be used to predict route diversions, temporal demand shifts, and even 

modal shifts in some cases.

Mesoscopic simulation models do have a number of weaknesses. One is their limited ability to 

model detailed operational strategies, such as complex signal control. Thus, if a work zone 

includes a number of signalized intersections, a different type of model (such as a microscopic 

simulation model or traffic signal optimization tool) may be a better choice. Another drawback 

is the overall model complexity and data requirements necessary for accurate results. 

Mesoscopic models are an order of magnitude more complex than the most sophisticated 

sketch-planning tools; require similar amounts of data (albeit at a more granular level) as 

regional planning models; and similar resources (time, money, and knowledge) as travel demand 

models and microscopic simulation models. 

Table 6 lists some general pros and cons of using mesoscopic simulation analysis for work zone 

traffic analysis.

Table 6. Pros and Cons of Mesoscopic Simulation Models for WZTA 

Pros Cons

• Capable of measuring corridor or areawide impacts

• Capable of dynamic routing

• Detailed temporal analysis

• Captures nonrecurring and operational strategies

• Less intensive than micro, and more refined 

than macro

• Resource intensive (data, cost, expertise, 

analysis time)

• Typically link, not lane-based impacts

• Not all are capable of reflecting detailed traffic 

signal parameters

• High-technical skill set
29 

A list of mesoscopic simulation models can be found in the Traffic Analysis Toolbox Volume I: 

Traffic Analysis Tools Primer.

(8)

Mesoscopic Simulation in Work Zone Traffic Analysis – DynusT

Dynamic Traffic Assignment Application: I-10 Corridor Study, Tucson Arizona(13)

The I-10 Corridor Project provides freeway and interchange improvement along a five-mile segment of I-10. 

Improvements associated with the project include road widening and reconstruction of four interchanges along 

I-10, converting them from overpasses to underpasses. During the planning stages of the project, DTA modeling 

was used in order to develop a design concept and preliminary plans for the project. Using mesoscopic 

simulation software, DynusT, DTA modeling was performed for three phases of the project development 

process – capacity analysis, construction sequencing, and evaluation of work zone traffic control. It will also be 

used to evaluate work zone strategies during the final design in 2012.

The DynusT model was created using a regional TransCAD travel demand model. Signal data and intersection 

geometry were imported into the DynusT model from a regional Synchro model. These two steps were 

automated with conversion tools. Manual fine-tuning was also conducted to correct signal phasing/timing, 

geometry, posted speed limit, etc. as necessary. The measures of effectiveness (MOE) used in ranking 

alternatives included travel time, volume-to-capacity (v/c) ratio, and average speed.

DynusT was chosen for the following reasons:

• A preliminary model was already available from local agency;

• DTA modeling in DynusT allows for greater accuracy, more realistic results, and capabilities for 

analysis at the corridor and regional level; and

• Capabilities for evaluating various work zone alternatives.

DynusT Network of I-10 Project

(Source: Zou, Nguyen, and Schoen, 2010.)
30 

Microscopic Simulation Models

Microscopic models simulate the movement of individual vehicles based on car-following and 

lane-changing theories. Typically, vehicles enter a transportation network using a statistical 

distribution of arrivals (a stochastic process) and are tracked through the network over small time 

intervals (e.g., one second or a fraction of a second). Typically, upon entry, each vehicle is 

assigned a destination, a vehicle type, and a driver type. Computer time and storage 

requirements for microscopic models are large, usually limiting the network size and the number 

of simulation runs that can be completed. 

Microscopic simulation models were developed to represent transportation systems 

accurately at the individual vehicle level. They simulate the movement of individual vehicles 

based on car-following and lane-changing theories and other parameters. Microscopic 

simulation models update the positions and intentions of individual vehicles every second (or 

fraction of a second) as they move through a network. To account for the diversity of vehicles 

and driving styles that are encountered in real-world traffic, each vehicle is assigned a set of 

characteristics that influence the way it responds to the presence of other vehicles and to traffic 

control devices. Many transportation agencies currently use microscopic models, in conjunction 

with travel demand models, to better understand the impact of roadway geometry modifications 

on LOS and carrying capacity.

Microscopic simulation models are effective in evaluating a wide range of scenarios, including 

heavily congested conditions, complex geometric configurations, and system-level impacts of 

proposed transportation improvements that are beyond the limitations of other model types. 

While practical considerations may limit the geographic coverage of a microscopic simulation

model, these models are useful in analyzing key bottlenecks on roadway segments and corridors, 

where the movement of each individual vehicle needs to be represented to better understand the 

impact on roadway conditions. 

The primary limitation of microscopic simulation models is the substantial amount of roadway 

geometry, traffic control, and traffic pattern data they require. Specialized training for staff is 

needed and the model development time is often lengthy, particularly when performing 

calibration of oversaturated conditions. In addition, microsimulation models do not directly 

calculate LOS and delay consistent with the HCM control delay definition, which can be 

difficult for decision-makers who have relied on this information in the past. 

Table 7 lists some general pros and cons of using microscopic simulation analysis for work zone 

traffic analysis.
31 

Table 7. Pros and Cons of Microscopic Simulation Models for WZTA 

Pros Cons

• Detailed design level

• Complex geometric configurations

• Operational impacts

• Static or dynamic

• Able to model oversaturated conditions

• Able to model key bottlenecks

• Good visualization capability (for both technical 

and lay persons)

• Wide range of performance measures

• Local and systemwide impacts

• Resource intensive (data, cost, expertise, 

analysis time)

• Restricted to corridor or smaller study areas

• Unfamiliar to decision-makers (produces different 

performance measures different from traditional 

HCM-based tools)

• Requires “tricks” to mimic work zone conditions

• High-technical skill set

Microscopic Simulation in Work Zone Traffic Analysis – CORSIM

I-80 Reconstruction Project, Iowa County, Iowa(14)

The Interstate 80 project in Iowa County, Iowa included a six-mile pavement reconstruction of four traffic lanes 

along I-80. The construction lasted from May 31, 1997 until September 13, 1997, where the Interstate was 

modified into a two-lane, two-way operation configuration. Using CORSIM, the impacts of four work zone 

alternatives were analyzed:

• Do-nothing alternative;

• Non-stop work alternative (chosen alternative);

• Four traffic lanes throughout the work zone; and

• Diversion route alternative.

Microscopic simulation was used to determine the amount of motorist delay associated with each alternative. 

Microscopic simulation Traffic Software Integrated System (TSIS-CORSIM) was chosen for the analysis 

because of its ability to simulate a work environment and provide the measures of effectiveness needed to 

effectively compare alternative traffic management plans. It also provided data required for benefit/cost 

analyses that would be difficult to obtain from other sources. Monetary values of delays were used to evaluate 

and compare alternatives.

I-80 Work Zone Location and Diversion Routes

(Source: Schrock and Maze, 2000.)
32 

A list of microscopic simulation models can be found in the Traffic Analysis Toolbox Volume I: 

Traffic Analysis Tools Primer.

(8)
33 

3.2 KEY FACTORS FOR SELECTING THE ANALYSIS TOOL(S)

Work zone impacts analysis may involve a high-level, qualitative review for some projects; or a 

detailed, quantitative analysis using modeling and/or simulation tools for other projects. Tool 

selection is critical in ensuring the most appropriate analysis results for decision-makers to 

make an informed judgment. The key to successful work zone analysis does not depend only on 

selecting the suitable transportation modeling approach, but also on successful integration of data 

and tools to provide a meaningful assessment of work zone impacts relevant to one or more key 

project decisions. One of the first and most critical steps in selecting a work zone traffic analysis 

tool is to identify the objectives of the analysis and the desired outcome(s). In order to achieve 

those objectives and make an informed decision, it is critical for a work zone analyst to consider 

a number of key factors before selecting a traffic analysis tool. As described in both Traffic 

Analysis Toolbox Volume IX: Work Zone Modeling and Simulation – A Guide for Analysts(2) and 

Traffic Analysis Toolbox Volume II: Decision Support Methodology for Selecting Traffic 

Analysis Tools,(7) the key factors in determining the appropriate tool can be broadly categorized 

into seven major areas: 1) project stages; 2) facility types; 3) tool features; 4) agency resources;

5) work zone characteristics; 6) transportation management plan; and 7) performance measures.

Within each of the key factors are important questions that the analyst should consider when 

identifying the appropriate tool for the project. A scoping work sheet such as the one featured 

in Traffic Analysis Toolbox Volume IX(2) and Figure 6, organizes these key questions and 

considerations by each key factor.

A similar work sheet can be developed with the recommended key factors aforementioned to use 

as a tool for documenting the various components of a work zone project that needs to be 

analyzed using a transportation analysis tool. These work sheets are intended to be used as a 

supplemental guide for tool selection. They should be used in conjunction with other important 

considerations that may be specific to the agency or the project when choosing a specific 

modeling approach(es)/tool(s). 

This chapter discusses the strategic framework to identify an appropriate transportation modeling 

approach and analysis tool(s). The chapter provides step-by-step guidance to assist the analyst in 

determining the suitable tool(s) to perform work zone analysis. A hypothetical case study also is 

provided in order to demonstrate the application of each step. Figure 7 illustrates the steps 

involved to assist the analyst identify the most appropriate modeling approaches. In total, seven 

criteria are necessary to help identify the analytical tools that are most appropriate for a 

particular project. Depending on the analytical context and the project’s goals and objectives, 

the relevance of each criterion may differ. The criteria include:

1. Project Stages – Ability to perform analysis under the specific project stage, including 

planning, preliminary engineering/design (PE/Design), and construction stages. 

2. Facility Types – Capability of modeling various facility types, such as freeways, ramps, 

arterials, intersections, high-occupancy vehicle (HOV) lanes, toll plazas, etc.

3. Tool Features – Ability to provide features such as mode split estimation and animation. 

Other features include ease of use and whether the tool is popular and well trusted. 
34 

4. Agency Resources – Ability to meet agency resources that are required for analysis, 

including technical skills, tool capital cost, schedule/time, hardware, and data requirements. 

5. Work Zone Characteristics – Ability to analyze the appropriate work zone type, work zone 

network configuration, work zone size, and work zone analysis area (study area).

6. Transportation Management Plan (TMP) – Ability to analyze various traffic management 

strategies and applications, such as temporary traffic control, motorist information, corridor 

management, and incident management.

7. Performance Measures – Ability to produce and output performance measures, such as 

travel time, delay, queue length, speed, volume, level of service (LOS), vehicle hours of 

travel (VHT), emissions, and fuel consumption. 

The tool relevance to each of the seven criteria presented in this section is for the general tool type, not 

individual tools. Certain tools within a tool type may have advanced features or capabilities that majority of the 

others within the same tool type do not have. The analyst may revise the tool relevance when justified.
35 

Figure 6. Work Zone Analysis Scoping Work Sheet 

(Source: Hardy and Wunderlich, 2009.)
36 

Work Zone Scoping 

Worksheet

Project Stages

Score Work 

Zone Factors

Identify Strategic 

Methodology Challenge Factors 

and Resources

Select Modeling 

Approach and 

Justify

Work Zone 

Characteristics

Transportation 

Management Plan 

(TMP)

Agency 

Resources

Tool Features

Performance 

Measures

If Insufficient

If 

Sufficient

Facility Types

Figure 7. Framework for Identifying Appropriate Modeling Approach for Work Zones
37 

Project Stages

One of the initial steps in selecting a transportation analysis tool and modeling approach will be 

to identify the key decisions that need to be supported during the analysis. These key decisions 

should include scheduling decisions (time of day, time of year, duration, etc.); application 

decisions; and transportation management plan decisions. Additionally, the analyst also should 

consider how these key decisions would differ based upon the project stage (planning, design, or 

construction) of the project or decision-making process. The three stages of the decision-making 

process are shown in Figure 8. Further information about these three stages in the decisionmaking process is provided in the FHWA’s Traffic Analysis Toolbox Volume VIII: Work Zone 

Modeling and Simulation – A Guide for Decision-Makers.

(1) The decision-making process 

evolves over time, and the decisions made in the initial stage will have a direct impact on 

forthcoming stages. For example, while making decisions at the planning stage, careful 

considerations should be made taking into account the preliminary engineering/design 

(PE/Design) and construction stages. Because these may change depending on where the project 

is in the process, certain tool classes may be better suited for certain key decisions involved in a 

particular stage than others. Table 8 presents the relevance of each tool class to the different 

project stages.

Figure 8. Work Zone Decision-Making Process

(Source: Hardy and Wunderlich, 2008.) 
38 

Table 8. Project Stages

Factors – 

PROJECT STAGES

Transportation Modeling Approach

SketchPlanning 

and HCM

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Planning       

PE/Design       

Operations/Construction       

Notes:

 Specific context is generally addressed by tools in this category.

 Some of tools in this category address the specific context but some do not.

 Tools within this category do not generally address the specific context.

Facility Types 

This factor includes the various facility types that will be included in the analysis area. An 

analysis can include a multitude of facility types such as freeways, arterials, ramps, and auxiliary 

lanes, among others. A full list and description of each facility type is provided in Table 9. 

Tool Features 

This factor includes various tool features such as mode split estimation and animation. Certain 

tool classes may offer additional capabilities or features that would allow the agency to optimize 

their modeling process. Some of these tool features that may be considered include: 

• Mode Split – It evaluates whether the tool provides estimation on percentage of travelers 

using each travel mode (SOV, HOV, transit, bicycle, pedestrian, etc.). 

• Ease of Use – It evaluates whether the tool is generally user-friendly. 

• Popular/Well Trusted – It evaluates whether the tool is popular and well regarded by 

current users.

• Animation/Presentation – This factor evaluates whether the tool has animation/

presentation features such as colorful three-dimensional views and simulation video clips. 

Table 10 provides a summary of these tool features for each specific modeling approach. 
39 

Table 9. Facility Types 

Factors – 

FACILITY 

TYPES Summary

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Isolated 

Intersection

Single crossing point between two or more 

roadway facilities.       

Roundabout Unsignalized intersection with a circulatory 

roadway with entering vehicles yielding to traffic.       

Arterial Signalized street that may serve through traffic 

and/or provide access to abutting properties.       

Highway High-speed roadway connecting major areas or 

arterials, with little to no traffic signal operations.       

Freeway

Multilane, divided highway with a minimum of 

two lanes in each direction, no traffic signal 

interruption.

      

HOV Lane

Exclusive lane for vehicles with a defined 

minimum number of occupants (more than one), 

including buses, taxis, or carpools.

      

Ramp Segment of roadway connecting two roadway 

facilities.       

Auxiliary Lane Additional lane on a freeway connecting an 

on-ramp and an off-ramp.       

Truck Lane Designated lane for commercial vehicles, but not 

for public transit vehicles.       

Bus Lane Designated lane for buses during specified periods.       

Toll Plaza Facility where a payment transaction for certain 

use of the roadway is required.       

Notes:

 Specific context is generally addressed by tools in this category.

 Some of tools in this category address the specific context but some do not.

 Tools within this category do not generally address the specific context.
40 

Table 10. Tool Features 

Factors – 

TOOL FEATURES Summary

Transportation Modeling Approach

SketchPlanning and

HCM 

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Mode Split

This factor evaluates whether 

the tool provides percentage of 

travelers using each travel mode 

(SOV, HOV, transit, bicycle, 

pedestrian, etc.).

     

Easy to Use

This factor evaluates whether 

the tool is generally userfriendly.

     

Popular/Well Trusted

This factor evaluates whether 

the tool is popular and well 

regarded by current users.

     

Animation/Presentation

This factor describes whether 

the tool provides capabilities for 

animation and/or other 

presentation features. 

      

Notes:

 Specific context is generally addressed by tools in this category.

 Some of tools in this category address the specific context but some do not.

 Tools within this category do not generally address the specific context.
41 

Agency Resources

The extent to which the impacts of a work zone can be analyzed is strongly dependent upon 

availability of agency resources. It is critical for an analyst to consider the following five factors 

while determining the suitable analysis approach.

1. Technical Skill Requirements – This factor describes the amount of technical knowledge 

required in order to use the tool. For instance, this factor addresses how much training may 

be involved in order for agency staff to utilize the tool effectively for the work zone traffic 

analysis. On Table 11 tools that do not require extensive technical skill requirements or 

training are indicated by a . Tools requiring a moderate amount of training are noted by 

 and those requiring an extensive amount of training or technical skill set is indicated by . 

2. Tool Capital Cost – This factor evaluates the average capital cost to acquire the tool. 

Tools with an average cost less than $1,000 are considered inexpensive. Those tools within 

the $1,000-$5,000 range can be considered as mid-range and those with average costs 

greater than $5,000 are considered to be expensive. On Table 11 tools considered 

inexpensive are depicted by . Tools with average costs falling in the medium range are 

rated  and those classified as expensive are indicated by . 

3. Time/Schedule Requirements for Analysis – The time or schedule requirements 

associated with a tool can include how much coding and/or data inputs are required in order 

to use the tool. It also can include how much computer run time is needed in order to 

obtain the necessary model output or performance measures using a particular tool. On 

Table 11 tools that do not have extensive time requirements for model network preparation 

and/or computer run time are depicted by . Tools with mid-range time requirements are 

rated  and those requiring a significant amount of time for model network coding and 

preparation as well as computer run time for post-processing outputs are indicated by . 

4. Hardware Requirements – The amount of computer power or processing capabilities 

required in order to run the analysis can influence which tool is best suited for the project. 

On Table 11 tools that do not have extensive hardware (computer power and memory 

storage) requirements for the analysis are depicted by . Tools with mid-range 

requirements are rated  and those requiring a significant amount of resources are 

depicted by . 

5. Data Requirements – The amount of input data required in order to perform the analysis 

also can influence what tool is suitable for the project. Input data typically fall under two 

categories: roadway characteristics and travel demand characteristics. Roadway 

characteristics data provides information regarding the physical design and operational 

characteristics of the roadway. Data inputs in this category can include intersection, lane, 

and road geometric characteristics, traffic control locations, and meter and traffic signal 

timing plans. Travel demand characteristics describes the vehicle and traffic demand of the 

system being analyzed and can include data inputs such as Origin-Destination trip tables, 

trip classifications, traffic volume counts and forecasts. On Table 11 tools that do not have 

extensive data requirements for the analysis are depicted by . Tools with mid-range 

requirements are rated  and those requiring a significant amount of data inputs and 

resources are depicted by . 
42 

Table 11. Agency Resources Required for Analysis

Factors –

AGENCY 

RESOURCES 

REQUIRED FOR 

ANALYSIS Summary

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Technical Skill 

Requirements

Tools without extensive technical skill or 

training requirements are shown with ; 

moderate amount of training is marked 

and extensive technical skill/training 

requirement is indicated by .

     

Tool Capital Cost

Inexpensive tools are marked by . Tools 

with average costs falling in the medium 

range are rated and those classified as 

expensive are indicated by . 

     

Schedule/Time 

Requirements

Tools that do not require much time for 

model network preparation and/or 

computer run time are depicted by . 

Tools with mid-range time requirements 

are rated and those requiring significant 

time requirements are marked with .

      

Hardware 

Requirements

Tools that do not have extensive hardware 

(computer power and memory storage) 

requirements for the analysis are depicted 

by . Tools with mid-range requirements 

are rated and those requiring a 

significant amount of resources are 

depicted by .

      

Data Requirements

Tools that do not have extensive data 

requirements for the analysis are depicted 

by . Tools with mid-range requirements 

are rated and those requiring a 

significant amount of data inputs and 

resources are depicted by . 

      

Notes:

 Tools that do not have extensive requirements for the analysis.

 Tools that have mid-range requirements for the analysis.

 Tools that have extensive requirements for the analysis.
43 

Work Zone Characteristics

This subsection discusses how the physical attributes of a work zone impacts the type of tool that 

should be selected for the analysis. The work zone characteristics include the type, the network 

configuration, size, and the analysis area dimension of the work zone analysis project included in 

the analysis area, as shown in Table 12. Detailed definitions of these attributes are provided in 

Traffic Analysis Toolbox Volume IX: Work Zone Modeling and Simulation – A Guide for 

Analysts(2) and Traffic Analysis Toolbox Volume II.(7) Work zone characteristics used in the tool 

selection methodology include: 

• Work Zone Type – As defined in the FHWA’s Work Zone Self Assessment Guide, Work 

Zone Types I, II, III and IV are indicative of the level of impact a work zone will have on 

travelers. Agencies may choose their own categories based on level of impact, but 

typically Type I is more complex and attracts greater public interest than Type II and so 

on. 

• Network Configuration – The network configuration determines the overall complexity 

of the work zone that will be evaluated. It is composed of the following three general 

categories:

• Isolated – Single work zone with limited interaction of surrounding infrastructure; 

• Pipe – Roadway segment with multiple, interacting work zones; and

• Grid – Connected, interdependent network structure with multiple access points and 

alternate routes.

• Work Zone Size – This factor includes the actual size of the work zone itself and the 

immediate area that will be impacted. Work zones are divided into three sizes: 

• Small – A work zone implemented on a short segment of an individual roadway or a 

single intersection;

• Medium – One or more work zones on longer stretches of a single facility and/or 

portions of adjacent facilities; and

• Large – Interacting work zones implemented on significant elements of a larger 

roadway network. 

• Analysis Area Dimension – Analysis area dimension determines the entire area 

impacted by the work zone. It is divided into three categories:

• Site – Restricted to the immediate area surrounding the work zone; 

• Local – Includes surrounding area beyond the work zone to account for detour routes 

or other localized impacts; and

• Metropolitan – Includes multiple jurisdictions (counties, cities, etc.) or facilities.
44 

Table 12. Work Zone Characteristics

Factors – 

WORK ZONE 

CHARACTERISTICS Summary

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic Signal 

Optimization

Macroscopic 

Simulation

Mesoscopi

c 

Simulation

Microscopic 

Simulation

Work Zone Type

Type I 

Affects a large number of traveler at the regional 

scale, with high public interest, significant user 

cost impacts and a long duration.

      

Type II Similar to Type I but moderate impact.       

Type III Similar to Type I but low impact.       

Type IV Short-duration work zone with low visibility and 

public interest.       

Work Zone Network Configuration

Isolated Single work zone with limited interaction of 

surrounding infrastructure.       

Pipe Roadway segment with multiple, interacting work 

zones.

     

Grid Connected, interdependent network structure with 

multiple access points and alternate routes.      

Work Zone Size

Small A work zone implemented on a short segment of 

an individual roadway or a single intersection.       

Medium One or more work zones on longer stretches of a 

single facility and/or portions of adjacent facilities.       

Large Interacting work zones implemented on 

significant elements of a larger roadway network.       

Work Zone Analysis Area Dimension

Site Analysis area is restricted to the immediate area 

surrounding the work zone.       

Local

Analysis area includes surrounding area beyond 

the work zone to account for detour routes or 

other localized impacts.

      

Metropolitan Analysis area includes multiple jurisdictions 

(counties, cities, etc.) or facilities.       

(Source: Hardy and Wunderlich, 2009.) 

Notes:

 Specific context is generally addressed by tools in this category.

 Some of tools in this category address the specific context but some do not.

 Tools within this category do not generally address the specific context.
45 

Transportation Management Plan (TMP) 

A TMP describes the management strategies that will be used to manage the impacts of a work 

zone. The work zone analysis tool must be capable of modeling the impacts of the type of 

strategy to be evaluated. As described in the FHWA’s Developing and Implementing 

Transportation Management Plans for Work Zones, TMP strategies are broadly grouped into the 

following categories:

• Temporary Traffic Control (TTC) – These strategies include conceptual decisions on 

possible construction approaches, traffic control and management approaches, and time 

of construction. These include the following: 

• Control strategies;

• Traffic control devices; and 

• Project innovation. 

• Public Information – For projects that will have a significant impact on the traveling 

public, the analyst should consider, including motorist information strategies. 

Information dissemination to the public can be delivered a number of ways, such as 

general public awareness campaigns and motorist information strategies (e.g., pre-trip 

and en route traveler information).

• Transportation Operations – Operational strategies are wide ranging and can be 

classified into four major categories:

• Demand management strategies;

• Corridor/network management strategies; and

• Traffic incident management and enforcement strategies.

Table 13 presents the relevance of each tool class to the different TMP strategies.
46 

Table 13. TMP Strategies

Factors – 

TMP STRATEGIES Summary

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Temporary Traffic 

Control 

Traffic control approaches used to 

accommodate road users within the 

work zone and adjoining corridor in an 

efficient and safe manner (e.g., 

construction phasing/staging, lane 

closures, ramp closures/relocation, 

temporary traffic signals).

      

Motorist Information 

Provision of current/real-time 

information to road users regarding the 

project work zone.

      

Demand Management 

Strategies intended to reduce the 

volume of traffic traveling through the 

work zone. Examples include 

diverting motorists to alternate modes 

of travel, shifting motorists to alternate 

routes, and encouraging off-peak travel 

trips.

     

Corridor/Network 

Management (traffic 

operations)

Strategies used to optimize traffic flow 

through work zone using traffic 

operations strategies and technologies.

      

Traffic/Incident 

Management and 

Enforcement Strategies

Strategies to manage work zone traffic 

operations.       

Notes:

 Specific context is generally addressed by tools in this category.

 Some of tools in this category address the specific context but some do not.

 Tools within this category do not generally address the specific context.
47 

Performance Measures

Once the agency determines the work zone traffic analysis objectives at the onset of the project, 

a list of the most desired and appropriate performance measures should be identified to allow for 

informed decision-making. Both the type of measure and the precision of the results (rough 

estimated or a detailed assessment) are important. A comprehensive list of performance 

measures are compiled in the FHWA’s Work Zone Impacts Assessment: An Approach to Assess 

and Manage Work Zone Safety and Mobility Impacts of Road Projects and A Primer on Work 

Zone Safety and Mobility Performance Measurement.

(5,6) Some of the most common measures 

include: 

• Travel time;

• Delay;

• Queue length;

• Speed;

• Volume; 

• Density;

• LOS; and

• VMT/VHT.

The stage of the project’s life cycle often determines the overall precision of the results. For 

example, as the project moves from Planning to PE/Design to Construction, the level of precision 

needed increases. Table 14 provides a summary of the relevance of each tool class to various 

performance measures.
48 

Table 14. Performance Measures

Factors – 

PERFORMANCE

MEASURES Summary

Transportation Modeling Approach

SketchPlanning 

and HCM

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Travel Time Average time spent by vehicles traversing a facility, 

typically in minutes per vehicle.       

Delay Additional travel time experienced by travelers at 

speeds less than the free-flow speed.       

Queue Length Length of queued vehicles waiting to be served by the 

system (expressed in distance or number of vehicles).       

Speed Rate of motion (expressed in distance per unit of time).       

Volume Number of persons or vehicles passing a point on a 

roadway during some time interval.       

V/C Ratio Ratio of flow rate to capacity for a transportation 

facility.       

Density

Number of vehicles on a roadway segment averaged 

over space (usually expressed in vehicles per mile or 

vehicles per mile per lane).

      

LOS

Qualitative measure describing operational conditions 

within a traffic stream, based on service measures such 

as speed and travel time, freedom to maneuver, traffic 

interruptions, comfort, and convenience. Ranges from 

LOS A (best) to LOS F (worst). 

      

VMT/PMT

Total distance traveled by all vehicles or persons on a 

transportation facility or network during a specified time 

period (expressed in miles).

      

VHT/PHT

Total travel time spent by all vehicles or persons on a 

transportation facility or network during a specified time 

period (expressed in hours).

      

Emissions Predicted emissions for each pollutant type on a 

transportation facility or network.       

Fuel Consumption Fuel consumption rate associated with the use of a 

transportation facility or network.       

Notes:

 Specific context is generally addressed by tools in this category.

 Some of tools in this category address the specific context but some do not.

 Tools within this category do not generally address the specific context. 
49 

3.3 SELECTING THE ANALYSIS TOOL(S)

The purpose of this section is to provide users guidance regarding how to use the criteria and 

factors described in Section 3.2 to select the appropriate analysis tool types. This section also 

uses a hypothetical example in order to demonstrate the steps of this approach. 

Assign Criteria Relevance Weight

In most cases, the most appropriate tool category or tool depends on the type of project and the 

level of detail required by each project. In this step, the criteria relevance weight is assigned to 

each of the criteria, depending on the type of study. The weight ranges from 0 (not relevant) to 5 

(most relevant). For instance, if the project is a long-range plan, the project stage weight should 

be 5 for “Planning” and 0s for “PE/Design” and “Construction,” as shown in Table 15. 

Table 15. Example Criteria Relevance Weight

Factors – PROJECT STAGES Criteria Relevance

Planning 5

PE/Design 0

Construction 0

Assign Tool Relevance Values 

The tool relevance values are assigned as follows:

a. For every solid circle (), assign a value of 10. 

b. For every null symbol (), assign a value of 5. 

c. For every empty circle (), assign a value of 0. 

Tables 16 through 22 present the tool relevance values for each of the seven criteria presented in 

Tables 8 through 14. 

The tool relevance to each of the seven criteria presented in this section is for the general tool type, not 

individual tools. Certain tools within a tool type may have advanced features or capabilities that majority of the 

others within the same tool type do not have. The analyst may revise the tool relevance values when justified. 

Also, the analyst may adopt a different set of values than the ones provided herein (0-10).
50 

Table 16. Tool Relevance Values – Project Stages

Factors – 

PROJECT STAGES

Transportation Modeling Approach

SketchPlanning 

and HCM

Travel 

Demand 

Models

Traffic Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Planning 10 10 0 5 5 0

PE/Design 5 5 10 10 10 10

Operations/Construction 5 0 10 10 10 10

Table 17. Tool Relevance Values – Facility Types 

Factors – 

FACILITY TYPES

Transportation Modeling Approach

SketchPlanning 

and HCM

Travel 

Demand 

Models

Traffic Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Isolated Intersection 5 5 10 5 10 10

Roundabout 5 0 0 5 5 10

Arterial 5 10 10 10 10 10

Highway 10 10 5 10 10 10

Freeway 10 10 5 10 10 10

HOV Lane 5 10 0 10 10 10

Ramp 5 10 5 10 10 10

Auxiliary Lane 5 0 5 5 10 10

Truck Lane 5 10 5 5 10 10

Bus Lane 0 10 0 5 5 10

Toll Plaza 5 5 0 0 5 10

Table 18. Tool Relevance Values – Tool Features 

Factors – 

TOOL FEATURES

Transportation Modeling Approach

SketchPlanning 

and HCM

Travel 

Demand 

Models

Traffic 

Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Mode Split 5 10 5 5 5 5

Easy to Use 10 0 5 5 0 0

Popular/Well Trusted 10 5 10 0 5 10

Animation/Presentation 0 5 0 5 5 10

Table 19. Tool Relevance Values – Agency Resources Required for Analysis

Factors –

AGENCY 

RESOURCES 

REQUIRED FOR 

ANALYSIS

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic 

Signal

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Technical Skill 

Requirements 10 0 5 5 0 0 

Tool Capital Cost 10 5 5 5 5 0

Schedule/Time 

Requirements 10 5 5 5 0 0 

Hardware Requirements 10 0 10 5 0 0

Data Requirements 10 5 5 5 0 0
51 

Table 20. Tool Relevance Values – Work Zone Characteristics

Factors – 

WORK ZONE 

CHARACTERISTICS

Transportation Modeling Approach

SketchPlanning 

and HCM

Travel 

Demand 

Models

Traffic Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Work Zone Type

Type I 5 10 5 5 10 0

Type II 5 5 5 5 10 5

Type III 10 0 10 0 5 10

Type IV 10 0 10 0 0 10

Work Zone Network Configuration

Isolated 10 0 5 5 5 5

Pipe 10 0 5 5 10 10

Grid 0 5 5 5 10 10

Work Zone Size

Small 10 0 10 0 5 5

Medium 10 5 5 5 10 10

Large 10 10 5 10 10 10

Work Zone Analysis Area Dimension

Site 10 0 10 0 5 5

Local 10 5 5 5 10 10

Metropolitan 5 10 5 10 10 5

Table 21. Tool Relevance Values – TMP Strategies

Factors – 

TMP STRATEGIES

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic Signal 

Optimization

Macroscopi

c Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Temporary Traffic 

Control 10 5 10 5 10 10

Motorist Information 5 0 0 5 10 10

Demand Management 10 10 5 5 10 5

Corridor/Network 

Management (Traffic 

Operations) 

5 5 5 5 10 10

Traffic/Incident 

Management and 

Enforcement Strategies

5 0 0 5 10 10
52 

Table 22. Tool Relevance Values – Performance Measures

Factors – 

PERFORMANCE 

MEASURES

Transportation Modeling Approach

SketchPlanning 

and HCM 

Travel 

Demand 

Models

Traffic Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

Travel Time 5 10 10 10 10 10

Delay 10 10 10 10 10 10

Queue Length 10 0 10 10 10 10

Speed 10 10 10 10 10 10

Volume 10 10 10 10 10 10

V/C Ratio 5 10 5 5 5 5

Density 5 0 10 10 10 10

LOS 5 5 10 5 5 5

VMT/PMT 5 10 5 10 10 10

VHT/PHT 5 10 5 10 10 10

Emissions 5 0 0 0 5 5

Fuel Consumption 5 0 0 5 10 10

Calculate Criteria Score

After assigning the tool relevance values and criteria relevance weights, the next step is to 

calculate the criteria score. The criteria score is calculated by multiplying the criteria relevance 

weight by the tool relevance value. For instance, if the project involves arterials, highways, 

freeways, ramps, and auxiliary lanes, the criteria scores can be calculated accordingly, as shown 

in Table 23. 

Calculate Total Score 

After the criteria score is calculated for each criterion, add up the scores for each tool type. The 

tool types with the highest totals are the most appropriate tools for the work zone project. 

Table 24 shows an example total score calculation for a project. For this project, the sketchplanning and HCM-based models have the highest score and they are the most appropriate tools. 

The work zone analysis scoring work sheet, as shown in Table 25, can be utilized to assist in 

selecting the analysis tools using the methodology described above. 
53 

Table 23. Example Criteria Score Calculation 

Factors –

FACILITY 

TYPES

Criteria 

Relevance

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Isolated 

Intersection 0 5 5 10 5 10 10 0 0 0 0 0 0 

Roundabout 0 5 0 0 5 5 10 0 0 0 0 0 0

Arterial 5 5 10 10 10 10 10 25 50 50 50 50 50

Highway 5 10 10 5 10 10 10 50 50 25 50 50 50

Freeway 5 10 10 5 10 10 10 50 50 25 50 50 50

HOV Lane 0 5 10 0 10 10 10 0 0 0 0 0 0

Ramp 5 5 10 5 10 10 10 25 50 25 50 50 50

Auxiliary Lane 5 5 0 5 5 10 10 25 0 25 25 50 50

Truck Lane 0 5 10 5 5 10 10 0 0 0 0 0 0

Bus Lane 0 0 10 0 5 5 10 0 0 0 0 0 0

Toll Plaza 0 5 5 0 0 5 10 0 0 0 0 0 0

Subtotal 175 200 150 225 250 250

Table 24. Example Total Score Calculation 

Criteria

Sketch-Planning 

and HCM-Based 

Models

Travel Demand 

Models

Traffic Signal 

Optimization

Macroscopic 

Simulation

Mesoscopic 

Simulation

Microscopic 

Simulation

1. Project Stage 50 50 0 25 25 0

2. Facility Types 100 100 50 100 100 100

3. Tool Features 50 25 50 0 25 50

4. Agency Resources 250 75 150 125 25 0

5. Work Zone Characteristics 200 25 125 50 125 150

6. Transportation Management 

Plan (TMP) Strategies 100 75 75 50 100 75

7. Performance Measures 225 200 250 250 250 250

Total 975 550 700 600 650 625
54 

Table 25. Work Zone Analysis Scoring Sheet 

Factors

Criteria 

Relevance

Relevance Value Score

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Project Stage

Planning 10 10 0 5 5 0

PE/Design 5 5 10 10 10 10

Construction 5 0 10 10 10 10

Facility Types

Isolated Intersection 5 5 10 5 10 10

Roundabout 5 0 0 5 5 10

Arterial 5 10 10 10 10 10

Highway 10 10 5 10 10 10

Freeway 10 10 5 10 10 10

HOV Lane 5 10 0 10 10 10

Ramp 5 10 5 10 10 10

Auxiliary Lane 5 0 5 5 10 10

Truck Lane 5 10 5 5 10 10

Bus Lane 0 10 0 5 5 10

Toll Plaza 5 5 0 0 5 10

Tool Features

Mode Split 5 10 5 5 5 5

Easy to Use 10 0 5 5 0 0

Popular/Well Trusted 10 5 10 0 5 10

Animation/Presentation 0 5 0 5 5 10

Agency Resources

Technical Skill Requirements 10 0 5 5 0 0

Tool Capital Cost 10 5 5 5 5 0

Schedule/Time Requirements 10 5 5 5 0 0

Hardware Requirements 10 0 10 5 0 0

Data Requirements 10 5 5 5 0 0

Work Zone Characteristics

Work Zone Type

Type I 5 10 5 5 10 0

Type II 5 5 5 5 10 5

Type III 10 0 10 0 5 10

Type IV 10 0 10 0 0 10
55 

Factors

Criteria 

Relevance

Relevance Value Score

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Work Zone Network Configuration

Isolated 10 0 5 5 5 5

Pipe 10 0 5 5 10 10

Grid 0 5 5 5 10 10

Work Zone Size

Small 10 0 10 0 5 5

Medium 10 5 5 5 10 10

Large 10 10 5 10 10 10

Work Zone Analysis Area

Site 10 0 10 0 5 5

Local 10 5 5 5 10 10

Metropolitan 5 10 5 10 10 5

Transportation Management Plan (TMP) Strategies

Temporary Traffic Control 10 5 10 5 10 10

Motorist Information 5 0 0 5 10 10

Demand Management 10 10 5 5 10 5

Corridor/Network 

Management 5 5 5 5 10 10

Traffic/Incident Management 

and Enforcement Strategies 5 0 0 5 10 10 

Performance Measures

Travel Time 5 10 10 10 10 10

Delay 10 10 10 10 10 10

Queue Length 10 0 10 10 10 10

Speed 10 10 10 10 10 10

Volume 10 10 10 10 10 10

V/C Ratio 5 10 5 5 5 5

Density 5 0 10 10 10 10

LOS 5 5 10 5 5 5

VMT/PMT 5 10 5 10 10 10

VHT/PHT 5 10 5 10 10 10

Emissions 5 0 0 0 5 5

Fuel Consumption 5 0 0 5 10 10

Total Score
56 

Analysis Tool Selection Example: I-123 Corridor Reconstruction Project

The following hypothetical example serves as an example to illustrate the analysis tool selection 

methodology featured in this chapter using the given project background information. The 

information presented in this example is intended for illustrative purposes only. While the 

following example can be used to select or justify the use of a particular analysis tool, there may 

be additional questions that the analyst should consider, such as those shown on the example 

scoping work sheets, when determining the overall analysis approach. 

Project Background 

A six-mile corridor reconstruction project is in the design stage for Interstate 123 (I-123). 

Figure 9 depicts the location of the reconstruction project, with the work zone area highlighted in 

a box (in red color). As shown on the figure, I-123 is an urban freeway located within the central 

business district (CBD) of Coast City. The reconstruction will include freeway connectors 

between Interstate 123 and SR 456. Interstate 123 includes four to six lanes in each direction 

with an AADT of 150,000 vehicles and 12 percent truck traffic. The I-123 project will include 

reconstruction of three interchanges and two auxiliary lanes, pavement rehabilitation, and lane 

widening. The estimated duration to complete the entire project is 10 months. Because the 

project will require lane and ramp closures, parallel facilities, Frontage Road, Shell Avenue, 

Palm Boulevard, and Coastal Highway, have been identified as potential detour routes. The 

alternative routes are shown in blue on Figure 9. 

At this design stage, the agency has identified several work zone alternatives for completing the 

project. Various alternatives considered include alternative construction work schedules (i.e., 

weekend work and night work) and various construction phasing/staging options. The agency 

will, therefore, need an analysis approach that applies a well-trusted tool in order to determine 

which of the alternatives should be implemented. For stakeholder and public meetings, the 

agency prefers that the selected tools have good animation and presentation features. 

The agency has collected plenty of data and has enough resources to meet technical, cost, 

schedule, and hardware requirements of all levels of analysis tools. The work zone project will 

involve temporary traffic controls, motorist information, demand management, corridor 

management, and traffic incident management strategies. The desired performance measures 

from the analysis include travel time, delay, queue length, speed, volume, VMT, and VHT. 
57 

Figure 9. Analysis Tool Selection Example: I-123 Reconstruction Project Coastal Highway Shell Avenue Palm Boulevard Frontage Road First Avenue
58 

Select the Analysis Tool

The detailed steps on how to select the appropriate tool types for this hypothetical example are 

presented below.

Step 1: Project Stages

The analysis is in the design stage. Therefore, a criteria relevance weight of 5 is assigned to 

PE/Design, as shown in Table 26. 

Step 2: Facility Types

This project will include reconstruction of three interchanges and two auxiliary lanes, pavement 

rehabilitation, and lane widening. It also will require lane and ramp closures. Major detour 

routes include Frontage Road, Shell Avenue, Palm Boulevard, and Coastal Highway. Therefore, 

the following facility types are involved in this project: arterial, highway, freeway, ramp, and 

auxiliary lane, as shown in Table 26. 

Step 3: Tool Features

The agency wants an analysis approach that applies a well-trusted tool in order to determine 

which of the alternatives should be implemented. Also, the agency prefers that the selected tools 

have good animation and presentation features for the stakeholder and public meetings. 

Therefore, criteria relevance weights of 5 are assigned to both Popular/Well Trusted and 

Animation/Presentation, as shown in Table 26. 

Step 4: Agency Resources

In order to determine the appropriate analysis methodology and tool that best fits the project, the 

resources that the agency can dedicate to the analysis should be considered. For the I-123 

reconstruction project, the agency has collected plenty of data and has enough resources to meet 

technical, cost, schedule, and hardware requirements of all levels of analysis tools. In other 

words, there is least concern on agency resources. Therefore, criteria relevance weights of 1, for 

instance, are assigned to all five major key factors related to agency resources, as shown in 

Table 26. 

Step 5: Work Zone Characteristics

The analysis area includes a six-mile segment in both directions of I-123. The analysis area also 

includes the diversion routes comprised of Frontage Road, Shell Avenue, Palm Boulevard, and 

Coastal Highway.

• Work Zone Type – The project can be classified as a Type II for the following reasons: 

• The project is located on an urban freeway near the city core and also includes access 

to two major highway facilities; and

• Because the work zone project takes up to 10 months to build, its impacts can be 

classified as moderate to high.
59 

• Work Zone Network Configuration – The six-mile work zone can be classified as a 

Pipe network. 

• Work Zone Size – The project can be classified as Medium size as it is on a longer 

stretch of a single facility with impacts that extend to adjacent facilities.

• Analysis Area – As previously mentioned the analysis area dimension identifies the full 

area impacted by the work zone. Because the impact of this work zone project extends to 

the multiple facilities, this project’s analysis area can be classified as a Local.

Step 6: Transportation Management Plan (TMP) Strategies 

The work zone project will involve temporary traffic controls, motorist information, demand 

management, corridor management, and traffic incident management strategies. Criteria 

relevance weights of 5 are assigned to them, as shown in Table 26. 

Step 7: Performance Measures

The agency wants to collect the following performance measures from the analysis: travel time, 

delay, queue length, speed, volume, VMT, and VHT, as shown in Table 26. 

Step 8: Criteria Score Calculation

After assigning the criteria relevance weights, the next step is to calculate the criteria score. As 

shown in Table 26, the criteria score is calculated by multiplying the criteria relevance weight by 

the tool relevance value.

Step 9: Total Score Calculation and Final Recommendation 

After the criteria score is calculated for each criterion, add up the scores for each tool type. As 

shown in Table 26, the two analysis methods that scored the highest are mesoscopic and 

microscopic simulation tools. For this project, either mesoscopic or microscopic simulation tools 

are appropriate for the analysis. Between them, mesoscopic simulation provides better detour 

analysis, while microscopic simulation provides better animation features (e.g., 3-D vehicle 

animation).
60 

Table 26. Example Work Zone Analysis Scoring Sheet: 

I-123 Corridor Reconstruction Project

Factors

Criteria 

Relevance

Relevance Value Score

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Project Stage

Planning 0 10 10 0 5 5 0 0 0 0 0 0 0

PE/Design 5 5 5 10 10 10 10 25 25 50 50 50 50

Construction 0 5 0 10 10 10 10 0 0 0 0 0 0

Facility Types

Isolated Intersection 0 5 5 10 5 10 10 0 0 0 0 0 0

Roundabout 0 5 0 0 5 5 10 0 0 0 0 0 0

Arterial 5 5 10 10 10 10 10 25 50 50 50 50 50

Highway 5 10 10 5 10 10 10 50 50 25 50 50 50

Freeway 5 10 10 5 10 10 10 50 50 25 50 50 50

HOV Lane 0 5 10 0 10 10 10 0 0 0 0 0 0

Ramp 5 5 10 5 10 10 10 25 50 25 50 50 50

Auxiliary Lane 5 5 0 5 5 10 10 25 0 25 25 50 50

Truck Lane 0 5 10 5 5 10 10 0 0 0 0 0 0

Bus Lane 0 0 10 0 5 5 10 0 0 0 0 0 0

Toll Plaza 0 5 5 0 0 5 10 0 0 0 0 0 0

Tool Features

Mode Split 0 5 10 5 5 5 5 0 0 0 0 0 0

Easy to Use 0 10 0 5 5 0 0 0 0 0 0 0 0

Popular/Well Trusted 5 10 5 10 0 5 10 50 25 50 0 25 50

Animation/Presentation 5 0 5 0 5 5 10 0 25 0 25 25 50

Agency Resources

Technical Skill Requirements 1 10 0 5 5 0 0 10 0 5 5 0 0

Tool Capital Cost 1 10 5 5 5 5 0 10 5 5 5 5 0

Schedule/Time Requirements 1 10 5 5 5 0 0 10 5 5 5 0 0

Hardware Requirements 1 10 0 10 5 0 0 10 0 10 5 0 0

Data Requirements 1 10 5 5 5 0 0 10 5 5 5 0 0

Work Zone Characteristics

Work Zone Type

Type I 0 5 10 5 5 10 0 0 0 0 0 0 0

Type II 5 5 5 5 5 10 5 25 25 25 25 50 25

Type III 0 10 0 10 0 5 10 0 0 0 0 0 0

Type IV 0 10 0 10 0 0 10 0 0 0 0 0 0
61 

Factors

Criteria 

Relevance

Relevance Value Score

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Sketch 

Plan TDM

Signal 

Opt.

Macro 

Sim

Meso 

Sim

Micro 

Sim

Work Zone Network Configuration

Isolated 0 10 0 5 5 5 5 0 0 0 0 0 0

Pipe 5 10 0 5 5 10 10 50 0 25 25 50 50

Grid 0 0 5 5 5 10 10 0 0 0 0 0 0

Work Zone Size

Small 0 10 0 10 0 5 5 0 0 0 0 0 0

Medium 5 10 5 5 5 10 10 50 25 25 25 50 50

Large 0 10 10 5 10 10 10 0 0 0 0 0 0

Work Zone Analysis Area

Site 0 10 0 10 0 5 5 0 0 0 0 0 0

Local 5 10 5 5 5 10 10 50 25 25 25 50 50

Metropolitan 0 5 10 5 10 10 5 0 0 0 0 0 0

Transportation Management Plan (TMP) Strategies

Temporary Traffic Control 5 10 5 10 5 10 10 50 25 50 25 50 50

Motorist Information 5 5 0 0 5 10 10 25 0 0 25 50 50

Demand Management 5 10 10 5 5 10 5 50 50 25 25 50 25

Corridor/Network 

Management 5 5 5 5 5 10 10 25 25 25 25 50 50

Traffic/Incident Management 

and Enforcement Strategies 5 5 0 0 5 10 10 25 0 0 25 50 50

Performance Measures

Travel Time 5 5 10 10 10 10 10 25 50 50 50 50 50

Delay 5 10 10 10 10 10 10 50 50 50 50 50 50

Queue Length 5 10 0 10 10 10 10 50 0 50 50 50 50

Speed 5 10 10 10 10 10 10 50 50 50 50 50 50

Volume 5 10 10 10 10 10 10 50 50 50 50 50 50

V/C Ratio 0 5 10 5 5 5 5 0 0 0 0 0 0

Density 0 5 0 10 10 10 10 0 0 0 0 0 0

LOS 0 5 5 10 5 5 5 0 0 0 0 0 0

VMT/PMT 5 5 10 5 10 10 10 25 50 25 50 50 50

VHT/PHT 5 5 10 5 10 10 10 25 50 25 50 50 50

Emissions 0 5 0 0 0 5 5 0 0 0 0 0 0

Fuel Consumption 0 5 0 0 5 10 10 0 0 0 0 0 0

Total Score 925 765 780 900 1,155 1,150
62 

3.4 CHALLENGES IN SELECTING TRAFFIC ANALYSIS TOOLS FOR WORK 

ZONE APPLICATIONS

In addition to identifying the objectives and available resources/requirements for a work zone 

traffic analysis, there are technical challenges and limitations to consider when determining 

which tool to use. 

Figure 10 depicts a continuum of traffic analysis tools from simple to complex. Simpler tools 

include the categories of HCM and sketch planning, while the more complex tools include 

macro-, meso-, and microscopic simulation software.(2) The spectrum includes seven of the 

many commonly used transportation analysis tools for assessing the impacts of roadway 

construction projects. As the diagram shows, the resources needed increase as the complexity of 

the tool increase.

Figure 10. Work Zone Modeling Spectrum

(Source: Hardy and Wunderlich, 2009.)

Common technical challenges and limitations are presented in Chapter 5 of the Traffic Analysis 

Toolbox Volume II: Decision Support Methodology for Selecting Traffic Analysis Tools, and are 

briefly summarized below.(7)

A significant technical challenge is the availability of data in the formats and quality required to 

effectively apply a particular tool. After weighing the key factors described in Section 3.2, an 

analyst may desire a detailed analysis tool, such as microsimulation. However, this type of 

modeling tool is data intensive and requires robust information about volumes (15-minute 

intervals or less), speeds, queues, etc. for calibration. It may later be discovered that the 

available peak hour traffic data is in one-hour blocks and/or it may be of poor quality. If 

financial and time resources are not available to collect what is needed, a simpler tool should be 

considered. 
63 

The availability and expertise of staff is another technical challenge. Because of lack of 

resources, past experience, or lack of familiarity with other available tools, many agencies use 

one of the tools currently in their possession, even if it is not the most appropriate tool for the 

project. In addition, simpler tools can be effectively applied by experienced technicians; 

whereas the complex tools require analysts with specialized training.

The availability of project funding is an important consideration, as traffic analysis tools 

require a significant capital investment. Costs associated with a work zone traffic analysis can 

include software licenses, training, running analytical scenarios, etc. When faced with funding 

limitations, analysts should focus on the project’s goals and objectives and try to identify the 

point of diminishing returns for the investment. 

The development time and computer run time typically increase with the complexity of the 

tool. The implementation of many traffic analysis tools can be a resource-intensive process, 

especially in the model coding and calibration (front-end) phases for simulation analyses. 

Depending on the computer hardware and the scope of the study (e.g., area size, data 

requirements, duration, analytical time periods, etc.), an analytical model run may range from a 

few seconds to several hours. The most effective approaches to addressing this issue involves 

using the most robust computer equipment available and/or carefully limiting the scope of the 

study to conform to the analytical needs. Careful scheduling and pre-agreed upon acceptance 

criteria are necessary to keep the project focused and on target.

The output measures produced by each tool vary, so the process of matching the project’s 

desired performance measures with the tool’s output is important. In addition, some of the more 

complex projects require the use of more than one tool or analysis method. Keep in mind that 

the definition of a particular performance measure can vary from tool to tool. For example, LOS 

is calculated differently in a traditional HCM demand-based analysis versus an operations-based 

simulation model.

Analytical tools have functional limitations. Some analytical tools are not designed to 

evaluate the specific strategies that users would like to implement. Common limitations for each 

tool category are outlined in Section 3.1. Key considerations for each tool type are provided in 

Chapter 4. 

A considerable amount of research has been conducted in the area of work zone capacity 

determination. Although it is widely accepted that capacity is reduced in work zone situations, 

estimating that reduction is not a straightforward process. Different transportation agencies 

around the country use varying methodologies, and work zone capacity values are often 

estimated differently depending on the roadway type (e.g., arterial versus freeway); duration of 

the work zone; intensity of the work activity (number of workers, types of machinery, and 

proximity of travel lanes to work under way); truck presence; and proximity of ramps, among 

other factors.

Some traffic analysis tools are unable to directly account for this reduced capacity. For example, 

reduced capacity can be directly specified in FREEVAL, an HCM-based deterministic analysis 
64 

tool. In contrast, there is no direct “capacity” input in VISSIM, a microsimulation tool. In order 

to account for the reduced capacity, the analyst needs to adjust a set of parameters that affect 

capacity, such as standstill distance, headway time, and following variation. For some tools, it is 

possible to adjust variables other than capacity itself to, in turn, reflect reduced capacity 

conditions. Those variables may include driver and vehicle characteristics (e.g., headways, etc.). 

The variables are different depending on the tool. This may not be an issue in every analysis, 

particularly if all the alternatives are being compared under the same assumptions. Work zone 

capacity determination is addressed for each tool category in Chapter 4. 

Finally, do not lose sight of the big picture, and be careful not to get lost in the technical details 

of the analysis. Keep focused on the identified project objectives, and let them guide your 

decisions as technical challenges are encountered along the way. 
65 

4.0 KEY CONSIDERATIONS FOR MODELING APPROACH

4.1 INTRODUCTION

After a suitable analysis tool is selected, the next step is to use the tool to determine the impacts 

of a work zone. This chapter describes the key considerations when applying the modeling tools 

for work zone traffic analysis.

Before proceeding to discuss each category of the modeling tools, it is important to have an 

understanding of the following key items, as they play a significant role in work zone analysis 

when using any of the modeling tools. 

• Study Network;

• Work Zone Capacity;

• Capacity versus Discharge Rate;

• Variability of Capacity and Time-of-Day Models; 

• Analysis Period; 

• Change in Traffic Pattern;

• Common Data Collection;

• Model Development Application Process;

• Error Checking; and 

• Level of Effort.

Study Network

In preparation for the data collection required for work zone analysis, it is necessary to determine 

the limits of the network to be studied. The study network should consider both the work zone 

boundaries and the surrounding area where the traffic is affected by the work zone. Specifically, 

the following should be considered: 

• The network boundary should be able to accommodate queues under the worst scenario, 

including the work zone conditions; 

• Include all detour routes, if possible; 

• For freeways and highways, include on- and off-ramps, and other routes to which traffic 

is diverted; and 

• For arterials, consider the following: 

• Include, at a minimum, the adjacent intersections both upstream and downstream of 

the work zone; 

• Include all intersections outside of the work zone that are impacted by queues from 

intersections within the work zone; 

• Consider the limits of the coordinated signal system; and

• Consider including any side streets that are expected to be impacted by work zone 

queues. 
66 

For instance, to obtain an initial estimation of the queue length caused by the work zone at 

signalized intersections, the following equation may be used:(15)

(1)

Where:

L = The approach queue length (in number of vehicles); 

V = The through traffic volume for the approach; 

G =The duration of the green interval for the through movement; 

C = The cycle length;

N = The number of open through lanes; and 

s = The saturation flow rate (in vehicles per hour per lane (vphpl)). Local data at work 

zones shall be used. In the absence of better local data, the analyst may use 1,400 vphpl 

(1,200 to 1,600 vphpl) as the starting point and make modifications accordingly to 

reflect local work zone conditions. 

If the work zone will directly impact more than one intersection, the queue length at the critical 

intersection, and at the first intersection that motorists will encounter while traveling through the 

work zone should be evaluated. The critical intersection is defined as the intersection where one 

or more of its approaches impacted by the work zone have the highest existing volume per 

through lane. Figure 11 illustrates how to determine where the queue length should be 

evaluated.(15)

 

CRITICAL 

INTERSECTION

WORK

ZONE

DIRECTION

OF FLOW

~

EVALUATE 

QUEUE LENGTH

Figure 11. Locations of Queue Length Evaluation 

(Source: Maryland State Highway Administration, 2008.)
67 

Work Zone Capacity 

The Highway Capacity Manual 2010 (HCM 2010) defines capacity as “the maximum sustainable 

hourly flow rate at which persons or vehicles reasonably can be expected to traverse a point or a 

uniform segment of a lane or roadway during a given time period under prevailing roadway, 

environmental, traffic, and control conditions.”(16) Roadway capacity in a work zone is 

reduced due to a variety of factors, including: 

• Work zone speed; 

• Work intensity (work zone type); 

• Percentages of trucks;

• Number of lanes;

• Number of lane closures;

• Lane width;

• Lateral clearance;

• Work zone layout (lane merging, lane shifting, and crossover); 

• Length of closure;

• Presence of ramps;

• Work zone location (urban or rural); 

• Work zone duration (long-term or short-term);

• Work time (daytime or nighttime);

• Work day (weekday or weekend); 

• Type of control devices and their placement;

• Weather conditions (sunny, rainy, or snowy); 

• Pavement conditions (dry, wet, or icy); 

• Pavement grade; and

• Driver composition (commuters or non-commuters, such as tourists). 

Tables 27 and 28 list work zone lane capacity defaults for use (in passenger cars per hour per 

lane or pcphpl) based on previous studies, with Table 27 for short-term work zones and Table 28 

for long-term ones.

(16,17,18) It is strongly recommended, though, that local data at work zones 

are collected and used instead. In the absence of better local data, the analyst may use 

these defaults as the starting point and make modifications accordingly to reflect local 

work zone conditions. Also, some state DOTs and other agencies may have their own 

guidelines on work zone capacity and related defaults. 

Table 27. Table Work Zone Lane Capacity Defaults that Can Be Used in the 

Absence of Better Data: Short-Term Work Zones

Short-Term Work Zone Capacity(17,18) (pcphpl)

Freeway/Highway2

Arterial

Urban Rural

1,200-1,600 600* 400*

(Source: Transportation Research Board, 2011. Weng and Meng, 2011. WSDOT Work Zone Safety and Mobility, 2009.)

* These are average capacity values. The actual values would be dependent on several factors, which include the existing 

number of lanes, number of lanes closed, traffic speed, truck percentage, interchanges/intersections, type of work, type of 

traffic control, and seasonal factors (among others).
68 

Table 28. Work Zone Lane Capacity Defaults that Can Be Used in the 

Absence of Better Data: Long-Term Work Zones

Default Type

Long-Term Work Zone Capacity (pcphpl)(16)

Normal Lanes to Reduced Lanes (Freeways)

2-1 3-2 3-1 4-3 4-2 4-1

Range 1,200-1,800 1,300-1,600 1,200-1,800 1,300-1,600 1,300-1,600 1,200-1,600

Single Value 1,400 1,450 1,450 1,500 1,450 1,350

(Source: Transportation Research Board, 2011. Weng and Meng, 2011. WSDOT Work Zone Safety and Mobility, 2009.)

Note: Capacities through long-term construction zones are highly variable and depend upon many site-specific 

characteristics. It is, therefore, better to base this adjustment on local data and experience. If such data do not exist, 

and cannot be reasonably acquired, the range above may be used to provide a very approximate estimate of 

construction zone capacity.

Capacity versus Discharge Rate

Queue discharge flow rate represents traffic flow that has just passed through a bottleneck 

and is accelerating back to the free-flow speed (FFS). Queue discharge flow is relatively 

stable as long as there is not another nearby bottleneck downstream. The Highway Capacity 

Manual 2010 suggests that the queue discharge flow rate is lower than the maximum flows 

observed before breakdown (capacity). Two methods have been widely used in previous studies 

to define work zone capacity:

• Work Zone Capacity – Method 1 – The observable maximum throughput under 

relatively free-flow conditions before the formation of a traffic queue; and

• Work Zone Capacity – Method 2 – The discharge flow rate under the queued 

conditions. 

Method 1 is particularly relevant to traffic mitigation efforts to avoid queue formation and 

Method 2 is particularly relevant to manage queues, estimate the length of queues, and 

understand the amount of delay when upstream flow rates are consistently above the capacity of 

the work zone. 

Since traffic conditions vary between pre-queuing and queuing conditions at work zone 

locations, a more precise work zone analysis may consider using both of the above work zone 

capacity values: one for pre-queuing conditions, and the other for queuing conditions. However, 

if resources or schedules do not allow for the development of separate models using different 

capacity values for pre-queuing and queuing conditions, select one of the above methods using 

engineering judgment and previous experience. The analyst may concentrate on other more 

important factors that have bigger impacts on model calibration and alternatives analysis.

Variability of Capacity and Time-of-Day Models 

Roadway capacity within a work zone may vary due to the following factors: 

• Driver Characteristics – This may vary from hour to hour, from day to day, and from 

one region to another. This variation is mainly due to the ability of the driver population 
69 

to adapt to the changing conditions. For instance, Al-Kaisy et al. suggested that 

commuter drivers are expected to be more familiar with the route and the work zone 

configuration.(19) Therefore, they can proceed through the work zone with shorter 

headways and higher flows. Similarly, Al-Kaisy and Hall examined the effect of driver 

population at freeway reconstruction zones.(20) Based on a factor of 1.0 for commuter 

traffic, a driver population factor of 0.93 was estimated for the afternoon peak 

period and a driver population factor of 0.84 was estimated for weekends. The 

driver population factor was responsible for more of a capacity reduction on weekends 

compared with the capacity on weekdays. It should be noted that the reduction factor 

varies and it depends on local conditions. 

• Nonrecurrent Events – Nonrecurrent events such as inclement weather and traffic 

accidents further reduce capacity within a work zone. For instance, in the HCM 2010 

(Exhibit 10-15), inclement weather may reduce the capacity of freeways by 1 percent to 

22 percent, depending on the intensity of the weather condition. 

• Work zone Duration – The duration of the work zone has an impact on driver behavior; 

after a period of time, drivers adjust to the changed conditions and capacity and speeds 

may go up. According to Kim, Lovell, and Paracha, the average capacity at long-term 

work zones was noticeably greater than that at short-term ones, as drivers became more 

familiar with the work zones. Based on this study, long-term work zones may have 

capacities as much as 150 pcphpl higher than short-term ones.(21) It should be noted 

that the capacity difference between long-term and short-term work zones varies and it 

depends on local conditions. 

• Random Effect – Even when all prevailing conditions are held constant, capacity was 

found to be a random variable, because of stochastic variations in vehicle mix, lane 

distribution, and driver behavior.(22)

As discussed above, work zone capacity is influenced by many factors. Therefore, sensitivity 

analysis may be deemed necessary when resources allow.

Analysis Period 

Work zone capacity is influenced by many factors; it may be specific to different times of the 

day, different demographic groups (age/experience), and different populations reflecting regional 

or local tendencies. Therefore, if different time-of-day analysis is considered critical and 

resources are available, it is recommended to create different time-of-day models, such as a.m. 

peak, off-peak, p.m. peak, weekend peak, and weekend off-peak, to reflect the variable capacity, 

as well as the variable demand in analysis tools. Also, for longer duration work zones, it is 

recommended that interim analysis be performed to better reflect varying capacities, if 

resources and data are available. 

For longer duration work zones, it is recommended that interim analysis be performed to better reflect varying 

capacities, if resources and data are available.
70 

In addition, different analysis tools may accommodate various time periods; some can analyze 

longer time periods, while others cannot. For instance, some sketch-planning analysis tools can 

analyze longer time periods, such as multiple days, months, or even years, which is typically 

beyond the capability of other analysis tools such as macro-, meso-, and microscopic simulation 

models. 

Changes in Traffic Pattern

Drivers obtain roadway information through various means to select their preferred route(s). 

These means include dynamic message signs (DMS), highway advisory radio (HAR), public 

information and outreach, incident management systems or other traveler information systems, 

etc. Some tools, such as DynusT, have the ability to directly model roadway information system, 

such as DMS. In DynusT, DMS can be set to provide multiple types of messages, such as speed 

advisory, mandatory detour, and congestion warning. DynusT permits users to specify a 

percentage of vehicles that respond to the DMS and make detour decisions. 

According to Horowitz et al., many drivers are responsive to warnings that they might encounter 

excessive delays along their current route.(23)

 However, a much larger percentage of drivers 

would not divert, even though it is to their advantage to do so. This study suggested that a 10 

percent alternative route selection rate during peak periods is achievable when accurate, up-tothe-minute information about delay through a work zone is provided and there is an attractive set 

of alternative routes. This study was conducted on a rural freeway. For drivers using lower 

classification or urban highways with attractive alternative routes, the diversion rate could be 

higher. 

The FHWA conducted a study that examined the use of ITS for work zone traffic management 

with a purpose to highlight “before and after” or “with and without” analyses that quantify the 

mobility and safety benefits.(24) According to this study, significant traffic diversion was 

observed in response to appropriate messages displayed during congested conditions. In Texas, 

an average of 10 percent diversion (range of 1 to 28 percent) was observed, while in the District 

of Columbia (D.C.) an average of 52 percent (range of 3 to 90 percent) lower mainline volume 

(combination of diversion, demand reduction, and congestion) was observed. The study noted 

that in Texas, during major incidents or high construction impact periods combined with high 

demand, the system diverted an average of 10 percent of mainline traffic to alternate routes and 

traffic diversion was as high as 28 percent.

When congestion occurs on the mainline due to a work zone, motorists may choose alternate 

routes to bypass the congested segment. Subsequently, travel time on the alternate routes will 

increase, and the route choice between the mainline and an alternate route may vary over time. 

Dynamic traffic assignment (DTA)-based tools can take this into account. In the case of longterm work zones, generally traffic reaches equilibrium after a few weeks, especially in urban 

areas with commuter traffic. Travelers typically choose the available route having the least 

The analysis period should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period.
71 

travel time between their origin and destination (O-D). Due to congestion effects, the travel time 

of a route between an origin and destination also depends on the choices made by other travelers, 

who are themselves also trying to choose the least travel time route between their own origin and 

destination. When every traveler succeeds in finding such a route, every used route has the 

minimum time or cost between its origin and destination. This condition is known as 

equilibrium. The collection of local data is encouraged in order to create a more robust 

analysis network that captures the effects of changes in traffic pattern.

Besides finding alternate routes, some motorists may change their trip departure times or even 

cancel their trips, especially in the case of long-term work zones with heavy congestion.

Common Data Collection 

Once the size of the study network has been determined, it is necessary to collect data that will 

be needed to model work zone operations. The data collection effort may be considerably 

reduced by obtaining information using aerial imagery, or by downloading the traffic signal 

plans from agency web sites, and/or by obtaining the roadway design plans (if available). If 

needed, these data should be confirmed with a field visit. 

It should be noted that the required data accuracy may vary during different project stages (e.g., 

project planning, preliminary engineering, and final design). It is not appropriate to use 

planning-level data to make engineering design decisions.

Included in Table 29 are the common data that should be collected for all work zone traffic 

analysis, no matter which method or tool is selected. Additional data for specific modeling tools 

will be discussed in each tool category. Depending on the complexity of the study network and 

of the analysis needed, different types of data may be required and may broadly be classified into 

four categories: general, geometric, traffic, and construction.
72 

Table 29. Work Zone Data Collection – Common Data

Category Data

General • Location of work zone

• Weather conditions

• Police presence

• Flagger presence

• Crash/incident information

• Traffic impact mitigation strategies

Geometry • Lane width

• Number of lanes in each direction

• Number of open lanes

• Presence of ramps

• Length of lane closure

• Position of closed lane

• Length of work activity

• Turn bay lengths

• Mid-block intersections

Traffic • Volume of traffic

• Heavy vehicle percentage

• Volume of pedestrians

• Speed limits

• Speed of vehicles in work zone

• Locations of traffic control devices

• Signal detection type and location

• Signal phasing and timing

• Queue lengths

• Presence of congestion (bottlenecks) – location and time of day

• Length and duration of congestion

• Driver behavior

• Travel time

• Delay

• Saturation flow rate

• Growth factor

• Seasonal variation factor

Construction • Type of construction activity

• Number of workers present

• Number and size of construction equipment

• Proximity of work activity to the travel lanes in use

• Traffic control devices used

Origin-Destination Data

Origin-destination (O-D) data is often needed for work zone traffic analysis. This data can be 

used to serve as a basis for trip assignment in the existing conditions models and trip 

redistribution between alternate routes in the analysis models. O-D data may be estimated 

based on a select link analysis using a regional travel demand model, field observations/studies, 

and data from the private sector such as INRIX® and TomTom®. The preferred method of 

obtaining origin-destination data is a field study, such as a post card survey or license plate 

study, as the travel demand model may not have the fidelity to generate an accurate estimate for 

detailed traffic operations analysis. For less complex projects, origin-destination data may not 
73 

need to be collected. Instead, assumptions may be made based on engineering judgment as to 

reasonable traffic distribution and traffic trends. Additionally, the appropriate agency may be 

consulted for assistance with traffic distribution and diversion assumptions. More information 

on performing O-D studies can be found in the Institute of Transportation Engineers’ (ITE) 

Manual of Transportation Engineering Studies. 

Origin-Destination Matrix Estimation (ODME)

The use of an Origin-Destination matrix is fundamental to the application of a travel demand 

model. However, the development of that O-D matrix can be both difficult and costly due to the 

need for extensive traveler survey data. An O-D matrix may be available or easily made 

available from the local MPO or other transportation planning agency. In addition, by using 

traffic counts and other available data, it may be possible to estimate a “reasonable” O-D matrix. 

This process is called ODME.

Many estimation techniques exist for developing a reliable O-D matrix, including Least Squares 

(constrained or not), statistical methods such as Likelihood methods and Kalman filtering, 

Bayesian estimator or combined estimators and Moving Horizon Estimation (MHE) methods. 

Model Development and Application Process

In order to evaluate the impacts of a proposed work zone, it is necessary to build models for both 

existing and work zone conditions. The existing conditions or preconstruction model can be 

built using data collected for existing conditions of the study network. This may include the 

existing lane configurations, existing traffic volumes, and existing signal timings and phasing. 

The model is then calibrated to represent field observed conditions through the adjustment of 

parameters in the analysis tool. This calibrated existing conditions model, often called the 

“no-build” model, provides the baseline for the performance of the roadway facility. The 

existing conditions model can then be modified to represent work zone conditions. 

The basic principles of work zone traffic analysis tools vary from one type to the next. The 

overall procedure of developing and applying models to work zone analysis consists of the 

following major tasks. A flowchart, complementing the overall process, is presented in 

Figure 12. It should be noted that the existing conditions (“no-build”) model development and 

calibration may be skipped if there already is a calibrated model available. The available 

existing conditions model could be generated by a project leading to the work zone analysis or 

from other independent studies. 

1. Identification of Project Scope and Data Collection – The project scope needs to be 

identified to define the study objectives, breadth (geographic and temporal boundaries), 

approach, tools, resources, and time schedule for the study. Once the project scope is 

determined, data should be collected for model development and calibration, such as 

roadway geometry, traffic demands, capacities, traffic controls, travel times, and queues. 

2. Existing Conditions Model Development – Once data are collected, the next step is to 

develop an existing conditions model with the correct geometry, traffic demands, 

capacities, and traffic controls. Coding errors can distort the subsequent model calibration 
74 

process and cause the analyst to adopt incorrect values for the calibration parameters. 

Therefore, error checking, as described in the next section, should be performed to identify 

and correct those model coding errors. If a calibrated existing conditions model already is 

available from other resources, this step and the following existing conditions model 

calibration step can be skipped. 

3. Existing Conditions Model Calibration – Each analysis tool has a set of user-adjustable 

parameters that enable the analyst to calibrate the tool to better match specific local 

conditions. This calibration process involves the selection of a few parameters for 

calibration and the repeated operation of the model to identify the best values for those 

parameters. The calibration process can be time-consuming. It should be well documented 

so that later reviewers of the model can understand the rationale for the various parameter 

changes made during calibration.

4. Work Zone Base Conditions Model Development – The next step is to develop a work 

zone base conditions model by modifying the calibrated existing conditions model to 

represent work zone conditions. These modifications may include geometry, traffic 

demands, capacities, and traffic controls. Roadway geometry and traffic controls can usually 

be collected from construction drawings or field surveys. Traffic demands for the work zone 

base scenario are usually generated from travel demand models, traffic counts, or O-D surveys. 

The preferred method of obtaining traffic demand data is a field study, as the travel demand 

model may not have the fidelity to generate an accurate estimate for detailed traffic operations 

analysis.

5. Work Zone Base Conditions Model Calibration – Work zone model calibration may 

include the calibration of both work zone capacity and performance measures. The work 

zone capacity calibration process is tool-dependent; some tools allow direct changes to 

capacity, while others do not have a “capacity” input and require adjusting a set of 

parameters in the tool that affect capacity. The performance measures collected either from 

the field or based on prior experience and engineering judgment, such as queue lengths, 

travel times, delays, and speeds, also must be calibrated in the model. Typically, work zone 

analysis is performed to gain insight into the potential impact of the proposed work zones. 

Therefore, collecting field data for the work zone to be implemented will likely not be 

possible. Under this case, the analyst should analyze similar work zone types in the project 

area to better understand local driving behaviors and implement those in the model.

6. Alternatives Analysis – Significant efforts have been made on developing and calibrating 

the model. Now it is time to put the model to work by analyzing various project 

alternatives. The alternatives analysis may involve the forecasting of the future demand 

and the testing of various project alternatives against the base case. The analyst needs to 

run the model for each alternative, review the output, compare against any mobility 

requirements, extract relevant statistics, and perform various analyses of the results. Some 

agencies may have their own mobility criteria. For instance, MD SHA uses LOS, control 

delay, and travel time as mobility requirements and the thresholds for arterials.(15)

7. Final Report/Technical Documentation – The analytical results are summarized in a final 

report and the analytical approaches are documented in a technical document. This task 

also may include presentation of study results to technical supervisors, elected officials, and 

the general public.
75 

Project Scope

& Data Collection

Existing Conditions Model 

Development (“No Build”)

Compare Model MOEs to Field 

Data

Acceptable 

Match

Calibrated Model 

(“No Build”)

Adjust Model 

Parameters

No

Yes

Initial Modeling

Existing Conditions 

Model Calibration

Alternatives Analysis

Final Report/

Technical Documentation

Model 

Application

Work Zone Base Conditions 

Model

Compare Model MOEs to Field 

Data

Acceptable 

Match

Calibrated Model 

(Work Zone Base)

Adjust Model 

Parameters

No

Yes

Work Zone Base 

Conditions Model 

Development and 

Calibration

Figure 12. Typical Model Development and Application Process 

for Work Zone Analysis 
76 

Error Checking

The error-checking step is necessary to identify and correct model coding errors so that they do 

not interfere with the model calibration task. Coding errors can distort the model calibration 

process and cause the analyst to adopt incorrect values for the calibration parameters. Error 

checking involves various tests of the coded network and the demand data to identify input 

coding errors. The goal of the error-checking step is to eliminate all major errors in network 

coding and demand before model calibration. 

Error checking involves various reviews of the coded network and coded demands. Error 

checking proceeds in the following basic stages: 

1. Review Software Errors – The analyst should review the software and user group web 

sites to ensure that he or she is aware of the latest known “bugs” and user workarounds for 

the software. The analyst should ensure that he or she is using the latest “patch” of the 

software.

2. Review Coded Network – The analyst should review the coded network, including: 

a. Check network connectivity. 

b. Check geometry, including lengths, number of lanes, free-flow speed, facility type, etc. 

Overlaying the coded network over aerial photographs of the study area is a quick way 

to verify the accuracy of the coded network geometry. In addition, color codes may be 

used to help identify network links with different attributes.

c. Check intersection controls, including control type and control data, if there are 

intersections in the network. 

d. Check lane closures or restrictions. 

3. Review Demand Data – The analyst should review the demand data, including: 

a. Check vehicle inputs or O-Ds;

b. Check vehicle mix proportions; and 

c. Verify output volumes against traffic counts. 

4. Other Reviews – There are some other review items that are tool-specific. For instance, 

microsimulation models require animation review to spot less obvious input errors. 

Animation output enables the analyst to see the vehicle behavior that is being modeled and 

assess the reasonableness of the microsimulation model itself. Running the simulation 

model and reviewing the animation, even with artificial demands, can be useful to identify 

input coding errors. 

Level of Effort (LOE)

The level of effort on conducting work zone traffic analysis varies with analysis tool types. In 

general, sketch-planning tools require the lowest level of effort, while microscopic simulation 

tools demand the highest level of effort. 

The level of effort determination typically provides labor-hour estimates for project tasks. These 

estimates assist project managers and program leaders in developing budget and schedule 
77 

estimates, as well as the scope of the project. Project tasks typically incorporated into the level 

of effort labor-hour estimates of a traffic analysis project include:

• Data collection;

• Base model and calibration;

• Alternatives analysis;

• Documentation/presentations; and 

• Project management.

For additional information regarding the development of a level of effort especially for a traffic 

analysis project using microsimulation, please refer to the FHWA sponsored Guidance on the 

Level of Effort Required to Conduct Traffic Analysis Using Microsimulation.

(25) 

In the subsequent sections, some of the commonly used software packages are mentioned in each 

tool category, and parameters are suggested to adjust for work zone analysis. Please note that 

these are associated with the current software versions, and future software updates may result in 

different parameters. Please refer to the latest documentation of the software for the most up-todate interpretation of those parameters.

4.2 USING SKETCH-PLANNING AND ANALYTICAL/DETERMINISTIC ANALYSIS

Sketch-planning and analytical/deterministic analysis tools (HCM-based) are typically 

specialized models designed for a specific application, such as work zone analysis. These types 

of tools are in contrast to other general-purpose tools, such as travel demand models and 

microscopic simulation models, which are targeted towards a much wider range of application, 

not just work zone analysis. Sketch-planning and analytical/deterministic analysis tools include 

a wide range of software, including spreadsheet models developed by individuals or state DOTs 

for specific projects or conditions, to more generalized delay estimation tools such as QuickZone 

and QUEWZ-98. They vary in complexity from analyzing only individual roadway segments to 

network-based analyses. Often, they are based on simple queuing techniques or volume-tocapacity relationships from the HCM.

The key strength of sketch-planning and analytical/deterministic analysis tools is their 

relative ease of use and ability to facilitate a rapid analysis. Typically, a sketch-planning or 

analytical/deterministic tool requires fewer resources and less staff training since they are 

simpler in terms of data requirements, calibration, and interpretation of the results. An analysis 

using a sketch-planning or analytical/deterministic tool is usually quite rapid. This is important 

in cases where a decision needs to be made quickly or the agency desires a less resourceintensive analysis. In addition, some sketch-planning analysis tools can analyze longer time 

periods, such as multiple days, months, or even years, which is typically beyond the capability of 

other analysis tools such as macro-, meso-, and microscopic simulation models. 

Some of the challenges of sketch-planning and analytical/deterministic analysis tools 

include limited network complexity and presentation capabilities, and the high level of the 

analysis techniques. For example, QUEWZ-98 only allows simple “pipeline” analysis without 

the ability to model cross-streets and detour routes. The results from sketch-planning and 
78 

analytical/deterministic analysis tool are relatively high-level (e.g., average queue and maximum 

queue) and the results are constrained by the quality of the input data and analysis methodology. 

Sketch-planning and analytical/deterministic analysis tools are primarily used in the planning 

stages of a project, and are not considered to be a substitute for the detailed engineering analysis 

often needed later in the project implementation process. 

Some examples of sketch-planning and analytical/deterministic analysis tools are listed below. 

For a complete list, please refer to Traffic Analysis Toolbox Volume I: Traffic Analysis Tools 

Primer.

(8)

• FREEVAL – http://www.hcm2010.org/; 

• HCS (Highway Capacity Software) – http://www.mctrans.ce.ufl.edu/hcs/; 

• IDAS (ITS Deployment Analysis System) – http:/www./idas.camsys.com/; 

• QUEWZ-98 – http://www.tti.tamu.edu/publications/catalog/record/?id=2920; and

• QuickZone – http://www.fhwa.dot.gov/research/topics/operations/travelanalysis/

quickzone/. 

The computational engine, FREEVAL (FREeway EVALuation) 2010 as shown in Figure 13, is a 

computerized, work sheet-based environment designed to implement the operational analysis 

computations for undersaturated and oversaturated directional freeway facilities.(26) It 

incorporates entire freeway segment procedures outlined in Chapters 11, 12, and 13 in 

HCM2010 for basic freeway segments, weaving segments, and merge and diverge segments, 

respectively. FREEVAL-2010 is executed in Microsoft Excel, with most computations 

embedded in Visual Basic modules. It allows the user to analyze a freeway facility of up to 70 

analysis segments and for up to 24, 15-minute time intervals (six hours). The engine can 

generally handle any facility that falls within these temporal and spatial constraints. It provides 

clean outputs, such as summary work sheets with segment performance (e.g., speed, density, 

volume, volume-to-capacity ratio (v/c), demand-to-capacity ratio (d/c), and LOS, and 3-D 

contour plots. FREEVAL-2010 can handle both undersaturated (d/c ≤ 1.0) and oversaturated 

traffic conditions (d/c > 1.0). When the facility is considered to be “oversaturated,” an additional 

output, i.e., demand-based LOS, is generated.

FREEVAL-2010 is not a commercial software product; instead, it is made available freely to all 

HCM users in HCM2010s on-line Volume 4. FREEVAL-2010 allows the user to make changes 

to geometry, segment capacity, demand patterns, and free-flow speeds to analyze the operational 

effects of work zones. 
79 

Figure 13. FREEVAL-2010 

(Source: Rouphail, Schroeder, and Eads, 2011.) 

Data Collection 

The precise input data required by a sketch-planning or analytical/deterministic analysis tool will 

vary by software and the specific modeling application as defined by the study objectives and 

scope. Most sketch-planning and analytical/deterministic analysis tools require the following 

types of input data, besides the general data identified at the beginning of this chapter. 

Existing Models 

Check if the agency maintains a database of sketch-planning and analytical/deterministic 

analysis model files. A field visit may need to be performed to verify the accuracy of the model 

input data. 

Traffic Volumes

Most sketch-planning and analytical/deterministic analysis tools require traffic volumes as input 

data. Traffic volumes can be either collected in the field, be extracted from existing detectors (if 

available), or exported from other analysis tools. Also, procedures exist in many demand models 

to generate O-D tables for the study area, which can be easily converted to traffic volumes. 
80 

Calibration Data

Calibration data consist of measures of traffic counts and system performance such as speeds and 

queues. 

Other Data

Some sketch-planning and analytical/deterministic analysis tools such as QuickZone require 

detailed work zone project data, such as project start date, project duration, and construction 

phases.

Capacity in Modeling Tools 

At the beginning of this chapter, details on work zone lane capacity were presented. It is 

strongly recommended that local data at work zones be collected and used, as driver 

characteristics vary from region to region and capacity may vary from location to location. In 

the absence of better local data, the analyst may use the capacity defaults as the starting point and 

make modifications accordingly to reflect local work zone conditions. 

Capacity can be directly specified in most sketch-planning and analytical/deterministic analysis 

tools. Typically, the user can adjust the capacity for any type of link in the network, if needed. 

In addition, the free-flow speed (if appropriate) can be modified as well for capacity calibration. 

Table 30 lists major parameters that the analyst may adjust for work zone analysis in several 

commonly used sketch-planning and analytical/deterministic analysis tools.

Table 30. Parameters to Adjust for Work Zone Analysis in Common SketchPlanning and Analytical/Deterministic Analysis Tools

Sketch-Planning and 

Analytical/Deterministic 

Analysis Tools Primary Parameters Secondary Parameters

FREEVAL • Capacity Adjustment Factors

• Free-Flow Speed

• Number of Lanes

• Traffic Composition (Percent Trucks)

HCS • Capacity

• Free-Flow Speed

• Number of Lanes

• Traffic Composition 

IDAS • Lane Capacity

• Free-Flow Speed

• Number of Lanes

• N/A

QUEWZ-98 • Work Zone Capacity

• Number of Lanes Closed

• Length of Lane Closure

• Schedule of Work Activity

• Percentage of Trucks

• Speed-Volume Relationship

• Definition of Excessive Queuing

QuickZone • Capacity

• Number of Lanes

• Free-Flow Speed

• Percent of Local Traffic 

Traveling on Detours 

(Percent) 

• Truck Percentage

• Yearly Capacity Decrease (Percent) 

• Yearly Demand Increase (Percent, for Large 

Projects that Extend over Several Years)

• Mode Shift Change (Percent) 

• Cancel Trip (Percent)
81 

Key Considerations Regarding Use for Analysis 

The key strengths of sketch-planning and analytical/deterministic analysis tools are their relative 

ease of use and ability to facilitate a rapid analysis. This is important in cases where a decision 

needs to be made quickly or the agency desires a less resource-intensive analysis. Some sketchplanning tools can analyze a longer time span in one model, such as multiple days, months, or 

even years of work zones, which is typically beyond the capability of other analysis tools such as 

macro-, meso-, and microscopic simulation models. Discussed below is the model development 

and application process when using analytical/deterministic analysis tools.

Model Development and Application Process 

The typical model development and application process, which was discussed at the beginning of 

this chapter, applies to sketch-planning and analytical/deterministic analysis tools as well. 

Compared to other types of tools, the model calibration of sketch-planning and 

analytical/deterministic analysis tools is more high-level and less time-consuming. 

Sketch-Planning Model Development and Application – Check List

This subsection provides a high-level synthesis combining the concepts presented above on 

model development and application. Please note that not all the items listed below are required 

for all work zone analysis. 

• Determine Study Objectives – The goals should directly correlate to the work zone 

safety, mobility, constructability concerns, and needs of the agency. The performance 

objectives then determine the selection of work zone alternatives or strategies that are 

designed to meet the established goals. 

• Identify Needed Coordination Tasks – Determine what coordination must occur 

between other projects, agencies, facilities, and events. This input may require additional 

modeling to quantify impacts, such as when work is to occur on an arterial that contains 

mass transit.

• Determine Study Network – Determine the limits of the study network. The network 

boundary should be able to accommodate queue lengths under the worst scenario, 

including the work zone conditions. The network also should include potential detour 

routes, both signed and unsigned. 

• Determine Analysis Time Period – Determine the analysis time period. If needed, 

create different time-of-day models, such as a.m. peak, off-peak, and p.m. peaks. For 

longer duration work zones, it is recommended that interim analysis be performed to 

better reflect varying capacities, if resources and data are available. Each analysis period 

should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period. 
82 

• Confirm Analysis Method – Verify that the analysis tool(s) selected have sufficient 

capabilities to satisfy the analysis needs with regards to work zone characteristics, 

transportation management plan, data, resources, and performance measures. 

• Select Measures of Effectiveness and Thresholds – The MOEs or thresholds selected 

will be customized to the agency’s goals and project characteristics. The MOEs and 

thresholds set a minimum performance standard that the alternatives must meet or 

exceed.

• Collect and Analyze Data – Collect data that is needed to model work zone operations. 

Depending on the complexity of the study network and of the analysis needed, different 

types of data may be required. Data may broadly be classified into four categories: 

general, geometric, traffic, and construction.

• Identify Work Zone Alternatives – Identify potential work zone alternatives for 

evaluation. 

• Conduct Fatal Flaw Analysis – Perform a fatal flaw analysis on each selected 

alternative to determine the viability of the alternative for further analysis.

• Determine Work Zone Capacity – Roadway capacity in a work zone is reduced due to 

a variety of factors. It is strongly recommended that local data at work zones be collected 

and used. Check with the appropriate stakeholder agencies as some of them may have 

their own guidelines on work zone capacity and related defaults. In the absence of better 

local data, the analyst may use the proposed defaults as the starting point and make 

modifications accordingly to reflect local work zone conditions. 

• Develop Existing Conditions Model – Develop an existing conditions model with the 

correct geometry, traffic demands, capacities, and traffic controls. Error checking should 

be performed to identify and correct the model coding errors. 

• Calibrate Existing Conditions Model – Calibrate the model by adjusting a set of useradjustable parameters in the analysis tool to better match specific local conditions.

• Develop Work Zone Base Conditions Model – Develop a work zone base conditions 

model by modifying the calibrated existing conditions model to represent work zone 

conditions. 

• Calibrate Work Zone Base Conditions Model – Work zone model calibration includes 

the calibration of both work zone capacity and performance measures. 

• Conduct Alternative Analysis – The alternatives analysis may involve the forecasting of 

the future demand and the testing of various project alternatives against the base case. 

The analyst needs to run the model for each alternative, review the output, compare 

against any performance requirements, extract relevant statistics, and perform various 

analyses of the results.

• Recommend Best Alternative – Utilizing a decision framework, the best overall 

alternative is selected.

• Document Findings – The analytical results are summarized in a final report and the 

analytical approaches are documented in a technical document.

4.3 USING TRAVEL DEMAND MODEL ANALYSIS

Travel demand models are analytical models that forecast future travel demand based on current 

conditions, and future projections of household and employment characteristics. Traditionally, 
83 

they are large regional planning models used by metropolitan planning organizations (MPO), and 

were originally developed to determine the benefits and impacts of major highway improvements 

in metropolitan areas. A distinguishing feature of travel demand models is their geographic 

coverage, which generally includes an entire metropolitan area: a city, its suburbs, and the 

adjacent counties.

For work zone analysis, a key strength in using travel demand models is their ability to 

predict areawide traffic redistribution. For example, if an agency is considering closing an 

important urban freeway-to-freeway interchange for several months while it is rebuilt, a travel 

demand model could help evaluate the overall changes in total daily traffic volumes on various 

roadways throughout the region. In general, travel demand models can handle larger networks 

more efficiently than mesoscopic and microscopic simulation models. Another important 

consideration is that most major metropolitan areas already have a travel demand model 

available that could be used either as the sole analysis tool or as a foundation for developing a 

transportation network or forecasting travel demand for another model type (e.g., mesoscopic 

and microscopic simulation models). 

However, travel demand models have limited capabilities to accurately estimate changes in 

operational characteristics (such as speed, delay, and queuing) resulting from the 

implementation of operational strategies and changes. This includes the effects associated with 

roadwork construction. Because these models are prepared at a broad regional scale, they lack 

detail that may be critical to the analysis objective. For example, they may model only one or 

two time periods (such as the a.m. peak hour or the daily average), which may not be sufficient 

for analyzing time-specific work zone traffic management strategies.

Some examples of travel demand models are listed below. For a complete list, please refer to 

Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer.

(8)

• CUBE – http://www.citilabs.com/products/cube;

• EMME – http://www.inro.ca/en/products/emme/index.php; 

• TransCAD – http://www.caliper.com/tcovu.htm; and

• VISUM – http://www.ptvamerica.com/. 

Data Collection 

Besides the general data identified at the beginning of this chapter, travel demand models require 

the following types of input data. 

Existing Models 

Check if the MPO or other local transportation planning agency maintains a regional travel 

demand model. A field visit may need to be performed to verify the accuracy of the model input 

data. Additionally, since the regional travel demand model maintained by an agency typically 

covers a significant area, it may be necessary to have a subarea model created for use in work 

zone analysis (see Subarea Modeling in Key Considerations Regarding Use for Analysis).
84 

Calibration Data (Traffic Counts and Speed/Travel Time) 

For work zone analysis, calibration data typically consist of measures of traffic counts and 

speed/travel time.

Model Refinement Data (Traffic Analysis Zone Splitting)

Often the physical changes to the roadway associated with a work zone are localized in nature 

and, thus, only impact the physical characteristics of the corridor where the work zone is present. 

For larger projects, work zone mitigation strategies may result in physical changes to detour 

routes in addition to changes to the subject corridor. 

Utilizing a travel demand model to capture the impact of a work zone requires the analysis of the 

localized corridor(s) where the work zone is occurring or has a significant impact, as opposed to 

regional travel patterns. As such, it may be necessary to increase the fidelity of the model by 

improving the network details surrounding the corridor(s) being studied. In addition, it 

may be necessary to improve details for parallel corridors if there are significant anticipated 

operational impacts or if potential mitigation strategies make significant changes to the physical 

or operational characteristics of these detour corridors.

A travel demand model typically represents travel in a region by generating trips from an origin 

to a destination, determining the mode split for these trips and then assigning these trips to the 

transportation network. The origin and destinations for these modeled trips are represented by 

Traffic Analysis Zones (TAZ). The size and complexity of these TAZs can vary by location in 

the model. Often, TAZs near the center (often the downtown area of a model) of the model are 

smaller in size and can represent a single city block or several city blocks combined. This TAZ 

will connect to the transportation network with several centroid connectors to allow modeled 

trips to be transferred from the TAZ to the represented transportation network. Further out 

toward the edges of the modeled area, TAZs often become larger and can represent several 

square miles of development with more limited number of centroid connectors. The size of 

TAZs represented near the corridor being studied should be examined to determine if they 

need to be split into smaller TAZs or if additional centroid connectors need to be added to 

the network to accurately represent travel near the corridor.

Capacity in Modeling Tools 

At the beginning of this chapter, details on work zone lane capacity were presented. It is 

strongly recommended that local data at work zones are collected and used, as driver 

characteristics vary from region to region and capacity may vary from location to location. In 

the absence of better local data, the analyst may use the capacity defaults as the starting point and 

make modifications accordingly to reflect local work zone conditions. 
85 

Capacity can be directly specified in travel demand models. Typically, the user can adjust 

the lane capacity for any link type in the network to reflect any capacity reduction due to the 

work zone. 

The major parameters that the analyst may adjust for work zone analysis using travel demand 

models include: 

• Number of lanes;

• Free-flow speed; and

• Capacity (vehicles per hour per lane).

Key Considerations Regarding Use for Analysis 

The main strengths of using travel demand models for work zone analysis are their ability to 

model large geographic areas and predict areawide traffic redistribution. Travel demand models, 

however, have limited capabilities to accurately estimate changes in detailed operational 

strategies (such as optimized signal operations). Therefore, it is important that the tool selection 

process detailed in Chapter 3 has been undertaken to ensure that travel demand models are the 

appropriate tool for the specific work zone analyzed. Discussed below are key considerations 

when using travel demand models, including: 

• Subarea modeling;

• Link length change to match work zone area; and

• Model development and application process. 

Subarea Modeling 

Travel demand models generally cover large regional areas and are maintained by MPOs. Travel 

demand models do not have to be cut to smaller subareas for work zone modeling. However, 

sometimes it is necessary to do so for faster modeling and better focus on the work zone and its 

immediate vicinity. If a subarea model is created for a particular analysis, care should be taken 

to ensure that any potential detour or alternate routes be contained within the subarea so that 

impacts from the changes associated with a work zone can be accurately captured.

The subarea models are typically validated using observed hourly count data. An Origin 

Destination Matrix Estimation (ODME) process is typically applied to improve the match 

between model volumes and observed counts in the subarea. 

Creating subarea models is necessary when travel demand models are used to generate OriginDestination (O-D) tables for other tools such as microscopic simulation analysis which do not 

capture traffic diversion directly. The subarea selected corresponds to the area to be modeled in 

the microscopic simulation analysis. Often, the subarea model created from the travel demand 

model can be utilized as a basis for generating the roadway network to be simulated in the 

microsimulation. However, because the roadway networks generally utilized for travel demand 
86 

modeling are developed for conventional static planning applications, they often lack several key 

features. Some of these missing or deficient data include:

• Time-Dependent Component for the O-D Demand – Commonly, the O-D demand is 

associated with a full 24-hour period or broken down into an a.m. peak, p.m. peak, and 

off-peak component. This fidelity may not be sufficient for detailed work zone analysis. 

• Explicit Roadway Network Representation – Most travel demand applications only 

need the roadway network to represent the connections possible between links. 

Consequently, the actual roadway geometry may be approximated. For example, a 

highway interchange loop ramp may only be represented as a link connecting the freeway 

link to the arterial link and not represent the actual curving or loop geometry of the ramp. 

• Signal Control Information – Signal controls are not typically explicitly modeled in a 

travel demand model. As such, signal timing and phasing data are not typically included. 

The inclusion of this data often represents the differentiation between a travel demand model and 

a mesoscopic model.

The use of a travel demand model subarea as a starting place for the development of another 

simulation tool, such as microsimulation, calls for careful integration of several different data 

sources. When utilizing a travel demand model to support analysis from another simulation tool, 

the interface between the outputs of the travel demand model and the necessary inputs to the 

other simulation tool can vary widely. Depending on the models utilized, this link can vary from 

being nearly automated to very labor intensive. For example, the output of travel demand data 

from a VISUM travel demand model to a microsimulation of VISSIM is accomplished via a 

series of automated scripts that come as part of the PTV Simulation Suite. On the other extreme, 

the use of travel demand data for use in the microsimulation model CORSIM requires each input 

volume and turning assignment to be either manually input or a script must be written to 

accomplish this.

Link Length Change to Match Work Zone Area 

Some links in travel demand models can be very long; it is not unusual to see links with a length 

of several miles. The work zone analyzed may be shorter than the corresponding link in the 

travel demand model. In this case, the link in the travel demand model has to be broken into 

shorter ones so that different link attributes (such as free-flow speed and capacity) can be 

assigned properly. 

Model Development and Application Process 

The typical model development and application process, which has been discussed at the 

beginning of this chapter, applies to travel demand models as well.

Travel demand models do not explicitly represent signal control at intersections. Less 

sophisticated travel demand models may utilize a simple volume-delay curve to represent the 

impedance of the signalized intersection on the approach links. The more sophisticated travel 

demand models utilize a “capacity calculator” to approximate the impact of signal control on the 
87 

upstream links to the intersection. This capacity calculator applies HCM style intersection 

analysis to the turning movements calculated for each intersection. The results of this analysis 

are used to generate penalties for each movement, which are applied during the next iteration of 

the travel demand model.

1. Travel demand models have a set of user-adjustable parameters that enable the analyst to 

calibrate the software to better match local conditions. This calibration process involves the 

selection of a few parameters for calibration and the repeated operation of the model to 

identify the best values for those parameters. The calibration process can be timeconsuming. It should be well documented so that later reviewers of the model can 

understand the rationale for the various parameter changes made during calibration. For 

details on the calibration and validation process, please refer to the Model Validation and 

Reasonableness Checking Manual.

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2. In most instances, it is beyond the scope of most work zone projects to undertake 

recalibration and validation of the regional travel demand model. There are typically strict 

calibration and validation guidelines associated with these models, which must be 

documented and verified by an independent agency. As such, this step is often skipped. 

Travel Demand Model Development and Application – Checklist

This subsection provides a high-level synthesis combining the concepts presented above on 

model development and application for travel demand model use for work zone analysis. Please 

note that not all the items listed below are required for all work zone analysis.

• Determine Study Objectives – The goals should directly correlate to the work zone 

safety, mobility, constructability concerns, and needs of the agency. The performance 

objectives then determine the selection of work zone alternatives or strategies that are 

designed to meet the established goals. 

• Identify Needed Coordination Tasks – Determine what coordination must occur 

between other projects, agencies, facilities, and events. This input may require additional 

modeling to quantify impacts, such as when work is to occur on an arterial that contains 

mass transit.

• Determine Study Network – Determine the limits of the study network. The network 

boundary should be able to accommodate queues under the worst scenario, including the 

work zone conditions. The network also should include potential detour routes, both 

signed and unsigned. For faster modeling and better focus on the work zone and its 

immediate vicinity areas, a subarea modeling may be necessary.

• Conduct Subarea Modeling – Determine whether a subarea model is necessary. Travel 

demand models do not have to be cut to smaller subareas for work zone modeling. 

However, sometimes, it is necessary to do so for faster modeling, better focus on the 

work zone and its immediate vicinity areas, and integration with other tools such as 

microsimulation analysis. Care should be taken to ensure that potential detour or 

alternate routes are contained within the subarea so that impacts from the changes 

associated with a work zone can be accurately captured.

• Split TAZs – The size of TAZs represented near the corridor being studied should be 

examined to determine if they need to be split into smaller TAZs or if additional centroid 
88 

connectors need to be added to the network to accurately represent travel near the 

corridor.

• Determine Analysis Time Period – Determine the analysis time period. If needed, 

create different time-of-day models, such as a.m. peak, off-peak, and p.m. peak. For 

longer duration work zones, it is recommended that interim analysis be performed to 

better reflect varying capacities, if resources and data are available. Each analysis period 

should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period. 

• Confirm Analysis Method – Verify that the analysis tool(s) selected have sufficient 

capabilities to satisfy the analysis needs with regards to work zone characteristics, 

transportation management plan, data, resources, and performance measures. 

• Select Measures of Effectiveness and Thresholds – The MOEs or thresholds selected 

will be customized to the agency’s goals and project characteristics. The MOEs and 

thresholds set a minimum performance standard that the alternatives must meet or 

exceed.

• Collect and Analyze Data – Collect data that are needed to model work zone operations. 

Depending on the complexity of the study network and of the analysis needed, different 

types of data may be required. Data may broadly be classified into four categories: 

general, geometric, traffic, and construction.

• Identify Work Zone Alternatives – Identify potential work zone alternatives for 

evaluation. 

• Conduct Fatal Flaw Analysis – Perform a fatal flaw analysis on each selected 

alternative to determine the viability of the alternative for further analysis.

• Determine Work Zone Capacity – Roadway capacity in a work zone is reduced due to 

a variety of factors. It is strongly recommended that local data at work zones be collected 

and used. Check with the appropriate stakeholder agencies as some of them may have 

their own guidelines on work zone capacity and related defaults. In the absence of better 

local data, the analyst may use the proposed defaults as the starting point and make 

modifications accordingly to reflect local work zone conditions. 

• Develop Existing Conditions Model – Develop an existing conditions model with the 

correct geometry, traffic demands, capacities, and traffic controls. Error checking should 

be performed to identify and correct the model coding errors. 

• Calibrate Existing Conditions Model – Calibrate the model by adjusting a set of useradjustable parameters in the analysis tool to better match specific local conditions.

• Develop Work Zone Base Conditions Model – Develop a work zone base conditions 

model by modifying the calibrated existing conditions model to represent work zone 

conditions. 

• Change Link Length – The work zone analyzed may be shorter than the corresponding 

link in the travel demand model. In this case, the link in the travel demand model needs 

to be broken to shorter ones so that different link attributes (such as free-flow speed and 

capacity) can be assigned properly.

• Calibrate Work Zone Base Conditions Model – Work zone model calibration includes 

the calibration of both work zone capacity and performance measures. 

• Conduct Alternative Analysis – The alternatives analysis may involve the forecasting of 

the future demand and the testing of various project alternatives against the base case. 

The analyst needs to run the model for each alternative, review the output, compare 
89 

against any performance requirements, extract relevant statistics, and perform various 

analyses of the results.

• Recommend Best Alternative – Utilizing a decision framework, the best overall 

alternative is selected.

• Document Findings – The analytical results are summarized in a final report and the 

analytical approaches are documented in a technical document.

4.4 USING TRAFFIC SIGNAL OPTIMIZATION ANALYSIS 

Traffic signal optimization tools are designed to develop optimal signal phasing and timing plans 

for isolated signalized intersections, arterial streets, or signalized networks. The more advanced 

traffic optimization tools are capable of modeling actuated and semi-actuated traffic signals, with 

or without signal coordination. 

With respect to work zones, traffic signal optimization tools are useful when developing a signal 

plan for temporary traffic signal controls or analyzing signal plans when a detour route directs 

traffic to an existing signalized arterial roadway. The primary limitation of these tools is their 

single focus. Therefore, traffic signal optimization tools are typically used to provide 

supplementary analysis when analyzing the overall mobility impacts of a work zone. Signal 

optimization tools are sometimes combined with simulation tools to have a better understanding 

of traffic operations (both calibration of existing conditions models, as well as future alternatives 

analysis). Examples of this combination are Synchro/SimTraffic and TRANSYT-7F/VISSIM.

Some examples of traffic signal optimization tools are listed below. For a complete list, please 

refer to Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer.

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• PASSER IV-96 – http://ttisoftware.tamu.edu/fraPasserIV_96.htm;

• Synchro – http://www.trafficware.com;

• TEAPAC/SIGNAL2000 – http://www.strongconcepts.com/Products.htm; and 

• TRANSYT-7F – http://www.mctrans.ce.ufl.edu/store/description.asp?itemID=437. 

Data Collection 

The precise input data required by a signal optimization model will vary by software and the 

specific modeling application as defined by the study objectives and scope. Besides the common 

data discussed at the beginning of this chapter, most signal optimization analytical work zone 

studies require the following types of input data. 

Existing Models 

Check if any of the local agencies maintain a database of traffic signal optimization model files. 

A field visit may need to be performed to verify the accuracy of the model input data. 
90 

Capacity in Modeling Tools 

At the beginning of this chapter, details on work zone lane capacity were presented. It is 

strongly recommended that local data at work zones are collected and used, as driver 

characteristics vary from region to region and capacity may vary from location to location. In 

the absence of better local data, the analyst may use the capacity defaults as the starting 

point and make modifications accordingly to reflect local work zone conditions. 

Once the work zone capacity is determined, the next step is to implement it in the signal 

optimization model. Among other things, the base saturation flow rate (SFR) is typically 

adjusted to represent the reduced capacity due to work zone operations. This can be 

accomplished either by reducing the lane widths/number of lanes through the work zone, or by 

changing the base saturation flow rate directly.

Table 31 lists some major parameters that the analyst may adjust for work zone analysis in 

several commonly used signal optimization software programs, including Synchro, Passer, and 

TRANSYT-7F.

Table 31. Parameters to Adjust for Work Zone Analysis in Common 

Signal Optimization Software 

Signal Optimization 

Software Primary Parameters Secondary Parameters

SYNCHRO • Number of lanes

• Ideal saturation flow rate

• Speed limit

• Lane inputs, including turn bays and U-turns

• Peak hour factor

• Heavy vehicle percentage

PASSER • Number of lanes

• Ideal saturation flow rate

• Speed limit

• Lane inputs, including turn bays

• Peak hour factor

• Heavy vehicle percentage

TRANSYT-7F • Number of lanes

• Base saturation flow rate

• Lane inputs, including turn bays

• Peak hour factor

• Heavy vehicle percentage

• Grade percentage

Key Considerations Regarding Use for Analysis 

Signal optimization tools have a variety of applications for work zone analysis, especially in 

urban and suburban environments. Broadly speaking, these applications can be grouped in four 

categories:

1. Preparing timing plans for temporary signals used to manage traffic within a work zone; 

2. Preparing timing plans for flagger or portable signals used to manage traffic within a work 

zone; 

3. Adjusting signal timing on corridors that are directly impacted by work zone; and 
91 

4. Adjusting signal timing to improve progression on corridors that serve as alternate routes or 

detours around a work zone. 

Model Development and Application Process 

Signal optimization tools can be used to identify the need for temporary traffic signals and to 

revise the signal timing plans for existing signals where volumes are expected to increase or 

decrease as a result of the construction. 

The typical model development and application process, which was discussed at the beginning of 

this chapter, applies to signal optimization tools as well. It should be noted that model 

calibration might not be required for some projects. If the analyst is unsure as to whether 

calibration will be required, consult with the contracting agency for a determination for the 

specific project.

For example, the Work Zone Analysis Guide by the MD SHA recommends the following 

guidelines be used to calibrate a Synchro model:(15)

• Synchro queue lengths should match field-measured queues on the critical movements 

within an 85 percent error (for field-measured queues shorter than 10 vehicles, a 2-

vehicle error may be acceptable).

• Synchro travel times should match field-measured travel times within 85 percent. Note 

that the travel times should be compared for the overall length of the arterial, and not on a 

link-by-link basis. 

• The SimTraffic simulations, a microsimulation tool coupled with Synchro, should match 

field-observed bottlenecks to the analysts’ satisfaction.

A work zone base conditions model is then developed by modifying the calibrated existing 

conditions model to represent work zone conditions. Adjustment to the base model to create the 

work zone base conditions model may include: 

• Lane Configurations: 

• Lane reductions on arterial segments can be modeled explicitly in most signal 

optimization tools. For instance, Synchro uses bend nodes and TRANSYT-7F uses 

dummy nodes to model lane reductions. Using Synchro as an example, bend nodes 

can be created on either end of the proposed work zone, overriding the number of 

lanes on one or both directions of the roadway. Take into consideration the required 

taper and buffer lengths approaching the work zone and adjust the model accordingly. 

• When lane reductions occur through an intersection, take into consideration any 

impacts that this may have on the turning lanes from the side street(s).

• When lane and/or shoulder widths are reduced, the work zone capacity should be 

reduced accordingly.

• Traffic Volumes:

• When a detour route is being proposed, it will be necessary to redistribute the traffic 

volumes in the network based on O-D data and engineering judgment.
92 

• If work zone demand management strategies are being employed, it may be necessary 

to reduce the traffic volumes by the estimated reduction in vehicle demand through 

the work zone. 

• Saturation Flow Rate:

• Where there are no lane reductions, but where work would occur adjacent to the 

roadway, it may be necessary to adjust the saturation flow rate to represent the 

reduced capacity due to work zone operations. This can be accomplished either by 

reducing the lane widths through the work zone, or by changing the saturation flow 

rate directly.

For alternatives analysis, the analyst needs to run the model for each alternative, review the 

output, compare against any mobility requirements, extract relevant statistics, and perform 

various analyses of the results. Some agencies may have signal-related mobility criteria. For 

instance, MD SHA uses LOS, control delay, and travel time as mobility requirements, as listed in 

Table 32.(15)

Table 32. Mobility Thresholds for Arterials (MD SHA)

Signalized Intersections

Existing LOS Mobility Threshold

A, B, or C Maximum LOS D, control delay ≤ 45 seconds

D Maximum increase in control delay of 30%

E Maximum increase in control delay of 30%, or control delay ≤ 80 seconds

F No increase in control delay

Arterials

TT Travel time cannot increase more than 15 minutes (maximum of TT+15)

(Source: Maryland State Highway Administration, 2008.)

Traffic Signal Optimization Model Development and Application – Checklist

This subsection provides a high-level synthesis combining the concepts presented above on 

model development and application for work zone analysis involving signal optimization tools. 

Please note that not all the items listed below are required for all work zone analysis.

• Determine Study Objectives – The goals should directly correlate to the work zone 

safety, mobility, constructability concerns, and needs of the agency. The performance 

objectives then determine the selection of work zone alternatives or strategies that are 

designed to meet the established goals. 

• Identify Needed Coordination Tasks – Determine what coordination must occur 

between other projects, agencies, facilities, and events. This input may require additional 

modeling to quantify impacts, such as when work is to occur on an arterial that contains 

mass transit.

• Determine Study Network – Determine the limits of the study network. The network 

boundary should be able to accommodate queues under the worst scenario, including the 

work zone conditions. The network also should include potential detour routes, both 

signed and unsigned. 
93 

• Determine Analysis Time Period – Determine the analysis time period. If needed, 

create different time-of-day models, such as a.m. peak, off-peak, and p.m. peak. For 

longer duration work zones, it is recommended that interim analysis be performed to 

better reflect varying capacities, if resources and data are available. Each analysis period 

should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period. 

• Confirm Analysis Method – Verify that the analysis tool(s) selected have sufficient 

capabilities to satisfy the analysis needs with regards to work zone characteristics, 

transportation management plan, data, resources, and performance measures. 

• Select Measures of Effectiveness and Thresholds – The MOEs or thresholds selected 

will be customized to the agency’s goals and project characteristics. The MOEs and 

thresholds set a minimum performance standard that the alternatives must meet or 

exceed.

• Collect and Analyze Data – Collect data that is needed to model work zone operations. 

Depending on the complexity of the study network and of the analysis needed, different 

types of data may be required. Data may broadly be classified into four categories: 

general, geometric, traffic, and construction. 

• Identify Work Zone Alternatives – Identify potential work zone alternatives for 

evaluation. 

• Conduct Fatal Flaw Analysis – Perform a fatal flaw analysis on each selected 

alternative to determine the viability of the alternative for further analysis.

• Determine Work Zone Capacity – Roadway capacity in a work zone is reduced due to 

a variety of factors. It is strongly recommended that local data at work zones are 

collected and used. Check with the appropriate stakeholder agencies as some of them 

may have their own guidelines on work zone capacity and related defaults. In the 

absence of better local data, the analyst may use the proposed defaults as the starting 

point and make modifications accordingly to reflect local work zone conditions. 

• Develop Existing Conditions Model – Develop an existing conditions model with the 

correct geometry, traffic demands, capacities, and traffic controls. Error checking should 

be performed to identify and correct the model coding errors. 

• Calibrate Existing Conditions Model – Calibrate the model by adjusting a set of useradjustable parameters in the analysis tool to better match specific local conditions.

• Develop Work Zone Base Conditions Model – Develop a work zone base conditions 

model by modifying the calibrated existing conditions model to represent work zone 

conditions. 

• Calibrate Work Zone Base Conditions Model – Work zone model calibration includes 

the calibration of both work zone capacity and performance measures. 

• Conduct Alternative Analysis – The alternatives analysis may involve the forecasting of 

the future demand and the testing of various project alternatives against the base case. 

The analyst needs to run the model for each alternative, review the output, compare 

against any performance requirements, extract relevant statistics, and perform various 

analyses of the results.

• Recommend Best Alternative – Utilizing a decision framework, the best overall 

alternative is selected.

• Document Findings – The analytical results are summarized in a final report and the 

analytical approaches are documented in a technical document.
94 

4.5 USING MACROSCOPIC ANALYSIS

Macroscopic simulation models are based on the deterministic relationships of the flow, 

speed, and density of the traffic stream. The simulation in a macroscopic model takes place 

on a section-by-section basis, meaning they treat traffic flows as an aggregated quantity; they do 

not model the movement of individual vehicles on a network. 

Similar to travel demand models, a key feature of macroscopic models is their ability to model 

large geographic areas. This ability is particularly useful when the work zone impacts may affect 

a larger corridor or region, where large geographic impacts need to be better understood based 

upon a certain work zone design such as a full closure. Macroscopic simulation models also can 

be set up and run fairly quickly due to the fact that they simulate aggregate flows, speeds, and 

density measures on each section of the network (rather than individual vehicles). 

However, the simple representation of traffic movement (e.g., no car-following/lane-changing 

algorithms) in macroscopic simulation models may limit the fidelity of the results. Also, 

macroscopic simulation models may provide limited detail in simulating real-world traffic 

conditions. They do not have the ability to analyze transportation improvements in as much 

detail as the mesoscopic and microscopic models. 

Some examples of macroscopic models are listed below. For a complete list, please refer to 

Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer.

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• FREQ – http://its.berkeley.edu/systems/research; and

• NetZone – http://www.dot.ca.gov/researchconn/past_speakers/DrZhang/

michael_zhang_pres_april_26.pdf. 

Data Collection 

The precise input data required by a macroscopic model will vary by software and the specific 

modeling application as defined by the study objectives and scope. Most macroscopic studies 

require the following types of input data besides the general data identified at the beginning of 

this chapter.

Existing Models 

Check if a local agency maintains a database of macroscopic model files. A field visit may need 

to be performed to verify the accuracy of the model input data. 

Travel Demand

Most macroscopic models require O-D tables as input data. Procedures exist in many demand 

models to generate O-D tables for the study area. Certain macroscopic models, such as FREQ, 

have the capability to derive O-D tables based on traffic counts.
95 

Calibration Data

Calibration data consist of measures of traffic counts and system performance such as travel 

times, speeds, delays, and queues. 

Other Data

Some macroscopic models (e.g., KRONOS) require a flow-speed-density (q-v-k) relationship for 

each roadway type (e.g., freeway and arterial). The q-v-k relationship can be acquired using 

typical tube counter data, in which the average speed and counts are reported per user-defined 

interval. First, the counts can be converted to flow rates (q). If the reported speed is the spacemean-speed (total distance divided by total time that the speed trap is occupied), then the density 

can be calculated by taking k = q/v. If the average is the time-mean-speed (e.g., the average 

speed is the arithmetic mean of the individual speed measures), then three percent to five percent 

reduction from the time-mean-speed can be used to approximate the space-mean-speed. 

Capacity in Modeling Tools 

At the beginning of this chapter, details on work zone lane capacity were presented. It is 

strongly recommended that local data at work zones be collected and used, as driver 

characteristics vary from region to region and capacity may vary from location to location. In 

the absence of better local data, the analyst may use the capacity defaults as the starting point, 

and make modifications accordingly to reflect local work zone conditions. 

Capacity can be directly specified in most macroscopic models. Typically, the user can 

adjust the capacity for any type of link in the network, if needed. In addition, the jam density 

and speed limit (if appropriate) can be modified as well for capacity calibration. Table 33 lists 

major parameters that the analyst may adjust for several commonly used macroscopic simulation 

software.
96 

Table 33. Parameters to Adjust for Work Zone Analysis in Common 

Macroscopic Simulation Software 

Signal 

Optimization 

Software Primary Parameters Secondary Parameters

FREQ • Capacity

• Speed

• Speed curves (volume-speed relationship)

• Traffic composition (percent trucks)

KRONOS • Capacity

• Minimum delay speed

• Jam density

• Critical density

• Traffic composition (vehicle types)

NetZone • Capacity

• Capacity reduction (Percent) 

• Free-flow speed

• Jam density

• Critical density

• Queue storage (vehicle/mile)

• Pre-trip traveler information factor

Key Considerations Regarding Use for Analysis 

The strength of macroscopic simulation models is their ability to model large geographic 

areas. This ability is particularly useful when the work zone impacts may affect a larger 

corridor or region. Macroscopic simulation models can be set up and run fairly quickly due to 

the fact that they simulate aggregate flows, rather than individual vehicles. 

Discussed below is the model development and application process when using macroscopic 

simulation tools.

Model Development and Application Process 

The typical model development and application process, which has been discussed at the 

beginning of this chapter, applies to macroscopic simulation as well.

The calibration of macroscopic simulation models typically involves replication of observed 

congestion patterns. Freeway validation is based on travel time run information and speed 

contour diagrams constructed for the analysis periods, which are then aggregated to provide a 

“typical” congestion pattern. Surface-street calibration is based on speed, queue, delay, and 

capacity information. Macroscopic models have considerably less demanding computer 

requirements than microscopic models. They do not, however, have the ability to analyze 

transportation improvements in as much detail as mesoscopic or microscopic models; and do not 

consider trip generation, trip distribution, and mode choice in their evaluation of changes in 

transportation systems.
97 

Macroscopic Simulation Model Development and Application – Checklist

This subsection provides a high-level synthesis combining the concepts presented above on 

model development and application for macroscopic tool work zone analysis. Please note that 

not all the items listed below are required for all work zone analysis. 

• Determine Study Objectives – The goals should directly correlate to the work zone 

safety, mobility, constructability concerns, and needs of the agency. The performance 

objectives then determine the selection of work zone alternatives or strategies that are 

designed to meet the established goals.

• Identify Needed Coordination Tasks – Determine what coordination must occur 

between other projects, agencies, facilities, and events. This input may require additional 

modeling to quantify impacts, such as when work is to occur on an arterial that contains 

mass transit.

• Determine Study Network – Determine the limits of the study network. The network 

boundary should be able to accommodate queues under the worst scenario, including the 

work zone conditions. The network also should include potential detour routes, both 

signed and unsigned. 

• Determine Analysis Time Period – Determine the analysis time period. If needed, 

create different time-of-day models, such as a.m. peak, off-peak, and p.m. peak. For 

longer duration work zones, it is recommended that interim analysis be performed to 

better reflect varying capacities, if resources and data are available. Each analysis period 

should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period. 

• Confirm Analysis Method – Verify that the analysis tool(s) selected have sufficient 

capabilities to satisfy the analysis needs with regards to work zone characteristics, 

transportation management plan, data, resources, and performance measures. 

• Select MOEs and Thresholds – The MOEs or thresholds selected will be customized to 

the agency’s goals and project characteristics. The MOEs and thresholds set a minimum 

performance standard that the alternatives must meet or exceed.

• Collect and Analyze Data – Collect data that is needed to model work zone operations. 

Depending on the complexity of the study network and of the analysis needed, different 

types of data may be required. Data may broadly be classified into four categories: 

general, geometric, traffic, and construction. 

• Identify Work Zone Alternatives – Identify potential work zone alternatives for 

evaluation. 

• Conduct Fatal Flaw Analysis – Perform a fatal flaw analysis on each selected 

alternative to determine the viability of the alternative for further analysis.

• Determine Work Zone Capacity – Roadway capacity in a work zone is reduced due to 

a variety of factors. It is strongly recommended that local data at work zones be collected 

and used. Check with the appropriate stakeholder agencies as some of them may have 

their own guidelines on work zone capacity and related defaults. In the absence of better 

local data, the analyst may use the proposed defaults as the starting point and make 

modifications accordingly to reflect local work zone conditions. 
98 

• Develop Existing Conditions Model – Develop an existing conditions model with the 

correct geometry, traffic demands, capacities, and traffic controls. Error checking should 

be performed to identify and correct the model coding errors. 

• Calibrate Existing Conditions Model – Calibrate the model by adjusting a set of useradjustable parameters in the analysis tool to better match specific local conditions.

• Develop Work Zone Base Conditions Model – Develop a work zone base conditions 

model by modifying the calibrated existing conditions model to represent work zone 

conditions. 

• Calibrate Work Zone Base Conditions Model – Work zone model calibration includes 

the calibration of both work zone capacity and performance measures. 

• Conduct Alternative Analysis – The alternatives analysis may involve the forecasting of 

the future demand and the testing of various project alternatives against the base case. 

The analyst needs to run the model for each alternative, review the output, compare 

against any performance requirements, extract relevant statistics, and perform various 

analyses of the results.

• Recommend Best Alternative – Utilizing a decision framework, the best overall 

alternative is selected.

• Document Findings – The analytical results are summarized in a final report and the 

analytical approaches are documented in a technical document.

4.6 USING MESOSCOPIC ANALYSIS

As discussed above, macroscopic models can only provide limited detail in simulating real-world 

traffic conditions. In certain cases, more in-depth simulation results are required. This is where 

mesoscopic models come into play. 

Mesoscopic simulation models combine the properties of both macroscopic and 

microscopic simulation models (discussed in the next section). As in microscopic models, the 

mesoscopic model’s unit of traffic flow is the individual vehicle. Their movement, however, 

follows the macroscopic properties of traffic flows (such as average speed, density, and flow 

rates) without having to examine the interaction of individual vehicles in detail. Therefore, the 

mesoscopic model provides greater computational efficiency that allows a much faster 

simulation of larger networks than microscopic models, while providing users with more detailed 

information than macroscopic models. For instance, the lane-change maneuver could be 

represented for individual vehicles as an instantaneous event with the decision based on relative 

lane densities, rather than detailed vehicle interactions. Another example is that users have the 

capability to model diversion routes from major roadways (e.g., freeways and highways) to other 

road types (e.g., signalized arterial).

In short, mesoscopic simulation strikes a balance between the macroscopic and microscopic 

models. Mesoscopic models provide users with higher accuracy in simulating real-world traffic 

behavior than macroscopic models, but less accuracy than microscopic models. Mesoscopic 

analysis has the ability to analyze larger geographic areas than microscopic analysis, while still 

providing some of the detailed data that macroscopic analysis cannot provide.
99 

If agency policy stipulates a need to measure lane-by-lane queue lengths, the most accurate 

results will most likely be generated by a microscopic model, since it provides results at the 

vehicle and lane level. However, microscopic tools are particularly complex and costly to 

deploy when modeling large corridors and regions. In these cases, a mesoscopic model or 

sketch-planning model could be used to predict queue lengths when lane-level results are not 

critical. It is important to select the approach that best provides the needed measures relative to 

the available data, resources, and work zone attributes.

Some examples of mesoscopic traffic simulation models are listed below. For a complete list,

please refer to Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer.

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• Aimsun – http://www.aimsun.com/wp/;

• Dynameq – http://www.inro.ca/en/products/dynameq/index.php; 

• DYNASMART-P – http://www.fhwa.dot.gov/research/deployment/dynasmart.cfm; 

• DynusT – http://dynust.net/; and

• TransModeler/Meso – http://www.caliper.com/transmodeler/Simulation.htm.

Data Collection 

The precise input data required by a mesoscopic model will vary by software and the specific 

modeling application, as defined by the study objectives and scope. Most mesoscopic analysis 

work zone studies require the following types of input data besides the general data identified at 

the beginning of this chapter. 

Existing Models 

Check if a local agency maintains a database of mesoscopic model files. A field visit may need 

to be performed to verify the accuracy of the model input data. 

Travel Demand (O-D Table)

Mesoscopic models require O-D tables as input data. Procedures exist in many demand 

modeling software packages to generate O-D tables for the study area.

Calibration Data (Traffic Counts and Performance Data, Such As Speed and Queues)

Calibration data consist of measures of traffic counts and system performance such as travel 

times, speeds, delays, and queues. 

In addition to the above basic input data, speed and density data for each roadway type (e.g., 

freeway and arterial) are required as most mesoscopic models move vehicles according to the 

speed-density (v-k) relationship. The v-k relationship can be acquired using typical tube counter 

data in which the average speed and counts are reported per user-defined interval. First, the 

counts can be converted to flow rates (q). If the reported speed is the space-mean-speed (total 

distance divided by total time that the speed trap is occupied), then the density can be calculated 
100 

by taking k = q/v. If the average is the time-mean-speed (e.g., the average speed is the arithmetic 

mean of the individual speed measures), then three percent to five percent reduction from the 

time-mean-speed can be used to approximate the space-mean-speed.

Capacity in Modeling Tools 

At the beginning of this chapter, details on work zone lane capacity were presented. It is 

strongly recommended that local data at work zones be collected and used, as driver 

characteristics vary from region to region and capacity may vary from location to location. In 

the absence of better local data, the analyst may use the capacity defaults as the starting point and 

make modifications accordingly to reflect local work zone conditions. 

Capacity can be directly specified in most mesoscopic models. Typically, the user can adjust the 

capacity for any link in the network, if needed. In addition, the jam density and speed limit (if 

appropriate) can be modified as well for capacity calibration. Table 34 lists major parameters 

that the analyst may adjust for several commonly used mesoscopic simulation models. 

Table 34. Parameters to Adjust for Work Zone Analysis in Common 

Mesoscopic Simulation Software 

Mesoscopic Simulation Software Parameters

AIMSUN/Meso • Maximum speed

• Jam density

• Curvature geometries

• Intersection geometries

• Reaction time factor

• Look ahead distance

DynaMIT/DYNASMART • Density

• Saturation flow rate

• Maximum service flow rate

• Travel/route guidance

• Weaving sections

DYNAMEQ • Free-flow speed

• Mandatory lane changes

• VMS

DynusT • Service flow rate

• Saturation flow rate

• Jam density

• Speed limit

TRANSMODELER/Meso • Lane width factor

• Speed limit

• Saturation flow rates

• Look ahead

Key Considerations Regarding Use for Analysis 

The strength of mesoscopic simulation models is the ability to model both large geographic areas 

and corridors. In addition, diversion routes and signalized intersections can be modeled. In the 

case of a corridor with a freeway and a signalized arterial road running through it, the diversion 
101 

route onto the arterial could be more readily modeled using a mesoscopic simulation model. 

Mesoscopic traffic simulation models can provide knowledge of route choice behavior and are 

ideal for prediction applications, where the detailed modeling of route choice and other strategic 

driver choices are essential, but where the detailed modeling of driver interaction with the road 

network and other drivers is not needed. 

Mesoscopic simulation models are useful for simulating existing and work zone traffic 

conditions for complex work zone scenarios. Discussed below are key considerations when 

using mesoscopic simulation tools, including: 

• DTA;

• Network convergence; and

• Model development and application process.

Dynamic Traffic Assignment (DTA)

DTA models are well suited for work zone analysis due to their capability of assigning traffic 

dynamically.(28)

 DTA models are more responsive to operational factors (e.g., queuing) than 

traditional travel demand models. Also, DTA models can dynamically assign traffic over a much 

larger network than microscopic models. It helps to assess impacts of different work zone 

management strategies at the network level, not just at the corridor level.

DTA models have become a viable modeling option recently. DTA models supplement existing 

travel demand models and microscopic simulation models. Travel demand models represent the 

static regional travel demand analysis capability, whereas microscopic simulation models are 

superior for dynamic travel analysis at the corridor level. DTA models fill in the gap by enabling 

dynamic traffic to be modeled at a range of scales from the corridor level to the regional with 

expanded and unique functional capabilities enabled by the DTA methodology. 

The static traffic assignment used in travel demand models determines network traffic flows and 

conditions resulting from demand/supply interactions via route choices from travelers. 

Route choice behavior is based upon the assumption that all travelers are aimed at traveling from 

their origin to their destination with the least travel time. When each traveler succeeds in finding 

such a route, every used route has the minimum time or cost between its origin and destination; 

moreover, for each O-D pair, every route used has the same travel time. This condition is known 

as user equilibrium. Therefore, in travel demand models, trips are instantaneously on all links 

between origins to destinations (not time-dependent). In addition, static traffic assignment does 

not take into consideration congestion spillback (i.e., where congested traffic spans a sequence of 

two or more links due to downstream bottleneck). 

Unlike the static traffic assignment, DTA considers time variations in traffic flows and 

conditions, thus, attempting to reflect the reality of that traffic network. To represent these timevarying conditions in the context of user equilibrium, two concepts must be recognized: 1) in a 

dynamic approach, the user equilibrium condition of equal travel times on used routes applies 

only to travelers who are assumed to depart at the same time between the same O-D pair, which 

means the equilibrium condition is disaggregated to consecutive departure times instead of the 
102 

entire analysis period; and 2) travel times on network links vary over time based on preceding 

experienced travel times, as this will depend on when travelers arrive at the various links along a 

route. 

DTA models seek to provide a more detailed means to represent the interaction between travel 

choices, traffic flows, and time and cost measures in a temporally coherent manner (e.g., further 

improve upon the existing time-of-day static assignment approach). More specifically, DTA 

models aim to describe such time-varying network and demand interaction using a behaviorally 

sound approach. The DTA model analysis results can be used to evaluate more meaningful 

performance measures related to individual travel time and cost, as well as systemwide network 

measures for regional planning purposes. 

DTA in Microscopic Simulation Models

Currently, many microscopic models are route-based, meaning vehicles select a route at 

departure and follow that route with or without further update along the journey during 

simulation. The “one-shot” (non-iterative) assignment-simulation approach is commonly used in 

microscopic simulation models, in which vehicles departing at different times are given a route 

that is periodically updated in simulation based on instantaneous travel times, not experienced 

travel time. 

• Instantaneous Travel Time – It is a snapshot travel time measured at the time that the 

routes are generated without considering congestion during subsequent time periods. 

Such an assignment can be regarded as if travelers strictly follow some type of “pre-trip” 

route guidance.

• Experienced Travel Time – Experienced travel time is used in DTA models instead. 

The experienced travel time needs to be evaluated “after the fact,” at which point the 

traffic condition along the entire journey is revealed and experienced. In other words, 

choosing a minimal experienced travel time route at departure involves anticipation of 

future traffic condition along the journey. This anticipation is usually formed by learning 

from prior experience (e.g., try different routes). To account for this “learning” process, 

an iterative algorithmic process is needed. Such an iterative process reflects the learning 

and adjustment in route choice from one iteration to the next until the traveler cannot find 

a route with a shorter experienced travel time. In reality, the majority of travelers choose 

a route that leads to the minimal experienced travel time instead of minimal 

instantaneous travel time.

The difference between experienced and instantaneous travel times can be better illustrated by 

using Figure 14, which is a simple one-way network with four nodes and three links.(28)

 The 

stack of values represents the different times to traverse a link when departing from the upstream 

node (and entering the link) at different times. Time-varying link travel time is common during 

peak hours due to congestion buildups. As an example, the time needed to traverse link 1 is 1 

time unit when departing the upstream node at time 1, and 3 time units when departing the 

upstream node at time 5. Similarly, the travel time for link 2 is 1 and 4 time units when 

departing the link 2 upstream node at times 1 and 5, respectively.
103 

• The instantaneous travel time for the entire route at each different departure time is 

calculated by summing up the link travel time corresponding to that same departure time 

for all links comprising the route. As an example, for vehicles departing at time 1, the 

travel time is 1+1+1=3 time units; for vehicle departing at time 2, the travel time is 

1+2+3=6 time units. 

• The experienced travel time calculation accounts for the time needed for traversing one 

link, and looks up the downstream link travel time based on the time of entering that 

downstream link (assuming that traversing a node takes no time). Based on this 

approach, the travel time for the route when starting at departure time 1 should be 1 + 2 

(vehicle entering link 2 at time 2, so the link 2 travel time is 2 time units), plus 6 (vehicle 

arriving at link 3 at time 4, so the link 3 travel time is 6 time units). The experienced 

travel time is 1+2+6=9 time units.

Clearly, these two methods produce different route travel times and, likewise, differing results 

for the shortest routes. The shortest route obtained based on the instantaneous travel time 

calculation has the minimum travel time based on the “snapshot” of the link travel times 

prevailing at departure. However, because link travel times change dynamically (due to 

congestion), that route does not necessarily result in minimal experienced travel time, because 

there is no provision in this procedure to reflect the anticipation of congestion that is to occur at a 

later time down the road (e.g., congestion caused by vehicles departing later but entering the 

same link, one which the vehicle being modeled is still traversing).

Assigning vehicles with an instantaneous travel time route is not necessarily “incorrect.” The 

route choice associated with instantaneous travel time may be interpreted as: 1) travelers know 

what the shortest route is at departure through pre-trip information (e.g., 511, news or web site) 

or en-route in-vehicle information system (if the traveler is to take another route when en-route); 

or 2) from day to day, travelers do not assess the route travel time from the experience 

standpoint, but rely instead on the traveler information. In contrast, the shortest route obtained 

based on the experienced travel time calculation method will yield a time-dependent shortest 

route with minimal experienced travel time. This assumes that travelers are willing to seek 

routes that minimize their experienced travel time instead of the route that appears to be the best 

only at the departure.
104 

Figure 14. Experienced Travel Time versus Instantaneous Travel Time

(Source: Transportation Research Board, 2010.) 

Network Convergence

Almost all DTA models apply an iterative algorithm to compute an approximation to dynamic 

user equilibrium. From one iteration to the next, time-dependent route choices and link flows 

and times change as the algorithm progresses towards an equilibrium solution. At some point, 

the convergence criteria are satisfied and the algorithm terminates.

A poorly converged network may have the problem of “nonlocal impacts” – a minor change in 

the network has large impacts on flows or conditions far from the location of the change. For 

example, a minor change, such as a speed limit change on a particular link, would not be 

expected to significantly affect flows and conditions far from the link in question. DTA model 

outputs that showed such effects should be closely examined. When an apparent nonlocal 

impact exists, it is best to verify the quality of the computed convergence solution, for example, 

by resolving the DTA model with more stringent convergence criteria. 
105 

However, stricter convergence criteria lead to increased computational time. Therefore, a 

tradeoff between convergence and computational time must be made when choosing the 

appropriate convergence criteria.

Model Development and Application Process 

The typical model development and application process, which was discussed at the beginning of 

this chapter, applies to mesoscopic models as well. 

1. For mesoscopic models, typically two forms of calibration are required; they are: 

• Capacity Calibration – Factors that influence capacity calibration typically include 

maximum speeds, jam densities, number of lanes, curvature geometries, intersection 

geometries, and other minor characteristics.

• O-D Matrix Calibration – This is a more time-consuming effort as numerous 

iterations are often required to produce an O-D matrix that results in realistically 

modeled traffic. This involves utilizing an existing demand matrix and then make some 

adjustments to refine the accuracy of simulated flow compared with observed traffic 

flow.

2. The O-D matrix calibration process is illustrated in Figure 15. The mesoscopic model is 

run with the estimated time-dependent O-D matrices. With the difference between the 

simulated link counts and the field counts, an optimization tool is typically used to calibrate 

the O-D matrices, resulting in new O-D matrices. The iterations continue until the total 

deviation between the simulated and field counts is less than a user-defined threshold, or 

when the maximum number of iterations is reached. 
106 

Figure 15. O-D Matrix Calibration in Mesoscopic Models

Mesoscopic Simulation Model Development and Application – Checklist

This subsection provides a high-level synthesis combining the concepts presented above on 

model development and application for mesoscopic simulation work zone analysis. Please note 

that not all the items listed below are required for all work zone analysis.

• Determine Study Objectives – The goals should directly correlate to the work zone 

safety, mobility, constructability concerns, and needs of the agency. The performance 

objectives then determine the selection of work zone alternatives or strategies that are 

designed to meet the established goals. 

• Identify Needed Coordination Tasks – Determine what coordination must occur 

between other projects, agencies, facilities, and events. This input may require additional 

modeling to quantify impacts, such as when work is to occur on an arterial that contains 

mass transit.

• Determine Study Network – Determine the limits of the study network. The network 

boundary should be able to accommodate queues under the worst scenario, including the 

work zone conditions. The network also should include potential detour routes, both 

signed and unsigned. 

• Determine Analysis Time Period – Determine the analysis time period. If needed, 

create different time-of-day models, such as a.m. peak, off-peak, and p.m. peak. For 
107 

longer duration work zones, it is recommended that interim analysis be performed to 

better reflect varying capacities, if resources and data are available. Each analysis period 

should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period. 

• Confirm Analysis Method – Verify that the analysis tool(s) selected have sufficient 

capabilities to satisfy the analysis needs with regards to work zone characteristics, 

transportation management plan, data, resources, and performance measures. 

• Select MOEs and Thresholds – The MOEs or thresholds selected should be customized 

to the agency’s goals and project characteristics. The MOEs and thresholds set a 

minimum performance standard that the alternatives must meet or exceed.

• Collect and Analyze Data – Collect data that is needed to model work zone operations. 

Depending on the complexity of the study network and of the analysis needed, different 

types of data may be required. Data may broadly be classified into four categories: 

general, geometric, traffic, and construction.

• Identify Work Zone Alternatives – Identify potential work zone alternatives for evaluation. 

• Conduct Fatal Flaw Analysis – Perform a fatal flaw analysis on each selected 

alternative to determine the viability of the alternative for further analysis.

• Determine Work Zone Capacity – Roadway capacity in a work zone is reduced due to 

a variety of factors. It is strongly recommended that local data at work zones be collected 

and used. Check with the appropriate stakeholder agencies as some of them may have 

their own guidelines on work zone capacity and related defaults. In the absence of better 

local data, the analyst may use the proposed defaults as the starting point and make 

modifications accordingly to reflect local work zone conditions. 

• Develop Existing Conditions Model – Develop an existing conditions model with the 

correct geometry, traffic demands, capacities, and traffic controls. Error checking should 

be performed to identify and correct the model coding errors. 

• Calibrate Existing Conditions Model – Calibrate the model by adjusting a set of useradjustable parameters in the analysis tool to better match specific local conditions. For 

mesoscopic models, typically two forms of calibration are required, including capacity 

calibration and O-D matrix calibration.

• Verify Network Convergence – The majority of mesoscopic simulation tools use DTA 

to compute an approximation to dynamic user equilibrium. Once done, verify the quality 

of the computed convergence solution to see if it meets the appropriate convergence criteria. 

• Develop Work Zone Base Conditions Model – Develop a work zone base conditions 

model by modifying the calibrated existing conditions model to represent work zone 

conditions. 

• Calibrate Work Zone Base Conditions Model – Work zone model calibration includes 

the calibration of both work zone capacity and performance measures. 

• Conduct Alternative Analysis – The alternatives analysis may involve the forecasting of 

the future demand and the testing of various project alternatives against the base case. 

The analyst needs to run the model for each alternative, review the output, compare 

against any performance requirements, extract relevant statistics, and perform various 

analyses of the results.

• Recommend Best Alternative – Utilizing a decision framework, the best overall 

alternative is selected.
108 

• Document Findings – The analytical results are summarized in a final report and the 

analytical approaches are documented in a technical document.

4.7 USING MICROSCOPIC ANALYSIS

Microscopic simulation tools simulate the movement of individual vehicles based on carfollowing and lane-changing models. Vehicles enter a transportation network using a statistical 

distribution of arrivals and are tracked through the network over brief time intervals (e.g., one 

second or a fraction of a second). 

Microscopic simulation models are extensively used in a wide range of applications, including 

evacuation planning and work zone impact analysis. They are effective in evaluating a wide 

range of scenarios, including heavily congested conditions, complex geometric 

configurations, and system-level impacts of proposed transportation improvements that are 

beyond the limitations of other model types. They also are useful in analyzing key bottlenecks 

on roadway segments and corridors where the movement of each individual vehicle needs to be 

represented to better understand the impact on roadway conditions. Many transportation 

agencies currently use microscopic models in conjunction with other analysis tools such as travel 

demand models and mesoscopic models to better understand the impact of roadway geometry 

modifications on carrying capacity and level of service.

Some examples of microscopic traffic simulation models are listed below. For a complete list, 

please refer to Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer.

(8)

• AIMSUN2 (Advanced Interactive Microscopic Simulator for Urban and Nonurban 

Networks) – http://www.tss-bcn.com/aimsun.html; 

• CORSIM/TSIS (Traffic Software Integrated System) –

http://www.mctrans.ce.ufl.edu/featured/tsis/version5/corsim.htm; 

• PARAMICS – http://www.paramics-online.com; 

• TRANSMODELER – http://www.caliper.com/transmodeler/default.htm; and

• VISSIM – http://www.ptvamerica.com/support/vissim/. 

Level of Effort (LOE)

The Guidance on the Level of Effort Required to Conduct Traffic Analysis Using 

Microsimulation evaluates four case studies of microsimulation projects of various sizes and 

their level of effort estimates.(25) Table 35 summarizes the labor-hour estimates expected for 

these case studies. Additionally, Figure 16 shows the typical percentage of labor hours required 

for various project tasks. These examples are meant to provide analysts and modeling managers 

with a frame of reference on the level of effort required to complete a microsimulation analysis. 

These estimates serve as a point of reference, not as absolute estimates that should be applied to 

all projects.
109 

Table 35. Level of Effort for Various Microsimulation Projects

Model Size and Hours

Task Small Medium 1 Medium 2 Large

Data Collection 40 262 80 2,000

Base Model and Calibration 140 597 560 2,500

Alternatives Analysis 280 1,056 640 2,520

Documentation/Presentations 40 62 240 1,850

Project Management 40 84 80 1,210

Total 540 2,061 1,600 10,080

(Source: Cambridge Systematics, Inc., 2012.)

Figure 16. Expected Level of Effort by Project Tasks for Microsimulation Projects

(Source: Cambridge Systematics, Inc., 2012.)

Data Collection 

The precise input data required by a microsimulation model will vary by software and the 

specific modeling application as defined by the study objectives and scope. Most 

microsimulation studies require the following types of input data besides the general data 

identified at the beginning of this chapter.

Base Model 

and 

Calibration

27%

Data Collection

16%

Alternatives 

Analysis

31%

Documentation 

15%

Project

Management

10%
110 

Existing Models 

Check if the local agency maintains a database of microsimulation model files. A field visit may 

need to be performed to verify the accuracy of the model input data. 

Demands (Entry Volumes, Turning Volumes, O-D Table) 

The basic travel demand data required for most models consist of entry volumes (traffic entering 

the study area) and traffic at ramps within the study area. Some models require one or more 

vehicular O-D table(s), which enable the modeling of route diversions. Procedures exist in many 

demand modeling software and some microsimulation software for estimating O-D tables from 

traffic counts.

Calibration Data (Traffic Counts and Performance Data, such as Speed and Queues)

Calibration data consist of measures of capacity, traffic counts, and measures of system 

performance such as travel times, speeds, delays, and queues.

Other Data

In addition to the above basic input data, microsimulation models also require data on vehicle 

and driver characteristics (vehicle length, maximum acceleration rate, driver aggressiveness, 

etc.). Because these data can be difficult to measure in the field, it is often supplied with the 

software in the form of various default values. 

Each microsimulation model also will require various control parameters that specify how the 

model conducts the simulation. The user’s guide for the specific simulation software should be 

consulted for a complete list of input requirements. 

Capacity in Modeling Tools 

At the beginning of this chapter, details on work zone lane capacity were presented. It is 

strongly recommended that local data at work zones be collected and used, as driver 

characteristics vary from region to region and capacity may vary from location to location. In 

the absence of better local data, the analyst may use the defaults as the starting point and 

make modifications accordingly to reflect local work zone conditions.

Once the work zone capacity is determined, the next step is to implement it in the 

microsimulation model. For microsimulation tools, however, there is no direct input for 

“capacity.” Instead, each microsimulation software program has its own set of parameters that 

affect capacity, depending on the specific car-following and lane-changing logic implemented in 

the software. Table 36 lists major parameters that the analyst may adjust for several commonly 

used microsimulation software programs.
111 

Similar to the input, there is no direct output in microsimulation models for “capacity.” 

Instead, the number of vehicles that pass a given point can be generated. Thus, the analyst must 

manipulate the input traffic demand as necessary to create a queue upstream of the target section 

to be calibrated so that the model will report the maximum flow rate (i.e., capacity) through the 

area. After the capacity is calibrated, the analyst will change the input traffic demand back.

Table 36. Parameters to Adjust for Work Zone Analysis in 

Common Microsimulation Software 

Microsimulation 

Software

Parameters

Speed Car-Following

Lane-Changing

(Before Approaching

Work Zone)

AIMSUN • Maximum speed

• Turn speed at 

intersections

• Lane speed limit 

• Reaction time factor

• Minimum headways

• Maximum delay time to 

remove vehicles

• Look ahead distance

• Lane-changing model

• Lane selection model

• Global reaction time and 

local reaction time factor

CORSIM • Speed limit • Car-following sensitivity

• Headway factor by 

vehicle type

• Rubbernecking factor

• Time to complete lanechange maneuver

• Percentage of drivers 

yielding the right-of-way 

to lane-changing vehicles

• Gap acceptance

PARAMICS • Speed limit

• Roadway type

• Headway factor

• Reaction factor

• Lane choice; Signposting

VISSIM • Reduced speed area

• Desired speed 

decision

• Standstill distance (CC0)

• Headway time (CC1)

• Following variation 

(CC2)

• Look-back distance

• Safety distance reduction 

factor

• Waiting time before 

diffusion

• Maximum deceleration

TRANSMODELER • Lane width factor

• Tunnel factor

• Max speed difference

• Speed limit

• Desired speed model

• Headway thresholds

• Look ahead

• Critical distance

Key Considerations Regarding Use for Analysis 

Microscopic simulation models are useful for simulating existing and work zone traffic 

conditions for complex work zone scenarios. Discussed below are key considerations when 

using microscopic simulation tools, including: 

• Identifying the chokepoint; 

• Work zone taper simulation; 

• Model development and application process; 

• Congested versus noncongested conditions; 

• Initialization period;
112 

• Number of runs; and 

• Avoiding bias in the results. 

Identifying the Chokepoint 

For most work zones, the chokepoint will be the taper.(22) However, if there is an on-ramp near 

the taper, either upstream or downstream, the chokepoint could be within the on-ramp merge 

area. If the chokepoint is in an on-ramp merge area, specific care must be exercised in setting or 

proportioning on-ramp flow rates. Being able to identify a single chokepoint can simplify the 

analysis.

Work Zone Taper Simulation 

A work zone taper is typically modeled as a “lane drop” in most microsimulation tools. A model 

can vary the amount of space ahead that drivers have to react to a lane drop, but the physical 

length of the taper cannot be specifically entered as a variable.

Model Development and Application Process 

The typical model development and application process discussed at the beginning of this 

chapter applies to microscopic models as well.

Each microsimulation software package has a set of user-adjustable parameters that enable the 

analyst to calibrate the software to better match specific local conditions. For details on the 

microsimulation model calibration process, please refer to the Traffic Analysis Toolbox Volume 

III: Guidelines for Applying Traffic Microsimulation Software.

(29) 

Congested versus Noncongested Conditions 

The calibration of a congested network follows the same procedure as that of noncongested ones. 

However, the analyst may need to pay special attention to the following issues: 

• Network Boundary – Ideally, the calibrated existing conditions model (preconstruction, 

or “no-build”) should have its network boundary established to accommodate queues 

even under the worst scenario, including the work zone conditions. However, this may 

not be always the case. Thus, under the congested conditions, the analyst may need to 

extend the network further upstream so that the ends of all queues are within the 

simulation network. 

• Temporal Boundary – The analyst also may consider expanding the temporal 

boundaries (i.e., the duration of the simulated time period), so that at the beginning and 

the end of the simulation, there are no significant queues within the network. 

• Merging/Weaving Segment – Because of the lack of congestion, the microsimulation 

model under the noncongested conditions may be calibrated using the default driving 

parameters on merging/weaving segments. However, under-congested conditions, the 

segments with heavy merging or weaving activities may need special attention. Local 
113 

lane-changing behavior parameters may need to be adjusted to reflect realistic driving 

conditions in the field. 

Initialization Period

Simulation model runs usually start with zero vehicles on the network. If the simulation output 

is being compared to field measurements (as in calibration), the artificial period where the 

simulation model starts out with zero vehicles (the warm-up period) must be excluded from the 

reported statistics for system performance. Some software programs will do this automatically. 

For others, the warm-up period must be computed offline by the analyst. For details on how to 

identify the warm-up period, please refer to the Traffic Analysis Toolbox Volume III: Guidelines 

for Applying Traffic Microsimulation Software.

(29) 

Number of Runs 

Random numbers are used in microsimulation models to generate vehicles, decide their behavior, 

and select their destination and route as they move through the network. A single simulation run 

cannot be expected to represent specific field conditions. It is necessary to run the model several 

times with different random number seeds to get the necessary output to determine mean, 

minimum, maximum values, and standard deviation. The multiple runs then need to be postprocessed to obtain the necessary output statistics. 

The determination of the number of simulation runs is an iterative process. For more 

information, please refer to the Traffic Analysis Toolbox Volume III: Guidelines for Applying 

Traffic Microsimulation Software.

(29) 

Avoiding Bias in the Results

The simulation geographic and temporal limits should be sufficient to include all congestion 

related to the base case and all of the alternatives. Otherwise, the model will not measure all of 

the congestion associated with an alternative, thus potentially causing the analyst to underreport 

the benefits of an alternative.

Microscopic Simulation Model Development and Application – Checklist

This subsection provides a high-level synthesis combining the concepts presented above on 

model development and application for microsimulation model work zone traffic analysis. 

Please note that not all the items listed below are required for all work zone analysis.

• Determine Study Objectives – The goals should directly correlate to the work zone 

safety, mobility, constructability concerns, and needs of the agency. The performance 

objectives then determine the selection of work zone alternatives or strategies that are 

designed to meet the established goals. 

• Identify Needed Coordination Tasks – Determine what coordination must occur 

between other projects, agencies, facilities, and events. This input may require additional 
114 

modeling to quantify impacts, such as when work is to occur on an arterial that contains 

mass transit.

• Determine Study Network – Determine the limits of the study network. The network 

boundary should be able to accommodate queues under the worst scenario, including the 

work zone conditions. The network also should include potential detour routes, both 

signed and unsigned. 

• Determine Analysis Time Period – Determine the analysis time period. If needed, 

create different time-of-day models, such as a.m. peak, off-peak, and p.m. peak. For 

longer duration work zones, it is recommended that interim analysis be performed to 

better reflect varying capacities, if resources and data are available. Each analysis period 

should be long enough to include no queue at the beginning, queue buildup, and queue 

dissipation at the end of the analysis period. 

• Confirm Analysis Method – Verify that the analysis tool(s) selected have sufficient 

capabilities to satisfy the analysis needs with regards to work zone characteristics, 

transportation management plan, data, resources, and performance measures. 

• Select MOEs and Thresholds – The MOEs or thresholds selected will be customized to 

the agency’s goals and project characteristics. The MOEs and thresholds set a minimum 

performance standard that the alternatives must meet or exceed.

• Collect and Analyze Data – Collect data that is needed to model work zone operations. 

Depending on the complexity of the study network and of the analysis needed, different 

types of data may be required. Data may broadly be classified into four categories: 

general, geometric, traffic, and construction.

• Identify Work Zone Alternatives – Identify potential work zone alternatives for 

evaluation. 

• Conduct Fatal Flaw Analysis – Perform a fatal flaw analysis on each selected 

alternative to determine the viability of the alternative for further analysis.

• Determine Work Zone Capacity – Roadway capacity in a work zone is reduced due to 

a variety of factors. It is strongly recommended that local data at work zones be collected 

and used. Check with the appropriate stakeholder agencies as some of them may have 

their own guidelines on work zone capacity and related defaults. In the absence of better 

local data, the analyst may use the proposed defaults as the starting point and make 

modifications accordingly to reflect local work zone conditions. 

• Determine Initialization Period – Simulation model runs usually start with zero 

vehicles on the network. Therefore, an initialization period (warm-up period), where the 

simulation model starts out with zero vehicles needs to be determined and used in the 

simulation models.

• Develop Existing Conditions Model – Develop an existing conditions model with the 

correct geometry, traffic demands, capacities, and traffic controls.

• Check Errors – Error checking should be performed to identify and correct the model 

coding errors. The error checking involves the reviews of software errors, network 

coding, demand data, and simulation animation. 

• Calibrate Existing Conditions Model – Calibrate the model by adjusting a set of useradjustable parameters in the analysis tool to better match specific local conditions.

• Verify Network Convergence – If dynamic assignment is used, the network 

convergence needs to be verified to see if it meets the appropriate convergence criteria.
115 

• Determine the Number of Runs – A single simulation run cannot be expected to 

represent specific field conditions. It is necessary to run the model several times with 

different random number seeds to get the necessary output to determine mean, minimum, 

maximum values, and standard deviation. 

• Develop Work Zone Base Conditions Model – Develop a work zone base conditions 

model by modifying the calibrated existing conditions model to represent work zone 

conditions. 

• Simulate Work Zone Taper – A work zone taper is typically modeled as a “lane drop” 

in most microsimulation tools. A model can vary the amount of space ahead that drivers 

have to react to a lane drop, but the physical length of the taper cannot be specifically 

entered as a variable.

• Calibrate Work Zone Base Conditions Model – Work zone model calibration includes 

the calibration of both work zone capacity and performance measures. 

• Conduct Alternatives Analysis – The alternatives analysis may involve the forecasting 

of the future demand and the testing of various project alternatives against the base case. 

The analyst needs to run the model for each alternative, review the output, compare 

against any performance requirements, extract relevant statistics, and perform various 

analyses of the results.

• Recommend Best Alternative – Utilizing a decision framework, the best overall 

alternative is selected.

• Document Findings – The analytical results are summarized in a final report and the 

analytical approaches are documented in a technical document.
116 

5.0 ESTABLISHING AN MOTAA DECISION FRAMEWORK 

This chapter will provide guidance on developing and applying an Maintenance of Traffic 

Alternatives Analysis (MOTAA) decision framework. The chapter is organized as follows: 

• The first section will provide an overview of the decision-making process within a work 

zone MOTAA. It also will describe the factors that shape the decision-making process, 

as well as the traditional decision-making frameworks often applied in work zone traffic 

analysis.

• The second section will highlight several analysis methodologies that can aid in the 

prioritization of criteria, and factors and/or thresholds used to evaluate and compare work 

zone alternatives.

• The third section will present evaluation methodologies that aid decision-makers in 

identifying the optimum alternative or combination of strategies that will best fit the 

project.

• The fourth section will highlight several decision-making tools that help automate the 

analysis needed to choose among different work zone alternatives. 

5.1 OVERVIEW OF AN MOTAA DECISION FRAMEWORK

The Decision Framework within an MOTAA Process

The MOTAA decision-making methods and evaluation framework are typically applied after the 

agency has developed their set of potential work zone alternatives. The alternatives are 

generated during the planning process after the agency has established its set of goals and 

objectives. The list of alternatives is further refined once the agency has narrowed down the 

alternatives to only those feasible, either through a fatal flaw or other type of analysis. The 

decision-making process then occurs after the agency has evaluated the performance of the 

project, along with their potential alternatives using a selected analysis tool. After the modeling 

analysis, a set of performance measures and other factors, such as those described in Chapter 6, 

will be used to form the criteria that determine how well the alternative meets the goals and 

objectives of the project. How these criteria or measures are used in determining the 

recommended alternative will be a function of the selected decision framework. 

Traditional Decision-Making Framework

While no standardized decision-making framework has been established in a typical MOTAA 

process, the most common decision-framework used to date typically follows rule-based 

reasoning. Rule-based reasoning uses “if-then-else” rule statements. For instance, in an 

MOTAA process, an “if” statement may evaluate whether an alternative meets a particular 

criterion. If the alternative does meet the criterion, the “then” statement may indicate that the 

alternative should be chosen. If the alternative does not meet the criterion, the “else” statement 

may specify to reject the alternative, suggest revising the alternative or choose the alternative 

with the least impact. An example of a typical rule-based reasoning for a hypothetical work zone 

scenario is illustrated in Figure 17. 
117 

 

Model Outputs

Delay, Queues, 

Volumes, and 

Speeds

Impact Analysis

Queue Length

Finalize Traffic 

Management 

Plan

Project 

Alternative

Recommend 

Alternative

Does the queue 

length meet the 

threshold?

“THEN” “IF”

“ELSE”

YES

NO

Figure 17. Example of a Traditional Decision-Framework

The traditional MOTAA decision-making process begins with the formulation of work zone 

alternatives – the development of different work zone configurations and strategies. Afterwards, 

these alternatives are analyzed based on performance measures or standards set by the agency. If 

the alternatives do not meet the standards, they will either be revised or replaced by other 

alternatives that comply with the set performance measures. The alternative that best meets the 

standard(s) or has the least negative impacts is selected.

Decision Methodologies

In the typical decision frameworks, the decision criterion used is often dependent upon one type 

of measure – a mobility, safety, or cost measure. While this method may address one of the 

objectives of the project, it does not account for a multidimensional way of comparing 

alternatives. The methodologies discussed in this section present additional analysis methods 

and tools that account for how multiple factors can form a criteria for evaluating and choosing 

among alternatives. The following decision-making frameworks serve as potential options for an 
118 

agency to consider. However, in some cases, an agency may be required to follow a standard 

approach established by their organization. 

In these types of decision-making frameworks, there are often two levels of analysis. The first 

level typically applies factor prioritization analysis methods. These analysis methods can 

prioritize and/or assign weights to the factors that are used to compare and evaluate 

potential work zone alternatives. The second level of analysis typically employs scoring 

and/or evaluation matrices for recommending an alternative(s). These methodologies provide 

the structure for the decision framework. They determine how the prioritized and/or weighted 

criterion determined in the factor prioritization procedures can be used to evaluate and choose 

among the alternatives.

Below is an overview of the factor prioritization methodologies featured in this chapter. 

Further information, including example work zone applications of such methodologies, is 

provided in Section 5.2. 

• Delphi Method – This method offers a methodology for identifying and prioritizing 

factors or criteria through surveying a panel of experts, who then works towards reaching 

a consensus on the priority of the factors based on their level of importance to the project. 

• Factor Analysis – This analysis is a general scientific method for analyzing data by 

uncovering order, patterns, and regularity in the data.(30) The overview focuses on its use 

for prioritizing and weighting criteria.

• Ranking Analysis – The ranking method calculates normalized criteria weights based on 

how a panel of surveyed or polled participants ranks the factors based on their level of 

importance.

• Ratio Analysis – The analysis calculates normalized criteria weights based on surveyed 

participants’ responses on how criteria elements measure up against each other.

• Paired Comparison Analysis – In a paired comparison analysis, criteria are compared 

against each other one pair at a time. A criterion’s score or weight is based on the 

number of times it is preferred over others. 

• 100-Point Distribution – In a 100-point distribution, criteria weights are decided based 

on how an individual or group distributes 100 points amongst the factors/criteria.

An overview of the work zone alternatives evaluation frameworks featured in this chapter are 

listed below. The factor prioritization methodologies previously identified are often incorporated 

within these evaluation frameworks to guide how the factors or criteria are used in 

recommending an alternative(s). Further information on how to apply these methodologies 

within an MOTAA decision framework is provided in Section 5.3. 

• Multi-Criteria Decision-Making Models (MCDM) – Mathematical methodologies that 

evaluate and compare the utility or relevancy of alternatives to the project goals and 

objectives. There are two MCDM techniques: Simple Additive Weighting (SAW) and 

Analytical Hierarchy Process (AHP), which are featured in this chapter. 
119 

• Kepner Tregoe (KT) Method – Decision-making that features a step-by-step approach, 

evaluating each alternative by its impacts, risks, and opportunities.

• Benefit/Cost Analysis – This compares the sum of all costs with the sum of all benefits 

associated with an alternative. The benefit/cost ratio can be used to compare alternatives.

Choosing a Decision-Making Framework

There are several general considerations an agency should consider in determining the 

framework or analysis method best suited for choosing among different work zone alternatives. 

Sections 5.2 and 5.3 discuss these considerations in further details, as well as addressing many of 

the methodologies’ limitations and strengths. Some of these considerations include the 

following: 

• Level of Data Collection Effort – Several of the methodologies may require gathering 

data from case studies, extracting performance measures from the analysis tools, or 

polling/surveying people in order to develop the criteria for choosing amongst 

alternatives. The agency needs to be aware of the data collection effort needed to 

conduct a particular decision-making methodology. 

• The Complexity of the Analysis – The analysis methodology may range from simple to 

more complex mathematical models. The agency must take into consideration whether 

or not they have the appropriate tools or expertise to perform a particular decisionmaking methodology. 

• Time – Some of the methods are more time-consuming than others. An agency should 

choose the methodology that best fits their time line.

• Project Complexity – The complexity of the project may impact which methodology is 

best suited for the agency. Since some methodologies may be more resource intensive 

than others, it may not make sense to use highly complex decision-making frameworks 

for simple, short-term projects.

5.2 APPROACHES TO FACTOR PRIORITIZATION

This section provides further detail on the use of the factor prioritization methodologies for an 

MOTAA application. As previously mentioned, analysis methods can aid agencies in 

prioritizing their selected factors by level of importance to the project, as well as in creating 

weighted criteria. The weighted factors can then be integrated into a weighting/scoring method 

to choose among alternatives.

At this stage in the process, the agency should have a list of factors, both quantitative and 

qualitative, that have relevancy to the project characteristics, goals, and objectives. These factors 

should indicate how well the alternative can meet the goals and objectives established by the 

agency for the work zone project. Some example criteria include level of delay at the work zone, 

average speeds, accident rates, and the work zone alternative’s impacts to operating and 

maintenance costs.
120 

Various methodologies are described below and each is structured to contain the following: 

• An overview of the approach; 

• Pros and cons of the methodology; and 

• An example application.

A summary table (Table 37) highlights the particular features and capabilities of these 

methodologies, their strengths, and weaknesses. 

Table 37. Factor Prioritization Methodologies Summary

Methods/

Considerations

Project 

Complexity 

Suited For

Analysis 

Complexity

Level of 

Data Needs

Development 

Time

Analysis 

Time

Potential 

for Bias/

Conflict

Delphi Medium-High      

Factor Analysis Medium-High      

Ranking Low-Medium      

Ratio Low-Medium      

Paired Comparison 

Analysis

Low      

100-Point Distribution Low      

 Low  Medium  High

The Delphi Method

Approach Overview

The Delphi Method offers a methodology for identifying the factors or criteria that can be used 

to screen alternatives. The steps for applying the Delphi Method are: 

• Step 1 – The careful selection of panel experts from disciplines most relevant to the 

project and its goals and objectives. 

• Step 2 – Distribute questionnaires that will narrow down the attributes, and determine the 

level of importance and average utility values of each of the attributes. A factor 

prioritization method can be used to determine the level of significance of each attribute 

using the survey results. 

• Step 3 – Repeat Step 2 as necessary.

• Step 4 – After all rounds of the questionnaire have been completed, the end result is a set 

of weighted attributes to be used in ranking the alternatives. 

The duration of this approach, including the number of questionnaire rounds and the number of 

experts comprising the panel, is dependent on available resources and other project 

characteristics. Table 38 lists some general pros and cons of the Delphi Method. 
121 

Table 38. Pros and Cons of the Delphi Method

Pros Cons

• Finds consensus among differing opinions

• Less biased

• Allows participants to be anonymous

• Well selected expert panel can provide broad 

analytical perspectives on impacts

• Flexibility and applicability to various issues

• Judgments are still made by a select group and not 

necessarily representative

• Technique can be time-consuming 

Example Application 

The following presents a hypothetical case study for the application of the Delphi Method in 

MOTAA. For ease of presentation of this technique, the example focuses on the analysis of two 

broad project goals – improvement of standard construction strategies/methodologies and 

minimizing traffic, environmental, and economic impacts.

The agency in this case has developed potential alternatives. Additionally, they have developed 

a list of potential factors, labeled “objectives” that can be used to screen the alternatives. In 

order to determine which factors have the most significance to the project goals, the agency 

applies the Delphi Method (see Table 39 for Delphi structure) through the following steps: 

• Step 1 – The agency assembles an expert panel. The panel participants in this case 

should have experience in work zone management, traffic management, and/or work 

zone relevant research and have an understanding of the agency’s goals.

• Step 2 – The agency assembles the first round of three questionnaires. This first round 

asks the panel to list five objectives that are of relevance to the project and its goals. 

After the results from the first round are turned in, the evaluator(s) can choose to reduce 

the list in preparation for the next round. 

• Step 3 – In the second round of questionnaires, the panel is asked to rank the refined list 

of objectives by desirability and importance to the project. Table 40 lists the reference 

scale and definitions for desirability and importance. In this example, the higher the 

value, the more desirable or important the objective is to the project. After the panel 

responses for the second round have been turned in, the evaluator(s) can then average the 

group scores for both desirability and importance. Table 41 shows example results.

• Step 4 – The evaluator determines a threshold for narrowing down the list of objectives. 

In this example, objectives that scored lower than 3.0 for either importance or desirability 

was eliminated for the next round. Objective that are bold in Table 41 were eliminated in 

the third round. 

• Step 5 – For the third round, the evaluator revises the motivations for the questionnaire. 

The panel participants are again asked to rank the objectives by level of desirability and 

importance. However, in this round they rank the objectives according to specified 

categories such as project management, safety, mobility, and environmental impacts as 

shown in Table 42. 

• Step 6 – After the responses from the third round have been collected, the evaluator can 

then create a weighting scale based on the results. In Table 42, the evaluator determined 

the weights by calculating the objective’s score as a percentage of the total score for that 

category.
122 

Table 39. Delphi Technique Example

Goals: Improve standard construction strategies and methodologies; 

minimize traffic, environmental, and economic impacts

Round 1 – Generating List 

of Objectives

Round 2 – Desirability and 

Importance Rating Round 3 – Final Ratings

Database for 

Questionnaire

Panel feedback Panel feedback Panel feedback

Duration 1-3 weeks 1-3 weeks 1-3 weeks

Number of Experts 5-30 5-30 5-30

Findings Panel respondents list five

objectives most relevant to 

project goals.

Rating the narrowed down 

objectives by desirability and

importance.

Narrowed down objectives 

will be organized into 

specific categories.

Analysis of Findings List of potential objectives. 

Evaluator may choose to 

reduce the number of 

objectives considered for the 

next round.

Determine average 

desirability and importance 

score by averaging panel 

response. Narrow down to 

highest scoring.

The refined list of objectives 

will be re-rated based on 

how they fit/address the 

specific categories.

Table 40. Delphi Example Scale Reference

Score Importance Definition Desirability Definition

1 Most 

Unimportant

No relevance; no priority Highly undesirable No benefit

2 Unimportant Insignificantly relevant; low 

priority

Undesirable Costs or negative effects 

outweigh benefits/positives

3 Moderately 

Important

May be relevant to project 

goals; third order priority

Neither desirable or 

undesirable

Equal benefits and costs

4 Important Is relevant to the project 

goals; second order priority

Desirable Positive effect with minimum 

negative effects or costs

5 Very important Most relevant to project 

goals; first order priority

Highly desirable Positive effect or little to no 

negative effects or costs

Table 41. Delphi Example – Round 2 Questionnaire

Objectives

Group Score Average

Desirability Importance

Reduce construction duration 3.56 3.89

Improve standard construction operations 2.71 2.66

Optimal use of resources 4.23 3.98

Reduce capital costs 4.58 4.88

Reduce operations and maintenance cost over life of project 4.78 4.88

Reduction/improvement over typical work zone delay 4.88 4.93

Reduce incident rate 4.55 4.72

Improve travel information dissemination 2.51 2.5

Maintain peak-period congestion to preconstruction level 3.11 3.47

Reduce typical/expected work zone area queue length 3.9 3.82

Air Quality 2.03 2.33

Noise pollution reduction 3.78 3.81

Fuel consumption reduction 1.43 1.23

Reduce impacts to local businesses 3.67 3.61

Reduce impacts to neighborhoods 3.91 3.83

Lengthen life of structures 2.87 2.65

Reduce current work zone accident rate (construction workers) 2.13 2.22

Reduce accident severity 4.07 4.19
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Table 42. Delphi Example – Round 3 

Desirability Weight Feasibility Weight

Project Management Efficiency

Reduce construction duration 4.89 29% 4.65 28%

Optimal use of resources 4.1 24% 4.21 26%

Reduce capital costs 3.97 24% 3.85 23%

Reduce operations and maintenance cost over life of project 3.88 23% 3.78 23%

Subtotal 16.84 100% 16.49 100%

Traffic Condition and Performance

Reduction/improvement over typical work zone delay 4.23 36% 4.17 36%

Maintain peak-period congestion to preconstruction level 3.67 31% 3.59 31%

Reduce typical/expected work zone area queue length 3.84 33% 3.89 33%

Subtotal 11.74 100% 11.65 100%

Environmental/Community

Noise pollution reduction 4.23 36% 4.17 36%

Reduce impacts to local businesses 3.67 31% 3.59 31%

Reduce impacts to neighborhoods 3.84 33% 3.89 33%

Subtotal 11.74 100% 11.65 100%

Safety

Reduce incident rate 4.68 55% 4.71 55%

Reduce accident severity 3.89 45% 3.78 45%

Subtotal 8.57 100% 8.49 100%

Findings from the final round will enable the evaluator(s) to identify the optimum list of factors 

that should be used to screen the work zone alternatives. It also will guide the evaluator(s) into 

determining weights that could be assigned to each factor. 

Factor Analysis 

Approach Overview

Factor analysis is a statistical approach that can be used to analyze interrelationships among 

variables and to explain these variables in terms of their common underlying dimensions, also 

known as factors. Factor analysis has several applications. For the purpose of factor 

prioritization, factor analysis can be used for the following: 

• Determine Factor Groupings – Factor analysis can be used to determine 

groups/categories amongst potential criteria elements. For example, delay reductions, 

queue lengths, and throughput are interrelated through a factor grouping that could be 

called “Traffic Performance Indicators.” 

• Consolidate Factors – Factor analysis can be used to identify the criteria elements that 

may be insignificant in terms of importance to the project. It also can identify those 

factors that may be redundant.

• Prioritization Based on Level of Importance – Factor analysis results can determine 

the level of significance or importance of each criteria element or variable.

• Generate Factor Scores – Factor analysis can be used to generate factor scores to 

compare and select the recommended alternative.
124 

Two types of factor analysis include:(31) 

1. Exploratory factor analysis (EFA) attempts to discover the nature of the constructs 

influencing a set of responses; and 

2. Confirmatory factor analysis (CFA) tests whether a specified set of constructs are 

influencing responses in a predicted way.

EFA is better for determining the number of common factors influencing a set of measures as 

well as for determining the strength of the relationship between the factor and variables. CFA is 

primarily used to determine the ability of a predetermined factor model to fit an observed set of 

data. Therefore, the EFA method is better for determining a weighting scale for different 

variables.

The following provides an overview of the basic steps involved in applying the EFA. However, 

for more information on the application of factor analysis, there is a major resource in the field of 

multivariate statistical analysis.(32) In addition, several of the steps in an EFA can be more easily 

applied using statistical software such as SAS or SPSS. For information on using SAS for factor 

analysis applications, one helpful resource is by Hatcher (1994).(33) 

The following lists the steps typically involved in an EFA: 

1. Conduct Data Collection – Determine the variables or measures to include from a factor 

analysis. Data collection can come from case studies and best practices, as well as 

variables determined by a panel of experts, surveys, and questionnaires. 

2. Generate the Correlation Matrix – Generates the correlations between variables (or 

criteria elements).

3. Select the Number of Factors for Inclusion – Determine the optimal number of factors to 

include using various methods that may use eigenvalue thresholds to determine which 

factors to include or exclude.

4. Extract Initial Factor Solution – Various methods can be used, though this is typically 

done through a statistical program. 

5. Conduct Rotation – There are two major categories of rotations, orthogonal rotations, 

which produce uncorrelated factors, and oblique rotations, which produce correlated 

factors. One of the more common orthogonal rotations is Varimax.

6. Extract Factor Matrix and Interpret Results – A factor matrix is produced after the 

rotation step. This matrix presents values called factor loadings that indicate the strength of 

interrelatedness or relationships between the various factors. At this stage, the analyst can 

identify or define the factor groupings. They also can eliminate variables that do not meet a 

factor loading threshold, since this can indicate their lack of relationship to the other criteria 

elements.

7. Construct Scales or Factor Scores to Use in Further Analysis – There are several 

methods that can be applied to generate factor scores that vary in levels of complexity. 

Many come standard in statistical software such as SAS or SPSS and utilize the more 

complex methods such as regression, Bartlett, and Anderson-Rubin methods.(34)
125 

Table 43. Pros and Cons of Factor Analysis 

Pros Cons

• Can reduce the number of variables/attributes used 

for screening to those most relevant

• Can be both objective and subjective

• Can identify how variables are related to each other

• Identify additional insight into the variables and 

their impacts that may not be apparent with other 

factor prioritization methodologies

• The utility of the analysis tool depends on the 

evaluators ability to collect sufficient data on the 

attributes

• The analysis may require more statistical 

knowledge or background

Example Application 

In this example, an agency wants to refine the criteria elements that will form the basis of their 

work zone alternative decision framework. They follow the typical steps of an EFA using 

statistical software to run the factor analysis.

• Step 1 – The agency conducts a review of work zone literature, best practices, and case 

study results to develop a set of considerations and impacts relevant to their work zone 

project. For the purpose of this example, this initial set of considerations will be termed 

as the potential variables, shown on Table 44. 

Table 44. List of Potential Variables 

Potential Variables

• Reduce construction duration

• Improve construction operations

• Optimal use of resources

• Reduce costs

• Improve public image

• Reduce work zone delay

• Reduce incidents

• Improve travel information dissemination

• Reduce peak-period congestion

• Reduce queue length

• Air quality 

• Noise pollution reduction

• Fuel consumption reduction

• Increase enforcement

• Reduce crash severity

• Step 2 – In order to narrow down the list of potential variables, the agency can administer 

a questionnaire internally and to stakeholders to rate the variables using a 1 through 5 

scale, where 1 is “not important” and 5 is “most important” to the project. At this step, 

the agency also can eliminate the variables consistently deemed not important. 

• Step 3 – Using the preferred software, the agency can run a factor analysis (using Steps 2 

to 5 of the EFA methodology listed above) using the data collected in order to extract the 

primary variables to be used for the criteria. At the end of this analysis, the agency will 
126 

have developed a factor matrix such as the example shown in Table 45. The factor 

matrix determines the strength of the relationships amongst variables.

Table 45. Example Factor Matrix

Potential Variables Factor 1 Factor 2 Factor 3 Factor 4

Reduce construction duration 0.888 0.245 0.132 0.103

Improve construction operations 0.957 0.143 0.007 0.112

Optimal use of resources 0.812 0.008 0.002 0.003

Reduce costs 0.755 0.005 0.001 0.002

Improve public image 0.004 0.003 0.002 0.012

Reduce work zone delay 0.071 0.915 0.231 0.089

Reduce incidents 0.009 0.771 0.005 0.979

Improve travel information dissemination 0.312 0.689 0.009 0.785

Reduce peak-period congestion 0.0813 0.876 0.34 0.321

Reduce queue length 0.0789 0.898 0.312 0.348

Air quality 0.002 0.001 0.889 0.004

Noise pollution reduction 0.001 0.001 0.651 0.009

Fuel consumption reduction 0.001 0.001 0.876 0.01

Increase enforcement 0.012 0.562 0.005 0.779

Reduce crash severity 0.001 0.387 0.002 0.985

• Step 4 – The agency interprets the results from the factor matrix. They decide that the 

variables within the set of factors with loadings greater than 0.5 can be grouped. Based 

on this exercise, the agency was able to define the factors due to the similarities between 

the variables contained within the group. In the example in Table 45, Factor 1 could be 

called “Project Management and Efficiency” as the variables interrelated (scoring above 

0.5) seem to be benefits regarding construction or project management. Factor 2 can be 

defined as “Traffic Condition and Performance” and Factor 3 can be defined as 

“Environmental.” In addition, note that the variable “Improves public image” will be 

eliminated because it did not have loadings greater than 0.5 across the factors.

• Step 5 – The agency can then decide to generate factor scores to develop a scale or 

weights, as shown in Table 46. The agency can use the regression method standard to 

their statistical software package. In the regression method, each variable is weighted 

proportionally to its involvement in a pattern. Therefore, the more involved or more 

relevant a variable, the higher the weight. The factor scores can be interpreted to mean 

that the particular case has a major impact on the factors the farther away they are from 

zero. The sign indicates whether it is a positive or a negative impact. The closer to zero 

indicates that the strategy has little relevance or less of an impact on the factor and vice 

versa. These scores are standardized, which means they have been scaled to have a mean 

of zero and the values lie between +1.00 and -1.00. 

An example is shown in Table 46. In this example, the analyst can use the factor scores as part 

of a weighting or scaling technique. It can be assumed that through the agency’s literature 

review and/or modeling analysis efforts they would gather data for each alternative that 

addresses the variables listed. The analyst can then multiply those data values by the associated 

variable factor scores and sum those for each of the factors: Project Management Efficiency; 

Traffic Condition and Performance; and Environmental. Using their weighting/scaling 
127 

technique, the agency can then select the recommended alternative based on the final scores from 

the three factors.

Table 46. Example Factor Scores

Priority Variables

Project 

Management 

Efficiency

Traffic 

Condition and

Performance Environmental Safety

Reduce construction duration 1.879 0.075 0.011 0.013

Improve construction operations 1.741 0.019 0.013 0.013

Optimal use of resources 1.782 -1.101 0.003 -0.954

Reduce costs 1.825 -1.001 -0.907 -1.455

Reduce work zone delay 0.092 1.311 0.004 0.098

Reduce incidents -1.492 0.965 -0.791 1.679

Improve travel information dissemination -1.482 0.765 -0.781 1.098

Reduce peak-period congestion -0.007 1.13 0.023 0.054

Reduce queue length -0.012 1.542 0.042 0.044

Air quality -1.281 -1.733 0.812 -1.329

Noise pollution reduction -0.619 -1.277 0.829 -1.278

Fuel consumption reduction -0.101 -0.761 0.801 -1.341

Increase enforcement -1.248 0.049 -0.021 1.763

Reduce crash severity -1.112 0.038 -0.091 1.589

Ranking System

Approach Overview

In the ranking system, the decision-maker(s) rank the criteria by level of importance. The 

ranking is then used to calculate the weights using the following formula:(35) 

(2)

Where:

Wi = Normalized weighting for the ith criterion;

ri= Ranking score for the ith criterion; and

n = Number of decision criteria.

Table 47. Pros and Cons of the Ranking System 

Pros Cons

• Simple and comprehensible for technical analysts, 

planners, and policy-makers.

• Weighting scales are flexible and easy to adapt or 

change based on the goals, objectives, and the 

performance measures of the project.

• Does not have to be too resource intensive. It does 

not require additional tools or software, or 

personnel with technical backgrounds.

• Because methodology may require polling a group 

for their rankings. Obtaining survey results could 

be time-consuming.

• Rankings are subjective and are not necessarily 

required to be justifiable based on field data, 

literature, case studies, etc.
128 

Example Application 

A combined example for ranking analysis, ratio analysis, paired comparison, and 100-point 

distribution is provided towards the end of this section. 

Ratio System 

Approach Overview

With the ratio system, decision-makers assign scores to criteria based on how they rate in 

importance relative to other criteria. For instance, decision-makers can give a score of 1 to the 

least important criterion. All other criteria are given greater scores relative to their level of 

importance in comparison to the least important criterion. Normalized importance weighting for 

each criterion can be calculated using the following formula:(36) 

(3)

Where:

Wi = Normalized weighting for the ith criterion;

Zi= Weight score assigned to the ith criterion; and

n = Number of decision criteria.

Table 48. Pros and Cons of the Ratio System 

Pros Cons

• Simple and comprehensible for technical analysts, 

planners, and policy-makers.

• Weighting scales are flexible and easy to adapt or 

change based on the goals, objectives, and the 

performance measures of the project.

• Does not have to be too resource intensive. It does 

not require additional tools or software, or 

personnel with technical backgrounds.

• Because methodology may require polling a group 

for their rankings. Obtaining survey results could 

be time-consuming.

• Results are subjective since choosing the least 

desirable options is not necessarily required to be 

justifiable based on field data, literature, case 

studies, etc.

• Limited comparability since ratios are based 

primarily on what is considered the least 

desirable/important option.

Example Application 

A combined example for ranking analysis, ratio analysis, paired comparison, and 100-point 

distribution is provided towards the end of this section. 
129 

Paired Comparison Analysis 

Approach Overview

In a paired comparison analysis, a range of options are compared to each other, 

determining the preferred option in each case. When comparing two options for instance, the 

option that is preferred could be given 2 points, while the other option receives 0 points. If both 

options are equally preferred, then both receive 1 point. After all options have been considered 

and compared by the individual or group polled, the scores are tallied. The option with the 

highest score is considered the preferred option or ranked first priority. In the example shown in 

Table 49, there are three options to choose from. After polling a group, the results show that 

Option C scored the highest with 4 points. Option B is in second place, and Option A is the least 

preferred of the options. 

Table 49. Example of a Paired Comparison Analysis 

Option

Option

A B C Total Score

A – – – 0 points

B B (2) – – 2 points

C C (2) C (2) – 4 points

Table 50. Pros and Cons of the Paired Comparison Analysis 

Pros Cons

• Simple and comprehensible for technical analysts, 

planners, and policy-makers.

• Weighting scales are flexible and easy to adapt or 

change based on the goals, objectives, and the 

performance measures of the project.

• Does not have to be too resource intensive. It does 

not require additional tools or software, or 

personnel with technical backgrounds.

• Because methodology may require polling a group 

for their rankings. Obtaining survey results could 

be time-consuming.

• Assigning the weights are subjective and is not 

necessarily required to be justifiable based on field 

data, market research, expert panels, recorded 

observations, etc.

• Limited comparability since scores are based on 

two options at a time.

Example Application 

A combined example for ranking analysis, ratio analysis, paired comparison, and 100-point 

distribution is provided towards the end of this section. 

100-Point System

Approach Overview

The 100-point system is a method where a total of 100 points is distributed amongst the 

attributes or factors. The distribution of the 100 points can be determined by an individual, 

through group consensus, or through a ranking system. Decision-makers can choose to distribute 

the points equally amongst the criteria or by level of importance. 
130 

Table 51. Pros and Cons of the 100-Point Distribution

Pros Cons

• Simple and comprehensible for technical analysts, 

planners, and policy-makers.

• Weighting scales are flexible and easy to adapt or 

change based on the goals, objectives, and the 

performance measures of the project.

• Does not have to be too resource intensive. It does 

not require additional tools or software, or 

personnel with technical backgrounds.

• Depending on the methodology, establishing the 

criteria weights may require a group consensus or 

polling a group of people for their opinions. 

Obtaining group consensus and/or obtaining survey 

results could be time-consuming.

• Assigning the weights are subjective and is not 

necessarily required to be justifiable based on field 

data, market research, expert panels, recorded 

observations, etc.

• Rater may be biased.

Example Application for Ranking, Ratio, Paired Comparison, and 100-Point Distribution

For this particular example, the same criteria will be prioritized and assigned weights using four 

methods: Ranking, Ratio, Paired Comparison, and 100-point distribution. The examples shown 

serve to highlight the methodologies. The values and criteria weights resulting from the example 

are illustrative only, and it is recommended that agencies utilize the methodologies presented to 

develop project-specific values.

• Step 1 – The first step is the same regardless of the analysis method selected to develop 

the weighted-criteria. During this step, the evaluator(s) must determine the set of 

performance measures that will be used, such as queue length impacts, delay, travel time, 

and speeds. 

• Step 2 – The evaluator then takes the set of criteria and applies a selected methodology to 

determine criteria weights:

• Ranking Method – A group selected by the evaluator were asked to rank the criteria 

elements on a scale of 1 to 5, where 1 is considered most important and 5 as least 

important. Criteria weights were assigned using the equation for the ranking method. 

The results are shown in Table 52. As shown, Capital and Operations and 

Maintenance Cost (Capital and O&M) is the highest weighted criteria, while Project 

Duration is assigned the lowest weight. 

Table 52. Assigning Criteria Weights – Ranking Method

Criteria Rank n-r(i)+1 Weight

Speed Reduction Potentials 2 4 0.27

Capital and O&M Costs 1 5 0.33

Queue Length 3 3 0.20

Travel Times 4 2 0.13

Project Duration 5 1 0.07

Total 15 1.00

• Ratio Method – Similar to the ranking method, the evaluator chooses a group of 

participants to compare the criteria elements against each other. In this situation, the 

evaluator considered the Project Duration criterion as least important and assigned it a 

value of “1.” The evaluator then asks the participants to determine how much greater 

the other criteria are in comparison to Project Duration. From their responses, the 
131 

evaluator determines the ratio scores. The example ratio scores are shown in 

Table 53. Criteria weights were assigned using the equation for the ratio method. As 

shown, Capital and O&M Costs is the highest weighted criteria while Project 

Duration is assigned the lowest weight. 

Table 53. Assigning Criteria Weights – Ratio Method

Criteria Ratio Score Weight

Speed Reduction Potentials 1.75 0.23

Capital and O&M Costs 2 0.27

Queue Length 1.5 0.20

Travel Times 1.25 0.17

Project Duration 1 0.13

Total 7.5 1.00

• Paired Comparison Analysis – In this analysis methodology, the evaluator 

assembles a group of survey participants. The evaluator asks the participants to 

compare pairs of criteria elements. For each pair comparison, the participants are 

asked to note which of the two is considered most important or equally important. 

After all the results were turned in, the scores were tallied for each criteria element. 

Table 54 shows the results from the poll. As shown, Capital and O&M Costs 

receives the highest score while Project Duration is scored the lowest.

Table 54. Assigning Criteria Weights – Paired Comparison Method

Speed

Capital and 

O&M Costs

Queue 

Length

Travel 

Times

Project 

Duration Scores

Speed 

Reduction 

Potentials

– – – – – 6

Capital and 

O&M Costs

Capital and O&M

Costs (2)

– – – – 8

Queue 

Length

Speed Reduction (2) Capital and 

O&M Costs (2)

– – – 3

Travel 

Times

Speed Reduction (2) Capital and 

O&M Costs (2)

Queue Length (1), 

Travel Times (1)

– – 2

Project 

Duration

Speed Reduction 

Potentials (2)

Capital and 

O&M Costs (2)

Queue Length (2) Project Duration (1), 

Travel Times (1)

– 1

• 100-Point Distribution – The evaluator, in this situation, decides how to distribute 

the 100-point scale amongst the various criteria elements. Table 55 depicts a 

potential scenario where the 100 points are distributed so that speed, queue length, 

and travel time measures each receives 20, Capital and O&M Costs receives 25, and 

project duration receives 15. 
132 

Table 55. Assigning Criteria Weights – 100-Point Distribution

Criteria Weight

Speed Reduction Potentials 20

Capital and O&M Costs 25

Queue Length 20

Travel Times 20

Project Duration 15

• Step 3 – The evaluator can then use the generated weighted criteria to evaluate and 

compare work zone alternatives. Methodologies and tools that can be used to compare 

work zone alternatives are discussed further in Section 5.3. 

5.3 WEIGHTING/SCORING TECHNIQUES

This section provides information on specific methodologies and tools that aid an agency in 

choosing among work zone alternatives. At this stage, the agency should have established a set 

of prioritized and weighted criteria to use for screening alternatives using the factor prioritization 

tools as detailed in Section 5.2. Many decision-making frameworks use weighted criteria to 

score alternatives. Alternatives are then compared and selected based on these scores. These 

Weighting/Scoring techniques are most commonly used to compare and choose amongst 

alternatives. This section highlights a few of these methods.

There are additional tools that have been created by researchers and developers that either 

automate a weighting/scoring technique or utilize a unique methodology for comparing 

alternatives. Several of these tools are given mention in Section 5.4. Ultimately, the evaluation 

frameworks and tools discussed in this and the following section can aid decision-makers in 

choosing an alternative or combination of alternatives that best meet their criteria.

This following is structured to provide information on the methodologies and tools and their 

applications for an MOTAA, including: 

• Overview of the methodology/tool, including application steps; 

• Additional considerations, including pros and cons; and 

• Example work zone application of methodology or tool. 

Overview

There are a variety of different weighting/scoring methods that can be applied in a work zone 

MOTAA process. Most take a common structure such as that depicted in Figure 18. Where the 

approaches tend to differ is in the establishment of criteria weights and how those weights are 

used to choose a recommended alternative. Some of the most common approaches in these 

weighting/scoring techniques are based in Multi-Criteria Decision-Making Models (MCDM). 

Using mathematical methodologies that assign weight to criteria, MCDM models enable analysts 

to evaluate and compare the utility or relevancy of criteria or factors to a problem. There 

are a variety of MCDM models that can be applied for work zone MOTAA. The more common 

MCDM models used for various decision-making applications include weighted sum model, 
133 

weighted product model, analytical hierarchy process, ELECTRE, and TOPSIS. For the purpose 

of this guide, two MCDM models will be discussed in further detail. The two models are the 

weighted sum model or simple additive weighting and analytical hierarchy process (AHP). The 

weighted sum is typically deemed the simplest of the MCDM models and AHP is one of the 

most commonly used MCDM models for decision-making. 

Additional weighting or scoring techniques that can be applied for evaluating alternatives include 

Kepner-Tregoe Method and Benefit/Cost Analysis. Kepner-Tregoe Decision Methodology can 

be used for gathering, prioritizing and evaluating information. It also incorporates ranking and 

scoring within its decision-making framework. Benefit/cost analysis is an economic decisionmaking approach that weighs and values an alternative’s expected benefits and costs. A 

benefit/cost analysis can generate Benefit/Cost ratios (B/C ratios) that can be used to compare 

and choose amongst alternatives. 

Figure 18. Weighting/Scoring Technique Framework

Table 56 summarizes the various features and capabilities of the different weighting and scoring 

techniques. More details on each tool can be found in the subsections that follow. 
134 

Table 56. Evaluation Tools and Methods for Comparing Alternatives 

Methods/

Considerations

Project 

Complexity 

Suited For

Analysis 

Complexity

Level 

of Data 

Needs

Development 

Time

Analysis 

Time

Number

of Criteria 

Elements

Traditional Low-Medium      

Simple Additive Weighting 

(SAW)

Low-Medium      

Analytical Hierarchy Process Medium-High      

Kepner Tregoe Method Medium-High      

Benefit/Cost Medium-High     

 Low  Medium  High

MCDM Models – Weighted Sum Method or Simple Additive Weighting (SAW)

The weighted sum method is commonly deemed as the more simple of the MCDM models. The 

weighted sum states that if there are m alternatives and n criteria, an alternative’s score is 

determined using the following equation: 

(4)

Where:

Si = The SAW score of the alternative;

aij= The ith alternative’s score for the jth criterion; and

wj= The weight of the jth criterion.

In this method, the decision-maker must accomplish two tasks prior to applying SAW. The first 

is to determine the criteria weights and the other is to develop a way to obtain each alternative’s 

relevancy scores. Once these two tasks have been completed, the decision-maker can apply the 

SAW equation to each alternative to calculate their weighted scores. The alternative that scores 

the highest is typically recommended. 

Example Application of SAW 

To evaluate and choose amongst a set of work zone alternatives using the SAW method, the 

decision-maker can take the following steps:

• Step 1 – The evaluator or analyst decides to use the weighted criteria using the ranking 

method (see Section 5.2). 

• Step 2 – The analyst assembles a panel of decision-makers that will score the alternatives 

based on how well they meet the criteria elements. The evaluator asks them to score the 

alternatives (e.g., such as the scale shown in Table 57).

• Step 3 – The analyst averages the scores for each alternative by criterion. The SAW 

equation is applied to determine the final weighted score for each alternative. The final 

scores are shown in Table 58. In this example, Alternative 2 scores the highest and is 

recommended.
135 

Table 57. Example Score for Alternatives

Scale for Scores

Most Effective 30

Mediocre 20

Not Effective 10

Table 58. SAW Example 

Alternative

Criteria and Weights

Speed 

Reduction 

Potentials

0.27

Capital and 

O&M Costs

0.33

Queue 

Length

0.20

Travel Times

0.13

Project 

Duration

0.07 Score

1 25 20 15 30 15 21.30

2 10 30 20 30 30 22.60

3 30 10 30 10 10 19.40

Table 59. Pros and Cons of SAW 

Pros Cons

• Simple analysis.

• Applicable to various problems.

• Does not have to be too resource intensive. It 

does not require additional tools or software, or 

personnel with technical backgrounds.

• Requires polling a group of people for their opinion 

on ranking or score, which could be time-consuming.

• Assigning of rankings/scores are subjective and are 

not necessarily required to be justifiable based on 

field data, market research, expert panels, recorded 

observations, etc.

MCDM Models – Analytical Hierarchy Process (AHP)

The Analytical Hierarchy Process (AHP) is an MCDM decision-making approach that 

utilizes a multiple choice criterion that is structured in a hierarchical format. It involves 

assessing the relative importance of the criteria (assigning criteria weights), comparing 

alternatives for each criterion, and determining an overall score or ranking for the alternatives.

Example Application 

This example demonstrates how an AHP can be used to evaluate and choose amongst work zone 

alternatives. The following outlines the preliminary steps in beginning an AHP analysis: 

• Step 1 – Determine the objective of the analysis: In this case, it is “To choose the 

preferred alternative amongst three potential choices.” 

• Step 2 – Define the criteria: Speed Reduction Potentials, Capital and O&M Costs, Queue 

Length, Travel Times, and Project Duration. 

• Step 3 – Determine a set of alternatives. For this example, the alternatives will be 

labeled as: Alternatives 1, 2, and 3. 

• Step 4 – Arrange the information in a hierarchical format (e.g., Figure 19).

• Step 5 – Use pairwise comparisons to determine the criteria weights. Similar to the 

Paired Comparison Analysis, the pairwise comparison in the AHP measures the 

importance of one criterion relative to another. An example is shown in Table 60. 
136 

Figure 19. AHP Hierarchical Tree Example

Table 60. Pairwise Comparison Matrix

Speed Reduction 

Potentials

Capital and 

O&M Costs

Queue 

Length

Travel 

Times

Project 

Duration

Speed Reduction Potentials 1/1 4/5 4/3 4/2 4/1

Capital and O&M Costs 5/4 1/1 5/3 5/2 5/1

Queue Length 3/4 3/5 1/1 3/2 3/1

Travel Times 2/4 2/5 2/3 1/1 2/1

Project Duration 1/4 1/5 1/3 1/2 1/1

• Step 6 – Turn the pairwise comparison matrix into a prioritization matrix using 

eigenvector. In order to do this, the matrix shown in Table 60 must be converted into its 

decimal values, as shown in Table 61. 

Table 61. Pairwise Matrix in Decimal Form

Speed Reduction 

Potentials

Capital and 

O&M Costs

Queue 

Length

Travel 

Times

Project 

Duration

Speed Reduction Potentials 1.00 0.80 1.33 2.00 4.00

Capital and O&M Costs 1.25 1.00 1.67 2.50 5.00

Queue Length 0.75 0.60 1.00 1.50 3.00

Travel Times 0.50 0.40 0.67 1.00 2.00

Project Duration 0.25 0.20 0.33 0.50 1.00

• Step 7 – Square and normalize the matrix. The matrix in Table 61 is squared to generate 

the new matrix shown in Table 62. The row sum and row total (sum of the row sum) are 

calculated. The values are normalized by dividing the row sum by the row total. The 

final column in Table 62 represents the eigenvector.

• Step 8 – Iteratively square the matrix. The matrix from Table 62 (columns Speed 

Reduction through Project Duration) is squared once more and a new set of eigenvectors 

Select Alternative

Speed 

Reduction 

Alternative 1

Alternative 2

Alternative 3

Benefit/Cost

Alternative 1

Alternative 2

Alternative 3

Queue Length

Alternative 1

Alternative 2

Alternative 3

Travel Times

Alternative 1

Alternative 2

Alternative 3

Project 

Duration

Alternative 1

Alternative 2

Alternative 3
137 

is generated. The analyst is recommended to go through multiple iterations of this 

process until the eigenvector solution does not change from the previous iteration. 

Table 62. Generating the Eigenvector

Speed 

Reduction 

Potentials

Capital 

and O&M

Costs

Queue 

Length

Travel 

Times

Project 

Duration Total

Normalize 

(Row Sum/Row 

Total) = 

EIGENVECTOR

Speed Reduction 

Potentials

1.00 0.64 1.78 4.00 16.00 23.42 0.2909

Capital and 

O&M Costs

1.56 1.00 2.78 6.25 25.00 36.59 0.4545

Queue Length 0.56 0.36 1.00 2.25 9.00 13.17 0.1636

Travel Times 0.25 0.16 0.44 1.00 4.00 5.85 0.0727

Project Duration 0.06 0.04 0.11 0.25 1.00 1.46 0.0182

Total 80.50 1.00

• Step 9 – Check for matrix stability. After several iterations, the eigenvectors stabilize to 

the values listed in Table 63. These values represent the criteria weights.

Table 63. Criteria Rankings/Weights 

Ranking

Speed Reduction Potentials 0.2907

Capital and O&M Costs 0.4543

Queue Length 0.1634

Travel Times 0.0725

Project Duration 0.0180

• Step 10 – Generate the pairwise comparisons. This pairwise comparison for the 

alternatives is used to determine the preference of each alternative over the other relative 

to the criterion. Table 64 shows the pairwise comparisons for the alternative for criterion, 

Speed Reduction Potentials. 

Table 64. Pairwise Comparison of Alternatives 

Alternative

Speed Reduction Potential

Alternative 1 Alternative 2 Alternative 3

1 1.00 2.00 3.00

2 0.50 1.00 2.00

3 0.33 0.50 1.00

• Step 11 – Compute the eigenvector of each alternative for each criterion and select the 

preferred alternative. As shown in Table 65, to generate the final score of each 

alternative, the alternative rankings are multiplied by the criteria weights. In this 

example, Alternative 2 scores the highest and is chosen. 
138 

Table 65. AHP Final Score and Recommendation

Alternative

Speed 

Reduction 

Potentials

0.2907

Capital and 

O&M Costs

0.4543

Queue 

Length

0.1634

Travel 

Times

0.0725

Project 

Duration

0.0180

Final Score 

(Criteria Ranking 

Alternative 

Ranking)

1 0.3846 0.3333 0.2308 0.4286 0.2727 0.3370

2 0.1538 0.5000 0.3077 0.4286 0.5455 0.3631

3 0.4615 0.1667 0.4615 0.1429 0.1818 0.2990

Table 66. Pros and Cons of AHP 

Pros Cons

• Applicable to various problems.

• Does not have to be too resource intensive. It 

does not require additional tools or software, or 

personnel with technical backgrounds.

• Checks the consistency of evaluation measures 

and alternatives in an effort to reduce bias.

• Requires polling a group of people for their opinion 

on ranking or score, which could be time-consuming.

• Determining the eigenvectors, if done manually, 

could be time-consuming as well.

• Different hierarchical structures could lead to 

different results.

• Pairwise comparison – limited comparability.

• Rank reversal – depending on how the question is 

framed may get different results.

Kepner-Tregoe (KT) Method

The Kepner-Tregoe decision-making methodology can be used for prioritizing and evaluating 

alternatives. The method features a step-by-step approach that can aid a decision-maker to 

choose amongst alternatives by evaluating each of their impacts, risks, and opportunities. It 

typically involves the following steps: 

1. Preparing the Decision Statement – This establishes the objectives, the desired result, and 

the action required. 

2. Defining the Objectives – At this step, the objectives are classified according to their 

relative importance as “musts” and “wants.” Certain objectives will be considered 

mandatory and will, therefore, be considered as “musts.” Those that are desirable will fit 

into the “wants” category.

3. Ranking the Objectives and Assigning Relative Weights – At this step, “wants” 

objectives are ranked on a 1 through 10 scale, where rank 10 is considered most important 

and 1 is least important.

4. Listing Alternatives – At this stage, a list of potential alternatives are brought into the 

analysis.

5. Evaluating the Alternatives – The alternatives are evaluated based on the “must” and 

“wants” objectives. The first step in evaluating the alternatives is to eliminate those that do 

not fit the “must” or mandatory objectives. The remaining alternatives are then scored by 

how they meet the “wants” objectives. The alternatives are rated against each “wants” 

objective on a scale of 1 through 10, where 10 means the alternative best satisfies the 

“want.” The final score for each alternative will be a weighted value calculated by 

multiplying the “want” objective’s weight times the alternative’s satisfaction score.
139 

6. Choosing the Top Alternatives – After Step 5 has been exercised for all of the 

alternatives, the top two or three may be considered for the next stage where the alternatives 

are rated against potential future adverse effects. 

7. Evaluating the Alternatives against Potential Negative Effects – In this step, a set of 

potential consequences will be generated. The impact of each consequence will then be 

determined by evaluating the probability of it occurring and the severity of the impact should 

this event occur. The probability and impact scores are again determined on a 1 through 10 

scale, where 10 is highest probability of occurrence or most serious degree of impact.

8. Choosing the Preferred Alternative – The preferred alternative will be the one that 

satisfies the “must” objectives, scores the highest on the “wants” objectives analysis, and 

provides best potential to minimize the adverse impacts. 

Example Application 

The following example is derived from the Work Zone Road User Costs – Concepts and 

Applications.(36)

Step 1 – Prepare Decision Statement

The KT decision analysis process begins with a precise statement of what needs to be done (i.e., 

the purpose or the intended result) and how it will be done (i.e., actions required). This 

statement provides the focus for all other steps that follow and sets the limits on the range of 

alternatives that would considered in the decision analysis. This statement must be defined 

consistent with the work zone-related agency policies and project-specific needs.

The decision statement for a hypothetical project “Pavement Reconstruction of U.S. 00” is 

presented as follows: 

Step 2 – Define Objectives

The objectives are the decision criteria that describe the required and desired attributes of the 

resulting choice, and the explicit limits imposed on the decision process. The objectives include: 

• MUSTS – These are the mandatory attributes required for an alternative to be considered 

in the decision process. These attributes are considered mandatory to guarantee a 

successful decision. Any alternative that cannot comply with a MUST objective is 

eliminated for further consideration, while those that comply with all the MUST 

objectives qualify as feasible alternatives. The MUST objectives should be measurable 

and provide an absolute GO/NO GO judgment.

Example 1 

U.S. 00 serves as a major arterial road connecting the regional industrial hub with the twin metros. The 

pavement has reached its useful life and needs major reconstruction. The route carries significant amounts of 

commuter and truck traffic. The alternative routes for the detour have limited lane capacity and can 

accommodate only a portion of the work zone traffic volume.

Decision Statement

The purpose of the decision analysis is to identify the most appropriate strategy for maintaining traffic on 

U.S. 00 during the reconstruction of the pavement segments between Mileposts 100 and 105.
140 

• WANTS – These are the desired attributes based on which preferred alternative is 

selected from the pool of feasible alternatives (i.e., alternatives that fulfill all the MUST 

objectives). A mandatory or high-priority objective can be considered as a WANT 

objective, if that objective is not measurable or a relative assessment is preferred over an 

absolute GO/NO GO judgment. A MUST objective also can be considered as a WANT 

objective by rephrasing the objective statement for relative assessment of feasible 

alternatives.

In other words, the MUSTs decide who gets to play, but the WANTS decide who wins. 

A list of MUST objectives for the “Pavement Reconstruction of U.S. 00” example is presented as 

follows: 

A list of WANT objectives for the example is presented as follows:

Selection of Objectives

One of the commonly cited concerns with a decision analysis is the interdependency among 

objectives. It is a phenomenon where two or more objectives are highly correlated. The 

presence of interdependence among objectives in a decision analysis can produce erroneous or 

misleading outcomes. Interdependence leads to double counting and tends to weigh heavily 

toward the interdependent factors, while diminishing the significance of other factors in the 

analysis. Therefore, it is imperative that a decision analyst screen for interdependency among 

the objectives and validate them.

For example, consider the list of WANT objectives presented above. The factor “daily road user 

costs” is highly correlated with the following factors: length of detour, maximum queue length, 

average delay time, average time to clear noninjury incidence, and percent traveling at a speed 

1. Minimize daily road user costs ($)

2. Minimize number of days for project completion

3. Minimize traffic control and construction engineering costs ($)

4. Minimize length of detour (miles)

5. Minimize queue length (lane-miles)

6. Minimize average delay time per vehicle (minutes) 

7. Minimize percent motorist traveling at a speed 15 mph less than the posted limit

8. Minimize average time to clear a noninjury incidence (minutes) 

9. Maintain emergency services (adjectival ratings – poor, average, good)

10. Reduce environmental impacts (adjectival ratings – low, moderate, severe)

1. Maintain a minimum of one lane each direction for work 

zone traffic during weekday peak hours.............................................................................Go/No Go

2. No lane closure between 7:00 a.m. through 10:00 a.m.

and 4:00 p.m. through 8:00 p.m. during weekdays ............................................................Go/No Go

3. Queue length not more than 0.75 miles for more than one hour ........................................Go/No Go

4. Delay time not more than 30 minutes.................................................................................Go/No Go

5. Alternative detour route exceeds capacity? ........................................................................Go/No Go
141 

15 mph less than the posted speed limit. These factors all contribute to the computation of the 

daily road user cost value. Similarly, the factors “the number of days for project completion” 

and “traffic control and construction engineering costs” also are highly correlated.

One common technique used by practitioners in screening the interdependency among objectives 

is sensitivity analysis. A sensitivity analysis can be conducted formally or informally to evaluate 

the effects of varying one objective (numerical or adjectival) on other objectives and final 

outcomes. The results of the sensitivity analysis will help to identify correlations among analysis 

factors. Both the degree of correlation and the logical dependency between the factors should be 

taken into account while identifying the dependent pairs. The purpose here is to avoid double 

counting rather than eliminating all correlated factors.

Consider the dependency between two pairs: 1) average delay time versus daily road user cost;

and 2) average delay time versus average time to clear a noninjury incidence. In the former case, 

considering both the factors in the analysis will lead to double counting as the factor “daily road 

user cost” is a monetized aggregation of various impacts, including the factor “average delay 

time.” Any change in the average delay time will result in a proportional change in the daily 

road user cost. In such cases, it is suggested to eliminate the factor “average delay time” or 

break the factor “daily road user cost” into individual components. 

In the latter case, the factor “the change in average time to clear a noninjury incidence” also 

causes a proportional change in the average delay time and, hence, is highly correlated. 

However, considering the probability of a noninjury incidence and the importance of clearing the 

incident, the analyst may prefer to list both factors to emphasize the effectiveness on traffic 

incident management in MOT alternative selection and to distinguish it from other traffic delay 

control strategies. Therefore, it is imperative to use engineering judgment and experience in 

selecting the objectives so that the intended purpose of the analysis and the complexity of the 

problem are not diluted. 

Interdependency can be countered effectively by defining the objectives in near similar 

hierarchical order. The problem of interdependency may occur if one objective is defined at the 

aggregate/generic level while another is defined at the component/specific level. For example, in 

the list of WANT objectives presented above, the interdependency between the factor “daily road 

user cost” and other factors is a result of mixing up the factors from different hierarchical order, 

as illustrated in Figure 20. This figure presents the relationship between “daily road user costs” 

and only those delay-related WANT objectives listed in the example. The factors listed on the 

left (queue length, average time to clear a noninjury incidence, etc.) contribute in determining the 

average delay time which, is used in the daily road user cost computation. 
142 

Daily Road 

User Cost Delay Costs Average 

Delay Time

Queue Length

Detour Length

Average time to 

clear a non-injury 

incidence

Percent motorist 

traveling at a speed 

15 mph less than 

the posted limit

Figure 20. Illustration of Relationships among Factors 

A modified list of WANT objectives for the example is presented as follows:

Step 3 – Weighting the Objectives

All MUST objectives are assigned with GO and NO GO options. Each WANT objective is 

weighted on a scale of 1 to 10 based on their relative importance in the decision process, with the 

weight of 1 indicating “least preferable” and 10 indicating “most preferable.” The weights 

assigned to the WANT objectives should reflect the agency’s work zone policies and projectspecific needs.

The following issues should be evaluated while assigning the weights: 

• Too many high weights may indicate either unrealistic expectations or a faulty perception 

of which objectives can guarantee success;

• Too many low weights suggest the possible inclusion of unimportant details in the 

analysis; and

• Biased objectives may produce an ineffective analysis. 

The following illustrates the assigning of weights to each of the WANT objectives considered in 

the “Pavement Reconstruction of U.S. 00” example:

1. Minimize delay costs

2. Minimize vehicle operating costs

3. Minimize number of days for project completion

4. Minimize traffic control and associated construction costs (e.g., shoulder widening, temp bridges), etc. 

5. Minimize average time to clear a noninjury incidence (minutes) 

6. Maintain emergency services (adjectival ratings – poor, average, good)

7. Reduce environmental impacts (adjectival ratings – low, moderate, severe)
143 

Step 4 – Identify Candidate Alternatives

Identify all potential alternatives, whether immediately feasible or not, to be evaluated and 

measured as MUST and WANT objectives. Use the alternatives identified in Step 4 as candidate 

alternatives for decision analysis.

The candidate alternatives for the “Pavement Reconstruction of U.S. 00” example are listed as 

follows:

Step 5 – Summarize the Findings of Work Zone Impact Assessment

A detailed work zone impact assessment for each candidate alternative should be done to 

evaluate both MUST and WANT objectives for quantitative and qualitative results. Use the 

findings of the preliminary and detailed impact assessments for evaluation. The assessment 

findings should be summarized for each alternative against the objectives. 

The following summarizes the impact assessment findings of all alternatives against the MUST 

objectives considered in the “Pavement Reconstruction of U.S. 00” example:

MUST Objective

Alternative Evaluation

A B C D E

Maintain a minimum of one lane each direction for 

work zone traffic during weekday peak hours

Yes Yes Yes Yes Yes

No lane closure between 7:00 a.m. through 10:00 a.m. 

and 4:00 p.m. through 8:00 p.m. during weekdays

No Yes Yes Yes Yes

Maximum queue length (miles) 1.6 0.0 0.0 0.5a 0.5b

Average delay time per vehicle (minutes) 19.0 6.0 3.0 10.0a 20.0b

Alternative detour route exceeds capacity? No No No No Yes

(Source: Federal Highway Administration, 2011.) 

a Calculated for the selected detour route.

b Weighted average for both mainline and detour routes.

Daytime partial lane closure – closed between 7:00 a.m. to 5:00 p.m.

Nighttime partial lane closure – closed between 8:00 p.m. to 6:00 a.m.

Nighttime partial lane closure – closed between 9:00 p.m. to 7:00 a.m.

Nighttime full lane closure – closed between 9:00 p.m. to 6:00 a.m.

Truck traffic diverted through alternative detour routes during peak hours.

No. WANT Objective Assigned Weight

1 Delay costs 10

2 Vehicle operating costs 8

3 Number of days for project completion 10

4 Traffic control and associated construction costs ($) 8

5 Average time to clear a noninjury incidence (minutes) 4

6 Maintenance of emergency services (adjectival ratings – poor, average, good) 6

7 Environmental impacts (adjectival ratings – low, moderate, severe) 3

(Source: Federal Highway Administration, 2011.) 
144 

The following summarizes the impact assessment findings of all alternatives against the WANT 

objectives considered in the “Pavement Reconstruction of U.S. 00” example:

Step 6 – Evaluation of Alternatives against MUST Objectives 

Evaluate all available alternatives against each of the MUST objectives identified in the earlier 

step. Any alternative is eliminated from further consideration if it fails to satisfy one or more of 

the MUST objectives; only those satisfying all the MUST objectives are considered as feasible 

alternatives.

The results obtained from the evaluation of alternatives against MUST objectives are presented 

as follows: 

Based on the evaluation results, Alternatives A and E are eliminated from further consideration 

as these alternatives did not satisfy all the required attributes. The remaining Alternatives B, C, 

and D are carried into the next step. 

MUST Objective

Alternatives

A B C D E

Maintain a minimum of one lane each direction for work zone 

traffic during weekday peak hours Go Go Go Go Go

No lane closure between 7:00 a.m. through 10:00 a.m. and 

4:00 p.m. through 8:00 p.m. during weekdays No Go Go Go Go Go

Queue length not more than 0.75 mile for more than one hour No Go Go Go Go Go

Delay time not more than 30 minutes Go Go Go Go Go

Alternative detour route exceeds capacity? Go Go Go Go No Go

(Source: Federal Highway Administration, 2011.) 

Outcome:

Alternatives A and E are eliminated.

Alternatives B, C, and D qualify as feasible alternatives.

WANT Objective

Alternative Evaluation

A B C D E

1. Delay costs $5,300 $3,125 $2,800 $4,700 $6,800

2. Vehicle operating costs $1,484 $656 $728 $1,175 $1,836

3. Number of days for project completion 150 84 84 60 90

4. Traffic control & associated construction costs ($) $55,000 $94,000 $75,000 $109,000 $85,000

5. Average time to clear a non-injury incidence 

(minutes) 

20 25 25 15 10

6. Maintenance of emergency services 

(adjectival ratings – poor, average, good)

Moderate Moderate Moderate Good Good

7. Environmental impacts 

(adjectival ratings – low, moderate, severe)

Moderate Severe Severe Low Low

(Source: Federal Highway Administration, 2011.) 
145 

Step 7 – Evaluation of Alternatives against WANT Objectives 

In this step, each alternative is assigned with a score of 1 to 10 against each WANT objective 

based on how well the alternative meets that objective. This step involves not only assessing 

each alternative individually against each WANT objective but also comparing the alternatives 

with each other against each WANT objective. The results obtained from the evaluation of 

alternatives against WANT objectives are presented as follows:

Step 8 – Weighting the Scores of Alternatives

The weighted score of each feasible alternative should be computed to determine the relative 

performance of the alternatives.

The weighted score is the score of an alternative multiplied by the weight of the WANT 

objective to which the score refers. For example, the weight of the objective “Length of detour” 

is 7 and the score of Alternative D against this objective is 2. Therefore, the weighted score of 

Alternative D on that objective is 14. For each alternative, all the weighted scores are added up 

to calculate the total weighted score for that alternative.

The total weighted score of an alternative indicates how well an alternative stacks up against 

each of the other alternatives for overall performance against WANT objectives. In other words, 

the total weighted scores function as a visible comparative performance of the alternatives.

For the “Pavement Reconstruction of U.S. 00” example, the individual and the total weighted 

scores of each feasible alternative are presented as follows:

WANT Objective

Alternative Score

A B C D E

Delay costs 90 100 60

Vehicle operating costs 80 64 56

Number of days for project completion 70 70 100

Traffic control and associated construction costs ($) 64 64 80

Average time to clear a noninjury incidence 24 24 40

Maintenance of emergency services 36 36 60

Environmental impacts 9 9 30

Total Weighted Score 373 367 426

(Source: Federal Highway Administration, 2011.) 

WANT Objective

Alternative Score

A B C D E

Delay costs 9 10 6

Vehicle operating costs 10 8 7

Number of days for project completion 7 7 10

Traffic control and associated construction costs ($) 8 8 10

Average time to clear a noninjury incidence 6 6 10

Maintenance of emergency services 6 6 10

Environmental impacts 3 3 10

(Source: Federal Highway Administration, 2011.)
146 

In this example, Alternative D is considered as the tentative choice.

Step 9 – Evaluation of Adverse Consequences (Optional) 

After the completion of the alternative evaluation using MUST and WANT objectives, the 

feasible alternatives can be further evaluated against potential risks. The objective of this step is 

to understand the consequences of selecting an alternative.

This step is deemed optional as the potential risks are expected to be identified in the work zone 

impact assessment and incorporated into the decision analysis as a MUST or WANT objective.

This step is particularly recommended when the total weighted scores of all the alternatives are 

closer. In the “Pavement Reconstruction of U.S. 00” example, the total weighted scores of 

alternatives B, C, and D are 373, 367, and 426, respectively. Since these scores are close, a risk 

assessment may be warranted to ensure that the best decision is being made. Suppose, if the total 

weighted scores of these alternatives were 100, 110 and 633, respectively, alternative D stands 

out among the feasible ones by an order of magnitude and, therefore, a risk assessment is not 

required. 

The risk assessment begins with the tentative choice (i.e., the alternative with the highest total 

weighted score). For this alternative, the probability of an adverse consequence and the severity 

of the impact (i.e., performance of an alternative under that event) is assessed and rated on a 

High-Medium-Low scale or a scale of 10 (high probable/very severe) to 1 (unlikely/not severe). 

This step is repeated for each potential adverse consequence to produce the “adverse 

consequence totals.” The risk assessment is then repeated for other feasible alternatives.

For the “Pavement Reconstruction of U.S. 00” example, three potential risks were considered:

• Event of flooding; 

• High severity crashes (involving multiple crashes and longer incidence time); and

• Event of an emergency evacuation due to a natural catastrophe.

The likelihood of these events occurring and the performance of an alternative under these 

situations were rated as probability and severity ratings, respectively. The weighted score for 

each risk factor is calculated to produce the total adverse consequence score of an alternative. 

The example is presented as follows:
147 

Step 10 – Selection of the Preferred MOT Strategy

For each alternative, the net score is calculated by subtracting the total adverse consequence 

score from the total weighted score. The alternative with the highest net score is selected as the 

preferred MOT strategy.

In the “Pavement Reconstruction of U.S. 00” example, Alternative D (i.e., Nighttime full lane 

closure between 9:00 p.m. and 6:00 a.m.) is selected as the preferred MOT strategy.

Alternative Description

Total Weighted 

Score

Total Adverse 

Consequence Score

Total 

Score Rank

A Daytime partial lane closure –

closed between 7:00 a.m. to

5:00 p.m.

Eliminated – – – 

B Nighttime partial lane closure –

closed between 8:00 p.m. to 

6:00 a.m.

373 -42 331 2 

C Nighttime partial lane closure –

closed between 9:00 p.m. to 

7:00 a.m.

367 -42 325 3 

D Nighttime full lane closure –

closed between 9:00 p.m. to 

6:00 a.m.

426 -59 367 1 

E Truck traffic diverted through 

alternative detour routes during 

peak hours.

Eliminated – – – 

(Source: Federal Highway Administration, 2011.)

Table 67. Pros and Cons of Kepner-Tregoe Method

Pros Cons

• Applicable to various problems.

• Does not have to be too resource intensive. It 

does not require additional tools or software, 

or personnel with technical backgrounds.

• Accounts for some risk analysis.

• Requires polling a group of people for their opinion on 

ranking or score, which could be time-consuming.

• Still has some bias since individual or group must decide 

on the relevancy, probability, and severity scores.

Adverse 

Consequence

Alternative

B C D

Probability Severity Score Probability Severity Score Probability Severity Score

Flood impact 3 5 15 3 5 15 3 5 15

High severity 

crashes

5 4 20 5 4 20 5 7 35

Emergency 

evacuation

1 7 7 1 7 7 1 9 9

Total adverse 

consequence 

score

42 42 59
148 

Benefit/Cost Analysis

A benefit/cost analysis (BCA), though primarily based on financial information, can be 

used to compare and choose among alternatives. An agency can choose from various readily 

available tools or customize their own. There are a variety of tools created for transportationrelated projects, including the FHWA’s HERS-ST, California DOT’s (Caltrans) Cal-B/C, and 

Texas Transportation Institute’s MicroBENCOST. Different B/C tools may be best fit for 

certain projects. The FHWA currently is working on developing a Benefit/Cost desk reference 

tool which will have a feature that will match the appropriate B/C tool to an agency’s project 

type and needs. 

The B/C method typically includes the following steps:

• Step 1 – Identify and select the data for use in the BCA;

• Step 2 – Determine the benefits elements; 

• Step 3 – Determine the cost elements; and

• Step 4 – Compare the sum of the costs with the sum of the benefits and determine a 

benefit/cost ratio.

For a work zone MOTAA analysis, there are several factors that can influence the benefit/cost 

analysis. Benefit elements that can be used in evaluating and comparing alternatives could 

include monetary value of travel time savings, road user cost reductions, emissions reductions, 

and other cost savings. Cost elements could include capital, operations and maintenance, and 

other costs and fees associated with the project. For more details on the specific work zonerelated considerations that should be incorporated in a work zone MOTAA-specific BCA, please 

refer to Chapter 6 of this document. For additional information on how to calculate specific 

work zone-related costs, please refer to Work Zone Road User Costs – Concepts and 

Applications.

(36) 

Example Application 

• Step 1 – The first step in a benefit/cost analysis for a work zone project would be to 

determine relevant project information that would serve as input data for the BCA. Such 

input data could include: 

• Project Characteristics – Length of construction period, project type, and length of 

peak period affected;

• Design and Traffic Information – Work zone strategy (i.e., lane closure, corridor 

reconstruction, etc.), average daily traffic, speeds, and vehicle make-up of traffic; and

• Safety and Accident Statistics – Fatal and injury accident rates.

• Step 2 – The benefits for the project alternative are calculated using the selected analysis 

tool. The performance measures are monetized based on the value of each performance 

measure. These benefits are typically calculated over the lifetime of the project and are 

brought to a Net Present Value (NPV). 

• Step 3 –The project costs information are determined by looking at all direct costs 

associated with construction, operations and maintenance of the project alternative, as 
149 

well as additional costs associated with any mitigation measures needed. The costs are 

typically calculated over the lifetime of the project and are discounted to a NPV.

• Step 4 – The results of the analysis will produce the total NPV of the benefits and costs 

of the project. Typically a Benefit/Cost ratio is determined as a common measure to 

compare different project alternatives. The B/C ratio is determined by dividing the total 

benefits by the total costs of the alternative. This process is conducted for all 

alternatives. The alternative with the best B/C ratio should be chosen as the preferred 

alternative. For reference, a summary results sheet from one BCA tool, Cal-B/C, is 

shown in Figure 21.(37)

Figure 21. Benefit/Cost Analysis Summary Results Example' 

Table 68. Pros and Cons of Benefit/Cost Analysis 

Pros Cons

• Applicable to various problems

• Accounts for costs, risks, and 

performance – all critical to determining 

the feasibility of the alternative

• Based on data that is verifiable through 

field data, case studies, etc.

• May be data intensive

• Could provide erroneous results if calculations, forecasts, 

and estimates are unrealistic

• Benefits and cost elements included in the analysis are 

subject to the analyst or decision-maker(s)

• Some benefits or costs may not be easily quantifiable
150 

5.4 DECISION-MAKING TOOLS FOR WORK ZONE ALTERNATIVES ANALYSIS

The previous sections described how to prioritize and establish criteria weights and then use 

those weighted criteria in a decision-making evaluation framework to evaluate and choose 

among potential alternatives. This section will provide information on various tools that 

automate or package decision-making analysis methods into a readily available tool or software. 

The section is structured as follows: 

• Overview of the methodology/tool, including application steps; 

• Additional considerations, including pros and cons; and 

• Case study of tool application for work zones. 

Knowledge-Based Systems (Case-Based Reasoning) 

Overview of Methodology/Tool 

Case-based reasoning (CBR) is a methodology for storing and retrieving previous cases and 

adapting them to a new solution. A case-based reasoning system is one methodology for 

solving problems that uses prior knowledge to adapt new solutions. The CBR framework 

featured in this section is based upon the CBR model developed by Karim and Adeli (2003) for 

work zone traffic management.(38) 

Typical components of a CBR are illustrated in Figure 22.(38) The following sections provide 

further explanation on how each of these elements can be applied to a work zone traffic 

management example. The use of CBR for work zone MOTAA analysis differs from the other 

decision methods because unlike the other frameworks, the agency does not have to create a 

preliminary list of potential alternatives. If there is a sufficient database that supports the CBR 

tool, the outputs of the analysis should present the optimal alternative(s) based on the project 

characteristics inputs and the weights set by the agency analyst.

Case Models for the Work Zone Traffic Management – Domain Information 

The domain information serves at the base of CBR, providing a structure for the problem 

identification and formulation and also the collection of experiences that will serve as references 

for the solution and calculation of impacts. Karim and Adeli’s research sets to explain the use of 

the CBR system for work zone traffic management through a four-set case model that consists of 

the following:

• General – The general set contains information that provides historical information and 

previous experiences for future reference.

• Problem – The problem set includes information that defines the constants of the work 

zone traffic control problem. This set typically contains project characteristics.

• Solution – The solution set contains information regarding the work zone layout, 

strategies, and traffic mitigation measures.

• Effects – The effects set contains information about the traffic impacts for the work zone. 
151 

Figure 23 provides an example of the data components and work zone parameters considered 

within the four-set case model.(38) 

Figure 22. Elements of CBR

(Source: Karim and Adeli, 2003.)
152 

ID

Description

Freeway/ 

direction

Location

Comments

Case

 

General Problem Solution Effects

Description

Traffic Control 

Measures

Time Cost Layout Traffic Flow 

Characteristics

Work 

Characteristics Layout Road User Cost

Start time

Duration

CCC

MTC

No. of lanes Flow rate

Percent trucks

Driver 

behavior

Phase 

duration

Work 

intensity

No. of open 

lanes

Layout

Queue length

Delay time

Complaints

Safety

Corridor 

capacity

Inside 

Work Zone

Speed limit

Lane width

Gawk/glare 

screens

Outside 

Work Zone

Advance 

warning

Real-time 

traffic info

Alternate 

route

A B

Object A contains Object B

Figure 23. Four Set Case Model – Work Zone Example 

(Source: Karim and Adeli, 2003.)
153 

Case Representation

The second element of CBR is representation, which defines a data structure for the collected 

experiences or cases stored as reference. The type of representation used in this CBR research is 

attribute-value. The attribute-value system consists of three elements: attribute field name, 

attribute type, and value. 

Case Retrieval

Another element of CBR is retrieval, which is the retrieval of a potential solution and 

determining similarities between the problem and previous cases. In the CBR system, retrieval 

starts with the formulation of a query. Based on this query, the system retrieves cases that can 

serve as potential solutions to the problem. These cases are retrieved due to their match or 

degree of similarity to the query/problem.

Factor Prioritization and Criteria Weights

The use of criteria weights is explained in further detail in the example application section. 

Weights can be determined using the factor prioritization methods detailed in the previous 

section.

Evaluation of Criteria Scores and Recommending an Alternative 

As previously mentioned, cases are retrieved from a series of queries. The cases retrieved are 

ranked based on a similarity score that denotes how similar the case is to the reference case. In 

the CBR system, the level of similarity or similarity score ranges from 0 to 1, where 0 indicates 

no similarity and 1 means full similarity. The cases retrieved are ranked and presented to the 

user. The case with the highest score serves as the potential solution. Additionally, the evaluator 

can modify this case to better fit the reference case or the project objective.

Table 69. Pros and Cons of the CBR Tool 

Pros Cons

• Tool is simple and user-friendly

• Provides a database of and case studies of work 

zone strategies

• Can require a lot of data

• There may not be enough case data available

• Development of tool and database modules could 

become complex

Example Application 

The procedure for the creation of work zone traffic control plans using the CBR system for work 

zone traffic management is shown in Figure 24.(38)

 When a traffic analyst decides to create a 

traffic control plan for a given work zone scenario, the analyst begins with basic project 

information, such as number of lanes and flow rate. This background information is fed into the 

CBR system by responding to queries made by the system. The queries are done iteratively until 

the best fit case is found. The first query may start with basic information, such as number of 
154 

lanes and the flow rate. To narrow down results further, additional queries can be made with 

other information about the work zone, such as phase duration or work zone intensity. 

 

No. of lanes; flow rate

Phase duration, work intensity

No. of open lanes; layout

Percent trucks; driver behavior

Traffic control measures

New work zone scenario

Adapt

Key in 

reference case

Choose weights

Evaluate 

case scores

Highest 

ranked 

case

Desired traffic 

control plan

Unsatisfactory

Satisfactory

Possible modification in subsequent interactive session

Figure 24. Work Zone Traffic Control CBR System 

(Source: Karim and Adeli, 2003.)

The traffic analyst also can assign weights to certain attributes to further filter results based on 

priority or mandatory work zone characteristics. Finally, after the queries and weights have been 

applied, the analyst can compare the final results to each other based on their similarity scores. 

The higher the score indicates a closer match to the reference case and weights inputted by the 

user.

Additionally, the CBR system can evaluate the final list of cases retrieved and measure their 

impacts on motorists, the number and type of traffic control measures, and the maintenance of 

traffic cost. The best fit case or the case with the highest score can, therefore, provide insight 

regarding some potential work zone alternatives and mitigation strategies that could optimize the 

reference project’s operations and benefits. The CBR analysis will allow the analyst to modify 

their reference case/project with a desired solution or set of alternatives. The agency also can 

modify an existing case to obtain an improved solution or set of alternatives that will enable 

them to meet their particular goals, objectives, and/or criteria. Figures 25 to 28 provide 

snapshots of the CBR system used for work zone analysis following the four sets: General, 

Problem, Effects, and Solution.(38) 
155 

Figure 25. CBR System General Set

(Source: Karim and Adeli, 2003.)

 

Figure 26. CBR System Problem Set 

(Source: Karim and Adeli, 2003.)
156 

Figure 27. CBR System Effects Object

(Source: Karim and Adeli, 2003.)
157 

Figure 28. CBR System Solution Set

(Source: Karim and Adeli, 2003.)

Matrix-Based Decision Support Tool 

Overview of Methodology/Tool 

The Matrix-Based Decision Support Tool was created through a study funded by the FHWA and 

Texas Transportation Institute to determine the most effective strategies or combination of 

strategies to support construction, traffic management, and public information during 

work zone activities in high-traffic environments.

(39) The research presents a series of 

decision-support matrices that include: 

• Preliminary strategy selection matrix;

• More detailed matrices focused on construction, traffic management, and public 

information strategies; and

• An interdependency matrix that considers synergy among multiple strategies.

The use of these three matrices lends to a three-step process. To develop this process, the 

authors used information from literature reviews, case studies, and opinions of experts. Using 

the literature review and case studies, they developed the Preliminary Strategy Selection Matrix. 

This matrix maps the observed successful use of various construction, traffic management, and 

public information strategies. Using the literature review and case studies, they also gathered 

information regarding the benefits of each strategy. Using the benefits information, they 
158 

developed the second matrix, the Secondary Strategy Selection Matrix. Separate matrices were 

developed for each of these three categories of strategies: construction, traffic management, and 

public information. Using the information sources, the researchers also observed that several of 

the strategies that were applied concurrently have synergic benefits. From the information 

regarding the different levels of synergy among strategies, the researchers created the third 

matrix, the Strategy Interdependency Matrix.

Preliminary Screening of Alternatives

Preliminary Strategy Selection Matrix

The Preliminary Strategy Selection Matrix presents various motivations or concerns related to 

construction, traffic management, and public information. It then identifies which strategies 

have been shown to mitigate or address such concerns using the three data sources previously 

mentioned. This matrix only identifies those strategies that positively address the associated 

motivations/concerns. Blank cells mean a negative or an unconfirmed positive relationship 

between the strategies and motivations/concerns.

Factor Prioritization and Criteria Weights

No factor prioritization methodology is necessary for this evaluation framework. However, the 

next strategy selection matrix aids the decision-maker to further reduce the set of strategies in 

consideration. 

Secondary Strategy Selection Matrix: Construction

There are three Secondary Strategy Selection Matrices: 1) construction; 2) traffic management; 

and 3) public information. This secondary matrix provides additional information regarding 

the relative impacts and benefits of each strategy and rates them as either a high, medium, 

or low impact. The Secondary Matrix categorizes strategies into the following: contract 

administration, planning/scheduling, project management, constructability, and construction 

practices. It uses project examples from the case studies and identifies which strategies apply to 

which projects. It also identifies the anticipated benefits of each strategy as it relates to:

• Enhanced communications/coordination;

• Project speed and efficiency;

• Construction quality; and 

• Work zone safety. 

Evaluation of Criteria Scores and Recommending an Alternative 

The three matrices work to decrease the initial set of strategies to those with a high level of 

relevance or significance to the project and the objectives. Additionally, the inclusion of the 

interdependency matrix also can provide insight into the optimal combination of strategies that 

could address the project objectives. 
159 

Strategy Interdependency Matrix

The third matrix, Strategy Interdependency Matrix, depicts the level of interdependencies 

between strategies. The interdependency levels are rated High, Medium, or Low. Similar to 

the Preliminary Strategy Selection Matrix, the Strategy Interdependency Matrix only identifies 

those combinations that have been shown to produce positive benefits. Therefore, blank cells 

reflect a disbenefit or an unconfirmed positive combination of strategies. 

Table 70. Pros and Cons of the Matrix-Based Decision Support Tool

Pros Cons

• Simple and comprehensible for technical analysts, 

planners, and policy-makers

• Provides insight into optimal combination of 

strategies

• Database of benefits and case studies of work zone 

strategies

• Requires a lot of data

• Data for all strategies may not be available

• Narrows down strategies and denotes relevant 

strategies, but does not specifically recommend an 

alternative(s)

Example Application 

Application of the matrices involves a three-step process that utilizes tables such as those shown 

in Figures 29 to 31.(39) The process is explained in further detail below. 

• Step 1 – Preliminary Identification of Candidate Strategies – Use the Preliminary 

Strategy Selection Matrix to identify the appropriate strategies to address project 

considerations in the areas of construction, traffic management, and public information. 

The items included in the matrix’s top column labeled “Motivations/Concerns” can be 

used to guide project considerations. 

• Step 2 – Further Investigation of Candidate Strategies – Use the Secondary Strategy 

Selection Matrix: Construction, Traffic Management, and Public Information Matrices

to obtain more information about the candidate strategies generated from Step 1. This 

step provides further insights into the benefits and disbenefits of each strategy. Using the 

information from this matrix, the user can further narrow down their list of strategies to 

those that would provide benefits over a certain threshold or impact level.

• Step 3 – Identification of Synergistic Opportunities Among Candidate Strategies –

After consolidating the initial list of strategies in Step 2, the third matrix, Strategy 

Interdependency Matrix, can be used to determine the optimal combination of strategies 

based on synergistic levels as a guide.
160 

Figure 29. Preliminary Strategy Selection Matrix
161 

Figure 30. Example Secondary Strategy Matrix: Construction
162 

Figure 31. Level of Interdependence Matrix
163 

6.0 RECONCILING INCONSISTENCIES AND

CONDUCTING SENSITIVITY ANALYSIS

Throughout the project development process, several factors can cause variations in performance 

measures and outputs used to compare and analyze the benefits and impacts of work zone 

alternatives.

• The first section of this chapter describes the typical variations that can be expected in the 

analysis outputs and measures of effectiveness extracted during various stages of a work 

zone traffic analysis.

• The second section describes several ways to reconcile the variations and inconsistencies 

that can occur in modeling or analysis results. 

• The third section describes how sensitivity analysis can be used to validate and rectify 

issues with the variations and inconsistencies resulting from the work zone modeling and 

analysis.

6.1 VARIATIONS IN THE MEASURES OF EFFECTIVENESS, TOOLS, AND OUTPUTS

The project type and scale can affect the structure, the level of data collection and analysis 

efforts, and decision-making framework that is incorporated into developing and 

evaluating the project and the project development process (PDP). Each PDP stage will be 

driven by a different purpose and objectives, which may require different types of analysis 

efforts and tools. Using different tools can yield differences in output results and variations in 

measures of effectiveness (MOE).

Table 71 presents common measures that can be extracted from different classes of analysis 

tools. The table shows that although the same type of performance measures may be collected 

from each analysis tool, the definition of the measure and the types of outputs needed in order to 

calculate the MOE may differ depending on the tool used. For instance, while both signal 

optimization and travel demand modeling tools can report delay, only signal optimization can 

report intersection delay effectively.

Table 71. Performance Measures and Outputs by Tool Type

Analysis Tools

Performance 

Measures Outputs Tool Focus 

Sketch-Planning –

Analytical/

Deterministic 

Tools

• Travel Time • Vehicle-Hours Traveled (VHT) Sketch-planning tools can 

be used for systemwide 

measures. Deterministic 

tools are better suited for 

facility-specific measures.

• Person-Hours Traveled (PHT)

• Delay • Intersection Total Delay

• Intersection Control Delay

• Stop Delay

• Queue • Freeway Facility Queue Density

• Percentile Probability Queues

• Speed • Average Speed 

• Space Mean Speed 

• Volume • HCM V/C Ratio

• User Costs • N/A
164 

Analysis Tools

Performance 

Measures Outputs Tool Focus 

• Density • For Freeway Facilities – Average Density by 

Segment or Across Facility

• Multilane Highways – Mean Passenger Car 

Density

• LOS • HCM LOS

Travel Demand 

Model Tools

• Travel Time • Total Travel Time Systemwide measures.

• Delay • N/A

• Queue • N/A

• Speed • Average Speed

• Volume • V/C Ratio

• Link Volumes

• User Costs • N/A

• Density • N/A

• LOS • N/A

Traffic Signal 

Optimization Tools

• Travel Time • Total Travel Time Facility-specific 

• Delay • Total Delay measures. 

• Control Delay

• Stop Delay 

• Queue Delay

• Queue • Average Queue

• Maximum Queue

• 50th and 95th Percentile Queue

• Speed • Average Speed

• Volume • HCM V/C Ratio

• User Costs • Fuel Consumption 

• Fuel Economy

• Density • N/A

• LOS • HCM and Intersection Capacity Utilization 

(ICU) LOS

Macroscopic 

Simulation Tools

• Travel Time • Total Travel Time Facility-specific.

• Delay • Total Delay

• Delay by Facility Type (i.e., Freeway, 

On-Ramp, and Off-Ramp)

• Queue • Congested Length or Queue Length

• Speed • Average Speed

• Volume • Volume (Expressed as Flows at an Hourly 

Rate)

• V/C Ratio

• User Costs • Fuel Consumption

• Density • Average Density (Vehicles per Mile per 

Lane)

• LOS • LOS (Calculated as a Function of Density)

Mesoscopic 

Simulation Tools

• Travel Time • Travel Time, Trip Time (VHT) Facility-specific.

• Delay • N/A

• Queue • Entry Queues

• Vehicle Queue Length

• Speed • System Average Speed

• Average Link Speed (mph)

• Volume • Number of Vehicles
165 

Analysis Tools

Performance 

Measures Outputs Tool Focus 

• User Costs • N/A

• Density • Link Density (Average Passenger Car/LaneMile)

• Mean Passenger Car Density of Moving 

Vehicles

• LOS • N/A

Microscopic 

Simulation Tools

• Travel Time • System- and Link-Based VHT Facility-specific.

• Total Time per Vehicle

• Delay • Average Delay (by Link, Node, and 

Network-Wide)

• Delay Time or Lost Time (the Difference 

between the Theoretical Travel Time versus 

Actual Vehicle Travel Time) per Vehicle

• Delay Time Rate (Minute/Mile)

• Queue Time 

• Stop Time, Stopped Delay, Average 

Stopped Delay

• Total Delay (Network-Wide)

• Queue • Average Queue Length

• Maximum Queue

• Percent of Link Storage 

• Speed • Average Speed by Link 

• Network-Wide Average Speed

• Volume • Volume Count and Flow (Vehicles per 

Hour)

• User Costs • Fuel Consumption

• Density • Average Density per Link Segment during 

Analysis Time Period (Vehicles/Lane-Mile)

• LOS • Most microsimulation tools do not report 

LOS. Paramics Analyzer reports HCM LOS 

using Passenger Car Unit (PCU) Densities

Different tools also are better suited for certain levels of analysis. The levels of analysis include 

systemwide and facility-specific. Systemwide measures report mobility, safety, and 

environmental impacts for the entire system or network while facility-specific focus either on a 

specific link, localized area, or facility type. For instance, macroscopic models and travel 

demand models are best suited for systemwide measures. Due to the level of detail and 

complexity involved in microscopic simulation analysis, microscopic simulation tools, while 

capable of generating systemwide MOEs, are best utilized for reporting measures at a more 

localized or facility-specific level than evaluating larger systems.

The project characteristics, goals, and how the PDP is structured will affect and be impacted by 

the types of tools, measures, and outputs generated. The following section describes how to 

reconcile these issues to aid in better decision-making. It also will feature an example that 

depicts how variations in MOEs can occur due to the use of different analysis tools, as well as 

how to reconcile those inconsistencies.
166 

Variations Based on Project Type 

The types of tools utilized during a work zone traffic analysis can vary based on the 

characteristics of the project and the project site. Different analysis tools may yield different 

outputs and performance measures.

One of the major project characteristics that can impact the type of tools and MOEs used on 

projects include the project’s size.

• Small-scale, site-specific projects may have a more concentrated impact, a shorter 

construction and project development period, and potentially more limited resources. 

These projects may utilize sketch-planning and HCM/deterministic models, where the 

analysis could be done simply and quickly, with a smaller-scale data collection effort. 

Example measures of effectiveness (MOE) for a smaller scale project may, therefore, be 

at a facility level.

• Larger, systemwide projects have wider-range impacts, with longer durations, and 

potentially more resources. These projects, due to their size, will require tools that can 

handle larger networks and a range of measures that include mobility, safety, and 

environmental factors. The performance measures would most likely be at a systemwide 

level.

The goals and objectives of the project along with the work zone characteristics also will dictate 

what tool may be used and consequently what types of outputs and measures can be generated. 

If the project’s main objectives, for instance, require that the site be maintained at a certain level 

of service, the type of tools and MOEs that an agency may choose will be different if the 

objectives focus on issues related more to safety. 

Variations Based on Project Stage 

Each project stage will have a different objective and timeframe for when an action or decision 

has to occur. Specific tools may be more applicable for certain objectives and timeframes. 

Therefore, different tools or combination of tools may be utilized for various stages of the project 

development process (PDP). The following describes the types of variations in MOEs, tools, and 

outputs that occurs during specific phases of the project. 

• Project Planning and Scoping – At the project planning stage, agency staff and 

decision-makers define and prioritize the goals and objectives of the project. Therefore, 

at the planning stage, these evaluators will typically look at measures at a systemwide 

level. Tools that may be used include sketch-planning tools and travel demand models. 

• Environmental, Preliminary Design/Project Approval Stage – The objectives at this 

stage of the project are to develop the preliminary design and operations plan, obtain 

environmental clearance, and receive approval to proceed with the next stage. At this 

stage, a preliminary design and list of potential alternatives will be evaluated for the 

project. The short- and long-term benefits and traffic and environmental impacts of the 

project also will be determined. The measures presented at this stage of the project will 
167 

be targeted for decision-makers and the public. In order to determine the benefits and 

impacts of the project, several different tools may be used depending on the size and 

complexity of the project.

• For broad- and system-level performance measures, travel demand models supported 

by sketch-planning tools may be appropriate. 

• For more complex analysis that may look at the performance of a specific facility or 

the performance of major highways along with adjacent arterials, a signal 

optimization model, mesoscopic, and/or microscopic simulation tools may be used. 

• Plans, Specifications, and Estimates (PS&E), Construction, and Operation Analysis 

Stage: 

• During the PS&E phase of the project, the agency prepares design plans and project 

specifications. At this stage, work zone traffic analysis is conducted in order to 

support the project design. The traffic analysis results ensure that the project will 

have the capacity to meet or service the demand for the facility.

• During construction, the agency’s objectives in conducting any modeling or analysis 

effort are to determine how to safely and efficiently construct a project with minimal 

impacts to the environment, the community, and workers. Modeling and analysis 

efforts for the construction stage are focused less on mobility and more towards 

evaluating how effective the project or work zone strategies are at improving safety 

and or project/construction efficiency. 

• During the operations stage, agency objectives are focused on evaluating the level of 

service of the facility and the traffic performance of the work zone. The types of 

measures at this stage include mobility measures such as delay, average speed, and 

queue lengths. At this stage, a larger data collection effort and more complex 

modeling and analysis tools may be brought in. 

• Alternatives Analysis – When choosing among work zone alternatives, the analyst will 

consider the various performance measures generated in the different project stages in 

order to make a decision regarding which alternative to recommend. 

6.2 ACHIEVING CONSISTENCY OF RESULTS WHEN USING MULTIPLE TOOLS

The previous section discussed the variations in measures of effectiveness, tools, and outputs that 

an analyst can encounter when conducting a work zone traffic analysis. These variations can 

cause inconsistencies in results, inaccuracies in calculating benefits and impacts, and 

misinformation in deciding among various alternatives. This section describes the need for 

consistency when using different analysis tools and reconciling the inconsistencies that may 

occur with different modeling outputs and measures of effectiveness. This section will provide 

background on consistency within the project development process, describing how 

inconsistencies occur and the importance of achieving consistency in outputs and measures. The 

section also will describe how to evaluate and ensure consistency throughout the various project 

stages.

Background on Consistency

“The goal of achieving or maintaining consistency throughout the project development process 

(PDP) ensures that the initial decision to proceed with the project is still the correct choice at the 
168 

end of the process.”(40) There are two resources that describe the potential variations and 

inconsistencies in MOEs and analysis results when using multiple tools. One resource is the 

FHWA-sponsored research, Guide on the Consistent Application of Traffic Analysis Tools, and 

the other is Traffic Analysis Toolbox Volume VI: Definition, Interpretation, and Calculation of 

Traffic Analysis Tools Measures of Effectiveness.

(40,41) These two resources provide guidance on 

how different types of analysis tools produce particular MOEs and how to reconcile the potential 

inconsistencies that may result from using multiple tools. For further details on many of the 

concepts addressed in this section, please refer to these two documents. 

Achieving consistency in terms of outputs, performance measures, and results is important for 

the following reasons: 

• Consistency ensures quality and accuracy of results; 

• Consistent and accurate results leads to verifiable decision-making process and outcomes; 

• Consistency enhances the agency’s credibility and effectiveness;

• Consistency assures that the analysis process is effective and efficient; and

• Consistency ensures that the analysis procedures are more refined and flexible enough to 

be reapplied even through certain parameter changes.

As mentioned in the previous section, variations in MOEs, tools, and outputs can occur naturally 

depending on the project type and project development stages. When different tools and types of 

performance measures are used throughout one project, inconsistency in results can occur. 

Additionally, inconsistency may occur due to the following factors:(41) 

• Overuse of defaults;

• Calibration mistakes;

• Inaccurate data or demand forecasts;

• Failure to check for reasonableness of results; and

• Differences in performance measure definitions across different tool types. 

Reconciling Measures of Effectiveness

As discussed in Section 6.1 of this document, variations in MOEs can occur at different project 

stages and through the use of different analysis tools. The use of different tools can produce 

inconsistencies in MOE results and challenges when comparing measures across different 

analysis tools. In order to reconcile the potential inconsistencies that can result from the 

use of different tools, the analyst should keep in mind the characteristics of the tool – its 

strengths and limitations. For further information on the capabilities and limitations of 

different traffic analysis tools, refer to Chapter 3 of this document. In order to reconcile the 

variations in MOEs that occur from the use of different tools, the analyst should account for the 

following when choosing the appropriate tool: 
169 

• Choose the tool most reliable for extracting the outputs needed to generate the 

performance measures established.

• Choose the tool that is most appropriate for producing MOEs by the focus level (i.e., 

facility- or system-specific) required for the particular project stage.

• If MOEs need to be compared across tools, find the least common denominator amongst 

the two. According to the FHWA-sponsored Traffic Analysis Toolbox Volume VI: 

Definition, Interpretation, and Calculation of Traffic Analysis Tools Measures of 

Effectiveness, “the comparison of results between tools and methods is possible only if 

the analyst looks at the lowest common denominator shared by all field data collection 

and analytical tools.”

(41)

 In this guide, vehicle trajectories are recognized as the lowest 

common denominator. However, the NCHRP Report 3-85-20 notes that because vehicle 

trajectories may be defined differently across different tools, the lowest common 

denominator is field observations. Because analysis tools and models should be 

calibrated or validated by measures from the field, field observations serve as a common 

link across different tools and methodologies.(42)

Reconciling Different Tools

There are several ways to reconcile the variations or inconsistencies that may be present in 

analysis results due to the utilization of different tools during the project development process. 

• When utilizing results from different tools, the most reliable tool should always take 

precedence over the results from the less reliable ones. For instance during the project 

planning stage of an MOTAA, a travel demand model coupled with a sketch planning 

may be used to determine the potential work zone alternatives to be considered for a 

project.

• When possible the same tool should be used throughout the various project stages. 

For instance, if a microsimulation model was developed during the environmental 

clearance stage, that model can be carried over to subsequent project stages, especially 

during the alternatives analysis.

• While the results from the more reliable or robust tool may be used, the analyst 

should still review the outputs from the less reliable tools to QA/QC the preferred 

tool’s results. One way to do this is to use another analysis tool’s results to determine 

whether common trends appear in both models. For instance, when comparing the 

mobility impacts of a ramp closure, a traffic optimization tool may note that nearby 

parallel signalized arterial intersections experience a degradation of LOS from an LOS B 

to an LOS D. A microsimulation tool may express this degradation in mobility levels 

with measures such as delay, speed reduction, or queue length. Although the values and 

measures are different, the two tools should agree that there are congestion issues at that 

particular section of the project area.

• Further checks on the modeling and analysis tool results should include error 

checking of the model network coding, volumes, and speeds by comparing the model 

inputs and outputs with observed traffic statistics. 
170 

Reconciling Outputs

When different tools must be used during the project development process, inconsistencies 

amongst model outputs are likely to occur. The following presents various ways to reconcile 

these differences:(40) 

• The tool that is used to report the facility-specific performance analysis should be 

used to report all facility performance results. For instance, if CORSIM was used to 

generate facility travel times, it also should be used to identify bottlenecks, queues, 

speeds, and delays for the facility. This would be the same for system-specific 

performance measures.

• When different tools have similar outputs but are defined differently, the analyst 

should be able to compare and understand the differences between the tools. 

• If neither tool provides the best or most complete solution to a problem, the analyst may 

want to consider the following: 

• Additional data processing may be needed to reconcile the differences by using 

revised outputs that utilize a combination of the outputs from the different tools. 

• It may be necessary to convert one tool’s outputs so that it can be compared at 

the same level as the other tool. 

Reconciling Variations and Inconsistencies Example: I-15 Ontario Corridor Pavement 

Reconstruction Project

An example of a work zone project that used different tools throughout the various stages of the 

project was the I-15 Pavement Reconstruction Project in Ontario, California. This work zone 

project required the reconstruction of two of three truck lanes in each direction of the interstate. 

For further details regarding the project refer to the Traffic Analysis Tools Volume IX: Work 

Zone Modeling and Simulation – A Guide for Analysts.

(2) Figure 32 shows the analysis process 

for I-15 project and presents when each analysis tool was used and for which stage of the project. 

Table 72 shows the various tools, measures, and outputs utilized throughout various stages of the 

project.
171 

Figure 32. I-15 Analysis Process

(Source: Hardy and Wunderlich, 2009.)

Table 72. MOEs, Tools, and Outputs Utilized for 

I-15 Pavement Reconstruction Case Study

Phase Stage MOEs Analysis Tool Outputs

1 • Planning/Scoping 

(Preconstruction 

Plans)

• Systemwide measures • Sketch-Planning –

CA4PRS

• Total number of lane 

closures

• Alternatives 

Analysis 

(Preconstruction 

Plan)

• Facility-specific 

(delays reported for 

the entire corridor but 

only one facility type, 

freeway involved in 

analysis)

• HCM DemandCapacity Model• Total and max delay; 

road user cost

2 • PS&E and 

Operational 

Analysis

• Systemwide

• Facility-specific

• Sketch – CA4PRS

• Dynameq

• Staging analysis plan 

(number of weekends 

needed, working days, 

etc.)

• Delay in minutes 

(reported by 

segmentation defined 

by the six construction

staging scenarios)

The I-15 project used three traffic modeling tools, including the Construction Analysis for 

Pavement Rehabilitation Strategies (CA4PRS), Highway Capacity Manual Demand-Capacity 

Model (HCM), and Dynameq mesoscopic traffic simulation tool. During the preconstruction 

planning, alternatives development, and initial alternatives analysis portion of the project, the 
172 

analysts utilized CA4PRS and HCM to analyze six alternative construction scenarios. Table 73 

shows the preconstruction analysis results from HCM and CA4PRS. As shown by the results, 

Scenario 1 was the most cost-effective and was, therefore, chosen as the preferred alternative.

Table 73. I-15 Preconstruction Analysis Results 

Scenario Name

Closure 

Scheme

Closure 

Assessment 

(Number of 

Closures)

Traffic Analysis

Cost 

Analysis

Total

Costa

RUC

(Million 

Dollars)

Delay 

(Minutes)

Original Median and

structure 

widening

35 weekends 3 16 78 79

Rapid Rehab 1 Full closure 

one roadbed

35 weekends 119 363 83 123

Rapid Rehab 2 Full closure 

one roadbed

8 weeks 123 363 77 118

Traditional Partial closure 1,220 nights 133 22 88 113

Long-Life CSOL Full closure 

one roadbed

20 weekends 69 363 60 83

a Total Cost = (RUC/3) + Cost Analysis. 

The second phase of the project entailed the development of a construction staging plan and 

traffic management plan. The agency, therefore, had to conduct further analysis of the preferred 

alternative in order to develop a construction staging plan that optimized construction schedule 

and resources while minimizing impacts to the public. CA4PRS was used to develop the more 

detailed construction staging plan of the preferred alternative and mesoscopic simulation tool, 

Dynameq, was used to evaluate the mobility impacts (delay) associated with the staging plan 

options. The final six construction staging scenarios included the following: 

• 2B – Closure of I-10W to I-15S and I-15S to Jurupa ramps; 

• 2C – Closure of I-10E to I-15S ramp; 

• 2D – Closures of I-15S to I-10W and from 4th Street to I-15S ramps; 

• 3D – Closures of Jurupa to I-15N and I-15N to I-10E and I-10W ramps; 

• 3F – Closure of EB I-10 to NB I-15 connector and both NB ramps at 4th Street; and

• 4B – Closure of SB I-15 to WB and EB SR 60. 

The following describes how each tool was used: 

• CA4PRS is a sketch-planning tool that was used in the preconstruction analysis and 

planning stage in order to identify rehabilitation strategies such as closure schemes that 

optimized construction production, schedule, and minimized inconvenience to the public. 

It was used in the second phase to analyze the preferred alternative and develop a more 

detailed staging analysis plan. CA4PRS also was chosen because results from this tool 

were easily integrated into traffic simulation tools for the calculation of road user costs.

• HCM Demand-Capacity Model (HCM) was used to analyze the impacts of the work 

zone closure strategies identified through CA4PRS. Using this model, the analysts were 
173 

able to calculate total and maximum delays as well as road user costs. The California 

Freeway Performance Measurement System (PeMS) was used to estimate traffic demand.

• Dynameq, a mesoscopic simulation modeling tool, was used to assist in the development 

of the transportation management plan (TMP). Dynameq was used to evaluate the delays 

stemming from the six staging scenarios. It also was used because it incorporated the 

impacts of detour routes in generating the delay calculations.

Although different tools were utilized during the various stages of the project, the agency 

managed to achieve consistency by following some of the concepts stated earlier in this chapter: 

• Choose the tool most reliable for extracting the outputs needed to generate the 

performance measures established. 

• The agency needed a simple analysis using historical work zone data that could guide 

them towards appropriate work zone and lane closure strategies. Sketch-planning 

tools, specifically, CA4PRS was best used for determining appropriate work zone 

strategies based on data from projects with similar characteristics and particular 

traffic conditions and scenarios. 

• Since CA4PRS did not have the appropriate modules for conducting HCM 

calculations at the time, the agency made the appropriate choice to use another tool, 

HCM demand-capacity model, order to calculate the necessary mobility measures 

needed to evaluate and compare the lane closure alternatives.

• In order to determine appropriate detour routes, the analysts needed the use of a 

traffic modeling tool that analyzed diversions and route shifts. The agency chose the 

appropriate tool that would generate these performance measures, mesoscopic tool 

Dynameq.

• Choose the tool that is most appropriate for producing MOEs by the focus level. 

• One example of how the agency followed this concept is through their choice of using 

a mesoscopic model. Since the traffic analysis for the project was for a larger study 

area, it might have been appropriate to use a macroscopic model. However, because 

of the number of detour routes involved in the analysis, macroscopic could not be 

used and microscopic simulation would be too resource intensive and complex for a 

study area of this size. The analysts, therefore, chose the most appropriate tool, 

mesoscopic, for the size of the project area and the scope of the analysis.

• Further checks on the modeling and analysis tool results should include error 

checking of the model network coding, volumes, and speeds by comparing the model 

inputs and outputs with observed traffic statistics. 

• PeMS was used to verify the travel demand forecasts.

6.3 SENSITIVITY ANALYSIS

The previous sections discussed the potential for inconsistencies with the analysis results and 

described how to reconcile those differences. Because of the dynamic nature of traffic 

conditions, sensitivity analysis can be used for verifying the quality and accuracy of results as 

well as providing further insight into acceptable ranges of results based on different traffic 

conditions and scenarios. This section is structured to provide an introduction to sensitivity 

analysis and its role within a work zone traffic analysis. The subsequent parts of the section 
174 

provide an overview of how and when to use sensitivity analysis during different stages of the 

project, such as during: 

• Demand forecasting and capacity analysis;

• Model validation and calibration; and 

• Alternatives analysis – decision-making and performance monitoring. 

Each section describes how sensitivity analysis can be used for that particular stage. Case 

studies and examples explain the key concepts for applying such analysis tools and methods. 

Introduction and Purpose of Sensitivity Analysis

Sensitivity analysis is a quantitative methodology that can be employed to do the following:

• Reconcile the variations in MOEs and analysis results; 

• Determine the robustness of a result; 

• Support recommendations or decisions; 

• Identify critical values or solutions;

• Better understand the relationship between different variables, inputs, and outputs; 

• Identify potential errors; and

• Improve and ensure accuracy of a model.

How a sensitivity analysis is conducted or structured can differ depending on its purpose and the 

resources available to conduct the analysis. There are some common steps involved in most 

sensitivity analysis. These steps include the following: 

• Defining the objective of the sensitivity analysis – answering the question “What to test?” 

• Determining which parameters to vary and which to remain constant. 

• Determining what to observe. 

• Determining which analysis outputs are relevant to the objective.

• Determining the design of the analysis – such as the combination of parameters, which 

parameters to vary, when they should change, and by how much. 

Sensitivity Analysis in Demand Forecasting and Work Zone Capacity Analysis 

This section provides an overview of how sensitivity analysis can be used to further support 

results from demand forecasting as well as work zone capacity analysis. As with most analysis 

tools there are limitations and gaps in a travel demand model’s capability to capture the potential 

impacts of work zone alternatives and mitigation strategies (i.e., travel demand management, 

intelligent transportation systems, and traveler information strategies), as well as the effects of 

certain scenarios and travel conditions (i.e., normal peak and off-peak conditions, incident, 

special events, and work zones). 

All forecasts are subject to uncertainty, as minor changes to certain model parameters can 

significantly impact the demand and capacity results of a model. It also can impact the measures 
175 

derived from demand and capacity such as the project’s mobility (delay, queue, and speed) 

impacts, benefits, and costs. Therefore, it is important to understand the potential variations in 

demand forecasts and capacity estimations, especially when using such to estimate other impacts 

and benefits. 

Sensitivity analyses can be conducted to validate and support the methodologies used to estimate 

work zone traffic demand and capacity. It also can aid in identifying the factors that impact 

demand and capacity, as well as the appropriate demand and/or capacity ranges that could be 

expected for particular work zone projects and alternative scenarios. The following provides 

examples describing how sensitivity analyses can be used to support demand and capacity 

measures for work zones. 

Demand/Capacity Scenarios – Q-DAT

In one sensitivity analysis approach, the Texas Transportation Institute (TTI) utilizes their 

developed tool, Q-DAT, to evaluate potential work zone alternatives by varying demand and 

capacity levels to determine how they impact delay and queues in the project area. In 

Q-DAT, the delay and queue estimation function of the tool calculates delay per vehicle and 

average queue length for three scenarios with varying traffic volumes and work zone capacities, 

“Expected Case,” “Best Case,” and “Worst Case.” 

• Expected Case – In the “Expected” case, the traffic volumes and work zone capacity are 

based on user input. These could be based on experience, historical information, or field 

observations. 

• Best Case – The “Best Case” reflects traffic volumes that are 10 percent lower and work 

zone capacity 10 percent higher than the user inputs. 

• Worst Case – The “Worst Case” scenario is based on traffic volumes that are 10 percent 

higher and work zone capacity that is 10 percent lower than the user input. 

Analyzing the variations in queue and delay impacts of these three scenarios serves as a 

sensitivity analysis for determining how varying demand and capacity levels impact mobility 

within a work zone. By evaluating a work zone project based on these three scenarios, an 

agency can have a wider spectrum or range of potential mobility impacts that could be expected 

at the work zone. Such information can be used to shape mitigation strategies as well as 

potential work zone alternatives (i.e., nighttime construction, partial and full lane closures, and 

detours) they may consider. 

Variations in Traffic Demand and Capacity Parameters Due to Specific Travel Conditions 

Sensitivity analyses can be used to determine appropriate demand and capacity values for 

various traffic conditions and scenarios. One example of this approach is an FHWA 

sponsored research report, Development of Highway Capacity Manual Methodologies To Assess 

Advanced Traffic Management Strategies That Influence Traffic Demand, which presents a 

methodology using different demand/capacity scenarios to develop travel time estimates that can 

be used to support the analysis of active transportation and demand management (ATDM) 

strategies’ impacts.(43)

 To predict the variability of demand in a project area, historical demand 
176 

data is collected. The demand scenarios utilized in the report include 5 percent, 20 percent, 50 

percent, 80 percent, and 95 percent demand scenarios. Additionally, capacity scenarios are 

generated. The capacity scenarios are constructed from historic data on incident and weather 

data for the facility (or facilities). After evaluating various scenarios with different combinations 

of capacity and demand values, the analyst determines a set of demand and capacity adjustment 

factors as shown in Tables 74 and 75.(43) These factors can be applied to the original model 

volumes and demands to adjust for particular mobility conditions. 

Table 74. Example Scenario Demand Adjustment Factors

Demand Factors Demand Scenarios

Capacity Scenarios

5th

Percentile

20th

Percentile

50th

Percentile

80th

Percentile

95th

Percentile

Fair Weather, No Incidents 0.88 0.93 0.96 0.99 1.01

Fair Weather, Single Lane Closed 0.88 0.93 0.96 0.99 1.01

Fair Weather, 2+ Lanes Closed 0.88 0.93 0.96 0.99 1.01

Poor Weather, No Incidents 0.88 0.93 0.96 0.99 1.01

Poor Weather, Single Lane Closed 0.88 0.93 0.96 0.99 1.01

Poor Weather, 2+ Lanes Closed 0.88 0.93 0.96 0.99 1.01

(Source: Dowling, 2011.) 

Table 75. Example Scenario Capacity Adjustment Factors

Capacity Factors Demand Scenarios

Capacity Scenarios

5th

Percentile

20th

Percentile

50th

Percentile

80th

Percentile

95th

Percentile

Fair Weather, No Incidents Present 1.00 1.00 1.00 1.00 1.00

Fair Weather, Single Lane Closeda 0.58 0.58 0.58 0.58 0.58

Fair Weather, 2+ Lanes Closeda 0.25 0.25 0.25 0.25 0.25

Poor Weather, No Incidents Present 0.93 0.93 0.93 0.93 0.93

Poor Weather, Single Lane Closeda 0.51 0.51 0.51 0.51 0.51

Poor Weather, 2+ Lanes Closeda 0.18 0.18 0.18 0.18 0.18

(Source: Dowling, 2011.) 

a Capacity Adjustment Factor = 1.00 – Proportion Capacity Reduction.

For incidents, capacity adjustment factor is applied only to section and time slices where and when incident is 

present. For weather, capacity adjustment factor is applied to all sections and time slices.

This methodology allows analysts to evaluate the impacts and benefits a variety of ATDM 

strategies that may be considered to mitigate the impacts of various work zone configurations 

and construction alternatives. This also ensures that the analyst’s model of the work zone is 

calibrated to the specific demand and capacity values expected in the field for particular 

scenarios. Adjustments of the demand and capacity values enable analysts to attain improved 

confidence for their calculated travel times and other performance measures.

Travel Demand Diversion

A sensitivity analysis also can be used for estimating and/or evaluating trip diversion due to 

the work zone. One such tool, NETZONE, has the ability to estimate demand diversion based 
177 

on different work zone project types and strategies. In NETZONE, the demand diversion model, 

which estimates the reduction in total demand and the diversion to other routes due to the 

construction work, is developed based on data from multiple work zone projects under different 

travel conditions (i.e., weekday, weekend, peak, off-peak, etc.) in California.(44) 

With NETZONE an analyst may have to apply the demand diversion model. Several scenarios 

can be run using the demand diversion model. NETZONE was run on a case study for the SR 41 

corridor network in Fresno, California. The three scenarios analyzed included: 

• Scenario I – Before construction;

• Scenario II – During construction (do nothing scenario); and 

• Scenario III – During construction with pre-trip information and media campaign. 

The results of the demand diversion model provide general statistics about the facility or project. 

These statistics include total cost, delay, average cost, and maximum delay, as shown in 

Table 76.(44) This analysis can be used to gain further insight on how variations in demand affect 

mobility measures and costs. Additionally, this analysis also can be used to compare and select 

among various alternatives or scenarios.

Table 76. NETZONE Example General Statistics

Scenario

Total Cost 

(Hour)

Total Delay 

(Hour)

Average Cost 

(Hour)

Maximum Delay

(Hour)

I 11,560 3,796 0.0049 0.1783

II 13,303 5,232 0.0054 0.9083

III 12,378 4,659 0.0052 0.9156

(Source: Zhang, Shen, Nie, and Ma, 2012.)

Sensitivity Analysis in Model Validation and Calibration 

This section provides an overview on the use of sensitivity analysis to verify model outputs, as 

well as the accuracy or credibility of the model results. The sensitivity analysis also can be 

used to identify potential errors in model analysis outputs and/or MOEs.

The FHWA Report Traffic Analysis Toolbox Volume III noted that a sensitivity analysis was 

recommended to assess the reliability of microsimulation results.(29) The sensitivity analysis for 

this effort could include varying certain model parameters or changing the demand levels to 

determine what the impact may be on the simulation results. Some examples of its use include 

determining driving behavior parameter values that will best fit the project and the area. Impacts 

can be compared to field observations to verify what the parameter values should be. As a result 

of the sensitivity analysis, parameters can be customized to the work zone site characteristics, 

instead of using defaults. This helps to ensure the accuracy of the model outputs and analysis 

results.
178 

VISSIM Example

A study by Chatterjee, et al. (2009) from the University of Missouri-Columbia involved a 

sensitivity analysis testing combinations of VISSIM model parameter values in order to 

determine the appropriate values for particular work zone configurations.(45) The study evaluated 

combinations of three different parameter values for several work zone configurations such as a 

two- to one-lane work zone and a three- to two-lane work zone. The three parameters included: 

• CC1 – Desired time headway. This parameter was evaluated between the values of 0.9 

to 1.8 seconds in increments of 0.1 second. 

• CC2 – This parameter corresponds to the threshold that restricts longitudinal oscillation 

beyond a certain safety distance that varied. The parameter was evaluated between the 

values of 15 to 60 feet at increments of 5 feet.

• Safety Distance Reduction (SRF) Factor – This parameter refers to the reduction in 

safety distance to the trailing and leading vehicle in the desired lane and the safety 

distance to the leading vehicle in the current lane. This parameter was evaluated from the 

default value of 0.6 to 0.15 at increments of 0.05. 

Scenarios of different combinations of the three parameters for each of the work zone 

configurations were generated and modeled using VISSIM (version 4.3). Note that the 

parameters and their values may vary in more recent versions; however, the following 

methodology should still be applicable for determining work zone capacity. Outputs such as 

occupancy and delay performance were collected to compare the different scenarios. The 

scenarios or combinations where model occupancy volumes exceeded the occupancy thresholds 

(determined through field occupancy data) were discarded from the analysis results. The end 

product of this sensitivity analysis included look-up tables that show the appropriate parameter 

values for different work zone configurations. In order to use the look-up tables, an analyst must 

first determine where their project or work zone alternative fits on a lane distribution plot based 

on the percentage of trucks in the field and work zone configuration. In this plot, an analyst 

would find the index number that best corresponds with the estimated capacity and percent of 

traffic on the right lane of their project. An example lane distribution plot is shown in 

Figure 33.(45) If an agency has a 2 to 1 configuration with a 5 percent truck percentage on the 

field, their analyst(s) can use this figure to determine the appropriate model parameter values to 

use when conducting the simulation. If the project area has a proportion of 11 percent for right 

lane traffic and a capacity of 1,550 vphpl, the lane distribution plot shown in Figure 33 shows 

that the index number associated with their project is number 19. 
179 

Figure 33. Lane Distribution at 1,000 Feet Upstream of Taper for Five Percent

Trucks in a Two to One Configuration 

(Source: Chatterjee, Edara, Menneni, and Sun, 2009.) 

In the next step, the analyst would then refer to the look-up tables shown on Figure 34 to find the 

corresponding CC1, CC2, and SRF factor parameter associated with index number 19.(45) 

According to the table, the parameter values associated with this example project are: 1) CC1 of 

1.4; 2) CC2 of 40; and 3) SRF factor of 0.55. 

Estimated Capacity (vph)

% of Traffic in the Right Lane
180 

Figure 34. Parameter Combinations for Two- to One-Lane Configuration 

(Source: Chatterjee, Edara, Menneni, and Sun, 2009.) 

Alternatives Analysis – Decision-Making and Performance Monitoring

This section describes how sensitivity analysis can be used in the decision-making process 

for choosing among different work zone alternatives or for determining which strategies 

and/or mitigation measures can be used to maintain a certain level of mobility across the 

work zone area. The following lists examples of these uses: 

• Sensitivity analysis could be used to determine the impacts of varying the number of 

lanes closed, extent of closures, and the duration of construction; 

• Sensitivity analysis also can be used to determine the optimal methods for dissemination 

of travel information, as well as for evaluating the impacts of this mitigation strategy on 

mobility; and

• Sensitivity analysis can be conducted to determine the impacts if traveler information was 

disseminated within 5 minutes of an incident occurring at the work zone versus 10 

minutes or 15 minutes.

For choosing amongst alternatives, sensitivity analysis can be used to: 

• Determine a threshold or range that the project must meet in order to achieve the 

construction schedule or financial objectives; and

• Determine the sensitivity of total project costs to various measures and project 

characteristics such as traffic flow rates and work zone length. 
181 

An example of how sensitivity analysis can be used for determining optimal work zone 

alternatives is a study by Tang and Chien (2009) where the researchers developed a model for 

optimizing work zone schedules jointly considering the time-varying traffic diversion, variable 

maintenance cost, and production rate of different maintenance crews.(46) The authors’ model 

identifies the total cost (agency and user cost) associated with different combinations of these 

factors. An agency may use such a model to plan maintenance activities cost-effectively and 

choose among different work zone alternatives. This model optimizes a work zone schedule 

based on two modules: 

1. Diversion module that determines the time-varying traffic diversion; and 

2. Genetic Algorithm (GA) module that uses the redistributed traffic flows from the Diversion 

module in order to generate improved work zone schedules and update the initial/previous 

work zones schedules for the next iteration.

The work zone schedule goes through several iterations of Modules 1 and 2 until an optimal 

schedule is achieved when a threshold, in this case the minimized total cost, is reached. 

This approach was applied on a resurfacing project in New Jersey. The analysis served to 

analyze different combinations of scenarios, including the following: 

• Scenario A without traffic diversion; and 

• Scenario B with traffic diversion.

Additionally, the analysis applied four potential maintenance crews with varying maintenance 

costs and production times to the two scenarios, which brought the total number of scenarios to 

eight as shown in Figure 35.(46) The table also summarizes the combined effects of traffic 

diversions and different construction methods on the project total cost. 

Figure 35. Summary of Cost Comparison for Scenarios and Maintenance Crews 

(Source: Tang and Chien, 2010.)
182 

After the scenarios were developed, a sensitivity analysis was conducted in order to evaluate the 

benefit of traffic diversion by varying the mainline AADT (AADTm) incrementally from 30,000 

vpd to 60,000 vpd for both Scenarios A and B. The minimized total costs associated with the 

sensitivity analysis are shown in Figures 36 and 37. The sensitivity analysis showed that in 

comparing maintenance crews 1 and 2 (Figure 36), diverting traffic proved to be the most 

economical strategy if less productive maintenance crews were employed for work zones on 

heavily traveled highways. However, it also showed that a more productive maintenance crew 

would be just as cost-effective if traffic was not able to divert to alternate routes.

Figure 36. Sensitivity Analysis Results for Crews 1 and 2 

(Source: Tang and Chien, 2010.) 
183 

Figure 37. Sensitivity Analysis Results for Crews 3 and 4 

(Source: Tang and Chien, 2010.) 
184 

7.0 EVALUATING FACTORS FOR MOTAA RECOMMENDATIONS

This chapter presents a mix of quantitative and qualitative factors that should be considered in a 

Maintenance of Traffic Alternatives Analysis (MOTAA), in addition to the mobility-based 

performance measures generated through the traffic modeling analysis. The first section of this 

chapter describes the various factors and details how they can be incorporated into the evaluation 

and comparison of alternatives. The second section is dedicated to measures of safety in relation 

to a work zone traffic analysis. Typical work zone safety analysis utilizes historical crash data to 

measure the safety impacts of the work zone, as well as to identify potential mitigation measures 

that minimize safety impacts. This section also describes an additional form of measuring the 

safety impacts of projects through surrogate measures of safety, and how to generate the safety 

measures, and where these considerations fit into the MOTAA process. 

7.1 WORK ZONE KEY FACTORS

While modeling analysis tools produce certain measures that could be used to evaluate and 

compare work zone alternatives, there are additional qualitative and quantitative measures that 

may not be easily captured by such tools. This section describes how these key factors can be 

used to support the development and evaluation of work zone alternatives. 

This section is structured to present the key factors into two categories: 1) development of 

potential work zone alternatives; and 2) evaluating and comparing work zone alternatives. 

Subfactors within the first category include those related to traffic operations, public information 

and awareness, and construction and contracting options. Subfactors within the category of 

evaluating work zone alternatives include environmental and land use impacts, project financial 

factors, and road user costs. 

Development of Potential Work Zone Alternatives 

There are several factors that an agency may need to consider early on in the project 

development process in order to develop the potential work zone alternatives that will be 

modeled or evaluated during the alternatives analysis. These factors have the ability to shape the 

development of the alternative. Additionally, these factors also can address which supporting 

strategies could be incorporated within those alternatives. Incorporated into the work zone 

alternatives, these strategies have the potential to affect the mobility and safety impacts and 

performance measures of various alternatives. Three such factors, i.e., traffic operations, 

construction and contracting factors, and public information and awareness, are discussed in this 

section.

Traffic Operations 

The Traffic Operations factor includes strategies and programs that serve to improve and/or 

optimize mobility and safety within the work zone area. This factor is considered in order to 

evaluate and compare work zone alternatives and to assess the effectiveness of certain mitigation 

measures that could help the agency meet their project goals and objectives. There are certain 
185 

strategies that support the key factor of traffic operations. The following lists examples of such 

strategies as well as how they can be used to shape and support work zone alternatives.

• TDM Strategies – Demand management strategies provide mode shift incentives that 

encourage people to use alternative modes of transportation as a way of relieving traffic 

congestion. TDM strategies can include transit, HOV, and park-and-ride incentives and 

programs. These strategies should be considered as mitigation strategies that can support 

the work zone alternatives and aid an agency with meeting their goals and objectives. 

For instance, an agency may choose to implement park-and-ride and transit incentives in 

order to reduce congestion during peak travel in the work zone project area. 

• Traffic Operational Improvements – Traffic operational strategies include projects and 

programs that help optimize or improve traffic flow along transportation facilities. Some 

examples of these improvements include signal phasing/coordination, geometric 

improvements, and temporary signals. Similar to TDM, traffic operational improvements 

can support existing work zone alternatives in order to minimize or mitigate the work 

zone’s mobility impacts. For instance, one potential work zone alternative may be to 

establish diversion routes. To ensure these detour options are able to handle the 

additional demand, signal phasing/coordination improvements can optimize traffic flow 

and operations along these routes. 

• Incident and Enforcement Strategies – Incident and enforcement strategies can aid in 

clearing incidents and/or diverting traffic quickly and efficiently. These strategies could 

include improving service patrols response, traveler information, and signage. Such 

strategies can help support work zone alternatives and aid the agency in achieving certain 

project goals and objectives, especially those related to improving safety and reducing 

mobility impacts in and around the project area. 

• Work Zone Safety – Increasing work zone safety strategies can aid in preventing 

incidents along work zone corridors. Work zone safety strategies can serve as forms of 

mitigation techniques to achieve certain safety goals and objectives. Additionally, 

alternatives could be evaluated for how effectively they address work zone safety project 

goals and objectives such as reducing worker incidence and crash frequency and severity 

rates. Safety is discussed in greater detail in Section 8.2. 

Construction and Contracting Factors 

The construction and contracting factors address the various options that may have the ability to 

optimize the project schedule, construction duration, use of resources, and costs. Consideration 

of such factors can aid an agency in shaping their list of work zone alternatives and in achieving 

project goals and objectives. Various strategies that may be considered as part of these factors 

can serve as alternatives or may be incorporated into the work zone alternatives considered for 

the MOTAA. Example strategies related to construction and contracting include the following: 

• Construction Phasing/Staging Strategies – Staging of projects refer to how the 

contractor will position the equipment and material. Phasing refers to the sequencing of 

various aspects of a project. These types of strategies can serve as potential work zone 

alternatives that address different options for work zone strategies (i.e., full road closure, 
186 

lane closures, and detours) and phasing options for when, and in which order, 

construction tasks and/or project sections are completed. 

• Contracting Strategies and Project Delivery Methods – These types of strategies 

provide alternative ways of contracting the work and delivering the project. These could 

be incorporated into certain work zone alternatives as ways of accelerating construction 

duration and they may be considered as options for work zone alternatives analyzed 

within an MOTAA. Some examples of alternative contracting strategies that have the 

potential to optimize and/or accelerate project delivery and schedule include:

• A+B Bidding – This contract combines the cost to perform the work (component A) 

and the time to complete the project (component B). The bidder with the lowest 

overall A and B combined bid is awarded the contract. 

• Incentive/Disincentive Contracts – With these contracts, the contractor is provided a 

monetary incentive for completing the project ahead of schedule and is penalized with 

a disincentive fee for delays. A similar version of the incentive/disincentive contract 

is the “No excuse incentives” contract where a contractor is given a firm completion 

date with incentives for completing the work by or before that date. 

• Design-Build – This is a project delivery method where the agency combines 

procurement for both the design and construction services into a single contract from 

one private entity. 

• Construction Manager/General Contractor – This is a two-phase project delivery 

method in which a construction manager is selected for both the preconstruction and 

construction services of a project. 

• Other Innovative Construction Strategies – These include innovations in construction 

strategies and use of materials that aid in improving the project schedule. Examples 

include using precast members, prefabricated structural systems, rapid strength concrete, 

and polymer modified concrete. These strategies also could be incorporated into the 

development of particular work zone alternatives considered for an MOTAA. 

Public Information and Awareness

The public information and awareness factor include strategies that inform nearby residents, 

businesses, and facility users of the project’s progress, updates, and impacts. Informing all 

relevant stakeholders that may be involved or impacted by the project is key for project approval, 

keeping on schedule, and reducing mobility and safety impacts in and around the project area. 

This factor and supporting strategies should be considered within an MOTAA to support work 

zone alternatives and mitigate impacts of the project. Strategies that fall into this factor category 

may include:

• Media Outlets for Project Background and Information – These types of public 

information and awareness strategies provide the community background on the project, 

goals, and objectives as well as a forum for the public to voice comments, concerns, and 

suggestions. Such strategies could include community and stakeholder meetings, the 

distribution of brochures and press releases, creation of a promotional web site, and 

provision of a hotline to provide information and updates regarding the project and 

construction progress. These strategies inform the agency of specific concerns and issues 

that may impact the effectiveness and/or success of the project and work zone 
187 

alternatives considered. Furthermore, such strategies can support particular work zone 

alternatives. For instance, public information strategies could inform users of detour 

routes and when to avoid the project area due to lane and road closures.

• Traveler Information – Traveler information strategies are another form of public 

information techniques that can help an agency support work zone alternatives and 

achieve their project goals and objectives. Traveler information strategies include web 

sites providing vital traveler information (i.e., 511), highway advisory radio (HAR), and 

dynamic message signs (DMS). These provide road users pre- and en-route information 

regarding expected travel times, congestion levels, and detour options due to the 

construction. Such strategies can be incorporated into work zone alternatives to help 

mitigate safety and mobility impacts.

Evaluating and Comparing Work Zone Alternatives 

The previous chapters on alternatives analysis presented some of the types of performance 

measures that could be extracted from some of the modeling and analysis tools. These outputs 

primarily consist of mobility measures that could be used to compare and evaluate alternatives. 

As previously mentioned, there are additional qualitative and quantitative factors that also should 

be considered in an alternatives analysis. This section presents several types of factors that 

should be incorporated into the criteria and measures used to select a preferred work zone 

alternative. These factors, as discussed in the following sections, include environmental and land 

use impacts, project costs, and road user costs (RUC). 

Environmental and Land Use Impacts 

Environmental and land use factors require the agency to evaluate how a work zone alternative 

may impact the environment, as well as the residential and business community near the project 

area. Consideration for these factors can be used to ensure that the potential work zone 

alternatives are able to meet certain environmental standards. Additionally, these should be 

incorporated into an alternatives analysis or decision-making criteria in order to minimize 

negative impacts on surrounding residents, businesses, and services. Such considerations can 

include the following: 

• Right-of-Way – An agency should consider any need for the acquisition or purchase of 

right-of-way for the specific work zone alternatives involved. The amount of right-ofway should be incorporated into evaluating the land use and community impacts of the 

work zone alternatives.

• Air Quality Impacts – This factor accounts for the vehicle emissions and greenhouse 

gas impacts of the project and/or work zone alternative. The air quality impacts of the 

work zone alternatives can be used as a criterion for evaluating and comparing the work 

zone alternatives, as well as identifying the need for certain mitigation strategies to 

reduce such impacts.

• Noise Impacts – This factor accounts for noise-level issues caused by the project that 

substantially exceed noise abatement criteria or existing noise levels in the area. 

Considerations for such impacts also can be used to compare and evaluate different work 
188 

zone alternatives as well as identify mitigation strategies to relieve or reduce noise 

impacts.

• Impact to Retail Neighborhoods, Businesses, Employment Centers, Major Activity 

Hubs – Residential and business community impacts should be a factor considered in 

comparing the impacts of various work zone alternatives. Consideration of this factor 

can be used to identify certain mitigation measures that can minimize the negative 

impacts of the project or work zone alternative on the community. Examples may 

include customer access to facilities and businesses, parking loss, change in property 

values, and decrease in sales and profits as a result of the work zone. 

• Political Sensitivity – This factor considers the risk to the project due to any political 

issues and/or opposition to the project. The project and proposed alternatives would be 

evaluated early on in the project to assess the feasibility and probability of it obtaining 

approval. This factor can be used to eliminate certain alternatives that may potentially 

generate political opposition or involve unwarranted risk. 

Project Costs

This factor includes the costs associated with implementing the project through the particular 

work zone or construction strategy selected, costs associated with incorporated mitigation and 

monitoring strategies, and any right-of-way costs. Such costs are typically a major criteria 

element of whether an agency will consider or recommend a particular work zone alternative. 

Several work zone-related project costs include:

• Costs for Implementing Work Zone Alternative Strategies – These costs could be 

related to the costs of pursuing the alternative strategies listed in the Construction 

Methodology and Innovation section versus the cost of traditional construction and 

contracting strategies.

• Costs for Mitigation and Monitoring Strategies – These costs would be related to 

implementing any mitigation measures identified to minimize the impacts of the project. 

It also should cover cost considerations for any monitoring activities put in place. 

• Right-of-Way Costs – These costs are related to the acquisition of right-of-way for the 

project.

Road User Costs 

Another factor for consideration when comparing and evaluating work zone alternatives is road 

user costs. Work Zone Road User Cost (WZ RUC) is defined as the incremental cost or value 

incurred by highway users and the community at-large as a result of a work zone alternative.(36) 

WZ RUC primarily refers to monetary and nonmonetary components of work zone impacts. 

These could include quantitative measures such as user delay, vehicle operating cost, and crash 

cost. WZ RUC also includes considerations for qualitative measures such as the environmental, 

business, and societal impacts of the work zone project. The costs of such impacts can be used 

to evaluate and compare the different work zone alternatives. 
189 

The following lists the typical components and considerations needed for calculating the WZ 

RUC. A work zone example also is used to illustrate the key points and processes involved for 

calculating the cost of delay. For the more detailed step-by-step process for calculating WZ 

RUC, including how the work zone example featured in this document can be used to calculate 

vehicle operating costs and other RUC components, please refer to the Work Zone Road User 

Costs – Concepts and Applications.

(36)

Step 1 – Inputs for Demand-Capacity Analysis 

One of the first components of the WZ RUC calculation process is the data collection effort. The 

inputs needed to estimate the work zone impacts for computing the RUC of a project include: 

hourly traffic demand, traffic composition, work zone capacity, travel speeds, work zone 

configuration, and the maintenance of traffic (MOT) strategy(s).

Step 2 – Computing the Monetary Value of Delay 

The total delay time is calculated as the sum of delay due to speed changes, speed reductions, 

stopping, queue, and detours that occur as a result of the work zone’s implementation. The 

monetary value of delay due to these components is computed by multiplying the total delay 

time with the monetary value of travel time. The monetary value of travel time is the sum of the 

value of personal, business, and truck travel time, as well as the cost of freight inventory delay 

(for trucks only) and cost of vehicle depreciation (for all vehicles). The following steps describe 

how to calculate the monetary value of delay using the example.

For the example, the input data would include the following:

• The northbound lane carries an average daily traffic of 33,000 vehicles, of which eight percent are singleunit trucks and four percent are combination trucks.

• There are also ramp closures. Traffic volumes on the closed ramps equal 1,000 vehicles per day, of which 

three percent are single-unit trucks and two percent are combination trucks.

• The work zone project area is two miles long with a 24-hour/day single-lane closure. The estimated 

construction duration period is 20 days.

• The unrestricted upstream approach has a speed limit posting of 55 mph. In the work zone area, the speed 

limit posted is 45 mph.

• There is a three-mile detour road provided where the average speed is 40 mph.

Work Zone Example Project Description

The following example illustrates the work zone RUC methodology as detailed in the FHWA’s Work Zone Road 

User Costs: Concepts and Applications. In this example, a pavement rehabilitation work zone project is being 

performed on a six-lane, urban interstate. The work zone features a single-lane closure in the northbound 

direction of the interstate. The following illustrates how the methodology can be used to compute the total delay 

costs generated by the work zone.
190 

1. Value of travel time for passenger cars.

• To determine the value of travel time for passenger cars, the analyst must first determine the total delay 

time. The total delay time is the sum of speed change delay, reduced speed delay, stopping delay, queue 

delay, and detour delay. Figure 38 presents how the total delay time of 10,192.6 vehicle-hours per day is 

computed for the example work zone.

• The next step is to determine travel delay costs per passenger by multiplying the total delay time with the 

unit travel time values per passenger.

• To determine these travel time values the analyst must estimate the unit value of personal and business 

travel time. For this example, personal travel time is determined to be $20.88/vehicle-hour. Business travel 

time is valued at $36.89/vehicle-hour.

• The analyst can then compute the hourly time value of passenger cars using the weighted average of travel 

time values considering both personal and business travel time. 2009 NHS statistics state that 93.7% of 

passenger cars are expected to be on personal travel while 6.3% are on business. Using these statistics and 

the hourly time values computed in the previous step, the weighted average travel time values can be 

computed for passenger cars through the following: Hourly time value of passenger cars = (93.7% * 

$20.88) + (6.3%*$36.89) = $21.89/hour.

• To estimate the delay costs for passenger cars on the northbound lanes, the analyst can use the following:

• Percent of passenger cars on NB lanes = 88%

• Estimated delay time for all vehicles = 10,192.6 vehicle-hours/day

• Estimated delay time for passenger cars = 0.88* 10,192.6 = 8969.49 vehicle-hours/day

• Estimated delay costs for passenger cars = 8969.49*$21.89/hour = $196,342.10 per day

2. Value of travel time for trucks.

• To determine the travel delay costs for trucks, the analyst must determine the truck travel time value. For 

this example, the hourly time value of single-unit trucks is $23.06/hour. The hourly time value of 

combination trucks is $29.65/hour.

• The analyst will then have to estimate the delay costs for both classes of trucks for the project using 

percentage of trucks, delay time for all vehicles, and the travel time unit values determined in the previous 

step.

• For single-unit trucks:

• Percent of single-unit trucks in example = 8%

• Estimated delay time for all vehicles = 10,192.6 vehicle-hours/day

• Estimated delay time for single-unit trucks = 8%*10,192.6 = 815.4 vehicle-hours/day

• Estimated delay costs for single-unit trucks = 815.4 * $23.06/hour = $18,803.31/day

• For combination trucks:

• Percent of combination trucks in example = 4%

• Estimated delay time for combination trucks = 4%*10,192.6 = 407.7 vehicle-hours/day

• Estimated delay costs for combination trucks = 407.7*$29.65 = $12,088.42/day

• Estimated delay for all trucks = $18,803.31 + $12,088.42 = $30,891.73
191 

3. Time-related vehicle depreciation costs for all vehicles.

The third step in determining total delay costs for all vehicles is to determine the time-related vehicle 

depreciation costs for the three vehicle types: passenger cars, single-unit trucks, and combination trucks.

• Passenger Cars – Through previous calculations, the analyst has determined that the percent of passenger 

cars is 88 percent and that the estimated delay time for this vehicle class is 8,969.49 vehicle-hours/day. 

Therefore, if the hourly time-related depreciation cost for passenger cars is $1.225/hour, the estimated 

depreciation cost for passenger cars = 8969,49 * $1.225/hr or $10,987.62/day.

• Single-Unit Trucks – Previous calculations have shown that this example can expect 8 percent of all 

vehicles to be single-unit trucks. Additionally, this percentage of trucks generates a total delay time of 

815.4 vehicle-hours/day. If the hourly depreciation cost for this truck class is $3.09/hour, the estimated 

depreciation cost for single-unit trucks is = 815.4 * $3.09/hr= $2,519.59/day. 

• Combination Trucks – Previous calculations have shown that there are 4 percent combination trucks in this 

example that generate a total delay time of 407.7 vehicle-hours/day. If the hourly depreciation cost for 

combination trucks is $9.29/hour, then the estimated time-related depreciation cost for this vehicle class is 

equal to 407.7 * $9.29/hour = $3,787.53/day.

• The estimated time-related depreciation cost for all vehicles = $10,987.62 + 2,519.59 + 3,787.53 = 

$17,294.74/day.

4. Cost of freight inventory delay.

The final delay value component is the cost of freight inventory delay. This cost is determined by multiplying 

the number of trucks carrying freight by the hourly cost of freight inventory valuables.

• The first step in this process is to estimate the hourly cost of freight inventory valuables. For single-unit 

trucks this is $0.18/hour and for combination trucks this is $0.31/hour.

• The next step is to estimate the number of loaded freight trucks. For this example, the number of loaded 

single-unit trucks is 1,874 and loaded combination trucks are 964.

• The third step is to estimate the cost of freight inventory delay. With the average delay time for a vehicle per 

day at .309 hour/vehicle/day, the cost of freight inventory delay for trucks can be computed as the product 

of average payload * number of trucks * average delay time.

• For single-unit trucks = $0.18/hour * 1,874 * 0.30 hour/vehicle/day = $104.23/day

• For combination trucks = $0.31 * 964 * 0.309 = $92.34/day

• Cost of freight inventory delay for all trucks = $104.23 +92.34 = $196.57/day

5. The Total Delay Costs.

The total delay costs is equal to the sum of value of travel time for passenger cars, the value of travel time for 

trucks, time-related depreciation costs for all vehicles, and cost of freight inventory delay.

Total Delay Costs for the example = $196, 342.10 + 30,891.73 + 17,294.74 + 196.57 = $244,725.10/day.
192 

Figure 38. Work Zone Travel Delay Analysis 

Step 3 – Computing Vehicle Operating Costs (VOC) 

Vehicle operating costs (VOC) are the costs incurred by road users as a result of vehicle use. It 

is calculated as the sum of vehicle operating costs associated with the speed change, stopping, 

queue idling, and detouring that occur as a result of the work zone. The vehicle operating costs 

is an aggregate measure of the consumption of the following resources while driving a vehicle 

between two points. It includes considerations for: fuel consumption, engine oil consumption, 

tire wear, repair and maintenance, and mileage-related depreciation.

Step 4 – Computing Crash Costs through Crash Analysis 

The crash costs associated with work zones are a function of the changes in crash rates due to the 

presence of the work zone. Crash-related inputs for this part of the WZ RUC analysis include: 

crash rate at work zones, crash severity rating, and unit cost of crashes.

Time Mainline

Traffic 

Volume

Total Delay Time (minutes/vehicle) Delay Time for all 

vehicles

(veh-hours /day)

Speed 

Change

Reduced 

Speed

Stopping Queuing Total

00-01 304 0.07 0.24 0 0 0.32 1.60

01-02 304 0.07 0.24 0 0 0.32 1.60

02-03 304 0.07 0.24 0 0 0.32 1.60

03-04 456 0.07 0.24 0 0 0.32 2.41

04-05 646 0.07 0.24 0 0 0.32 3.41

05-06 988 0.07 0.24 0 0 0.32 5.22

06-07 1558 0.07 0.24 0 0 0.32 8.22

07-08 2964 0.40 0.24 0.40 23.81 24.85 1227.34

08-09 3610 0.40 0.24 0.40 23.81 24.85 1494.84

09-10 2470 0.40 0.24 0.40 23.81 24.85 1022.79

10-11 1786 0.40 0.24 0.40 23.81 24.85 739.55

11-12 1710 0.47 0.24 0.40 23.81 24.92 710.20

12-13 1634 0.07 0.24 0 0 0.32 8.62

13-14 1710 0.07 0.24 0 0 0.32 9.03

14-15 1862 0.07 0.24 0 0 0.32 9.83

15-16 2470 0.07 0.24 0 0 0.32 13.04

16-17 3002 0.40 0.24 0.40 23.81 24.85 1243.08

17-18 3534 0.40 0.24 0.40 23.81 24.85 1463.37

18-19 2432 0.40 0.24 0.40 23.81 24.85 1007.05

19-20 1482 0.40 0.24 0.40 23.81 24.85 613.67

20-21 1254 0.47 0.24 0.40 23.81 24.92 520.81

21-22 684 0.07 0.24 0 0 0.32 3.61

22-23 456 0.07 0.24 0 0 0.32 2.41

23-24 380 0.07 0.24 0 0 0.32 2.01

Total delay time of mainline through traffic =10,115.32

Detour delay time = 2.32 min/vehicle * 2000 vehicles= 4636 min = 77.28

Total estimated delay time per day 10,192.6
193 

Step 5 – Emissions Analysis

Work zone-related emission costs are computed by determining the costs associated with the 

additional vehicle emissions resulting from reduced speeds and queuing. Vehicle emissions 

considerations include air pollutant emissions and greenhouse gases. Emissions cost is the 

product of the vehicle miles traveled, emission rate, and unit costs (cost/mile) by emission type. 

Several models such as the Static Emission Factor Model and Dynamic Instantaneous Emission 

Model can be used in order to estimate emission rates. The monetary values for emissions are 

typically derived from economic analyses of health impacts caused by the different types of 

emissions. Two sources of emission costs include the California Department of Transportation 

(Caltrans) estimates and HERS-ST Technical Report.

Step 6 – Accounting for Nonmonetary and Qualitative Factors

Additionally, WZ RUC analysis also takes into consideration additional factors such as noise, 

business, and societal impacts. Further details on these considerations are provided in the 

previous section on Environmental and Land Use Factors. 

I-15 Devore Example 

Background and Overview 

In 2004, Caltrans applied an innovative, fast-track reconstruction program to a heavily traveled 

Long-Life Pavement Rehabilitation Strategies (LLPRS) project on I-15 in the area of Devore in 

southern California. This area covered a 4.5-kilometer (2.8-mile) stretch of badly damaged 

concrete lanes. It was rebuilt in only two single-roadbed continuous closures (also called 

“extended closures”) totaling 210 hours, using counterflow traffic (opposite direction to the main 

traffic flow) and 24-hour-per-day construction operations. Traditional nighttime-only closures 

would have required 10 months worth of work. Instead, the reconstruction took 19 days, with 

each extended closure for one roadbed lasting 9 and one-half days.

The following describes the factors project managers and engineers considered before and during 

construction. Such strategies assisted decision-makers with selecting the optimal and most costeffective work zone alternatives, use of resources, and traffic operation mitigation strategies to 

employ.

Development of Work Zone Alternatives and Mitigations Measures 

The I-15 Devore Project included four closure work zone alternatives: 72-hour weekday, 55-

hour weekend, 1-roadbed continuous (24 hours per day, 7 days per week), and 10-hour 

nighttime.(47) The factors, supporting strategies, and mitigation measures that were incorporated 

into the development of these strategies include the following:

• Operational Considerations – The project’s traffic operational strategies included the 

use of moveable barriers during construction, which provided dynamic lane 

configurations that helped to balance traffic and accommodate peak direction traffic. The 
194 

travel demand management strategies implemented with this project included free 

commuter bus service to promote ridesharing. The enforcement and monitoring 

strategies included the provision of a project command center (agency coordination and 

monitored traffic and construction using CCTVs), the Construction Zone Enhanced 

Enforcement Program, which improved traffic control and enforcement in the 

construction work zone, and increased funding for California Highway Patrol to increase 

enforcement within the project area.

• Construction Method and Innovation – The project used innovative materials such as 

rapid-strength concrete mix, incentive/disincentive contracts.

• For pavement design and material alternatives, Caltrans used a software model to 

make comparisons from the perspective of production scheduling and traffic 

inconvenience. As a result, Caltrans selected: 1) rapid-strength concrete with a 12-

hour curing time rather than fast-setting hydraulic cement concrete with a 4-hour 

curing time; 2) a 15-centimeter (6-inch) new AC base, rather than a 15-cm lean 

concrete base; and 3) a widened 4.3-meter (14-foot)-wide lane, rather than the usual 

3.7-meter (12-foot)-wide lane tied to a new concrete shoulder on the outermost truck 

lane.

• Public Information and Awareness – The project included a multifaceted outreach 

program. The project utilized systems that provided real-time work zone travel information 

through changeable message signs, the project web site, and a telephone hotline. Caltrans 

received and shared feedback from the local community through the High Desert 

Commuter Advisory Committee. Prior to construction, large employers and affected 

businesses were informed through project fliers, public meetings, and media outreach.

Factors Considered in Alternatives Analysis and Decision-Making 

The following factors were considered when evaluating and deciding among the four closure 

scenarios:

• Project, Road User Costs, Construction Duration, and Mobility Impacts: 

• A preconstruction analysis was conducted in order to arrive at the optimal 

construction closure scenario and pavement design and material alternatives.

• Project engineers used traffic simulation modeling analysis software to arrive at an 

optimal and economical scenario for rehabilitation closures.

• The analysis compared the scenarios by cost, schedule duration, and delay impacts as 

shown on Table 77. The analysis concluded that the continuous/extended closure 

scenario would be the most economical. 

• Compared with traditional 10-hour nighttime closures, the preconstruction analysis 

indicated that the extended closure scenario would generate about 80 percent less 

total closure time, about 30 percent less road-user cost due to traffic delay, and about 

25 percent less agency cost for construction and traffic control. 

• The I-15 northbound roadbed was closed for reconstruction, and northbound traffic 

was switched to the southbound side via median crossovers at the ends of the work 

zone. Traffic in the two directions shared the southbound lanes as counterflow traffic 

separated by the moveable barrier. The same process was repeated for reconstruction 

of the southbound roadbed. 
195 

Table 77. Closure Options for I-15 Devore Project

Closure Scenario

Schedule Comparison Traffic Delay

Cost Comparison

(Million Dollars) 

Number of 

Closures

Closures

(Hours)

Road User 

Cost

(Million 

Dollars)

Maximum 

Delay 

(Minutes)

Agency 

Cost

Total Cost 

to Users 

and Agency

72-Hour Weekday 8 512 6.6 75 12.6 19.2

55-Hour Weekend 10 550 12.7 196 15.1 27.8

1 Roadbed Continuous 2 400 6.1 196 9.9 16.0

10-Hour Nighttime 220 2,200 10.0 36 20.4 30.4

7.2 WORK ZONE SAFETY ANALYSIS

This section discusses work zone safety factors and considerations within an MOTAA process, 

with a focus on surrogate measures of safety as it relates to work zone traffic analysis. The 

section introduces measures of safety by providing an overview of the differences between 

traditional traffic safety analysis versus the development of surrogate safety measures as a way 

of predicting and preventing future crashes. The section then provides examples of surrogate 

safety measures and their role in an MOTAA procedure.

Purpose of a Work Zone Safety Analysis 

In general, most work zone assessment studies agree that crash rates appear to be higher during 

work zone periods than preconstruction conditions. The diverse and sometimes conflicting range 

of results from work zone research occur because the various projects studied often incorporate 

different work zone configurations and strategies, and exhibit different types of roadway and 

traffic characteristics. For instance, incident rates are typically seen to increase by 

approximately 20 to 30 percent within work zones relative to the area’s normal crash rates.(48) 

Additionally, among these work zone incidents the predominant crash type seen are rear-end 

crashes.(49)

 Due to the significant potential increases in crash frequencies during work zone 

conditions, it is important for agencies to conduct work zone safety analyses to identify potential 

mitigation and/or preventative strategies that could improve safety within the project area. 

Additionally, when conducting a work zone safety analysis, agencies should be aware of the 

potential variations in results from work zone safety research. When possible, agencies and 

analysts should conduct work zone safety studies and analyses customized to their specific work 

zone’s design and strategies, as well as the project area’s traffic and road geometric 

characteristics.

Traditional Work Zone Safety Analysis 

Work zone crashes are typically divided into two categories: crashes due to the presence of the 

work zone and crashes that would have occurred regardless of the work zone. Traditional work 

zone safety analysis consists of reviewing existing crash patterns prior to construction. These 

historical crashes are reviewed in order to estimate the potential crashes that would have 
196 

occurred regardless of the work zone’s presence. In order to estimate potential crashes due to the 

work zone, traditional safety analysis typically requires reviewing crash patterns of a previous 

work zone project that has similar characteristics to the subject project. Although the traditional 

work zone safety analysis procedures may vary slightly between agencies, the process typically 

involves the following steps: 

1. Identify completed work zone projects that share similar characteristics to the new project;

2. Review crash data for the similar work zone projects; 

3. Review existing preconstruction crash data at the project location;

4. Determine best practices of design and mitigation measures for the new project based on 

review conducted in Step 2; and 

5. Based on results from Steps 3 and 4, determine if there are crash patterns that could be 

addressed through design or mitigation strategies that minimize crash rates and severity 

prior to and during construction. 

Using historical crash data, accidents also can be valued and used to compare the safety 

implications of work zone alternatives. For example, a United Kingdom Department for 

Transport (DfT) sponsored computer program, Quadro (Queues and Delays at Roadworks) 

estimates the increase in accident rates along a transportation facility as a result of the presence 

of construction.(50)

 It then converts this increase into a monetary value that can be used to 

evaluate and compare different work zone alternatives. In order to perform this calculation, the 

Quadro tool utilizes historical crash data. Crash rates for each facility type are collected during 

preconstruction conditions and with various types of work zone traffic control strategies. These 

rates are then multiplied by the estimated traffic volumes in the work zone to estimate the 

potential number of crashes that will occur when the work zone project is implemented. The 

difference between the crash rates at preconstruction and during construction is then converted to 

a monetary value by multiplying the average comprehensive cost for each crash severity level.(51) 

Table 78 shows the Quadro road user cost outputs from a Wisconsin Department of 

Transportation work zone traffic analysis of a 24-hour freeway lane closure project. In this 

example, Wisconsin DOT analyzed the mobility and road user cost impacts associated with one 

of the project’s diversion routes. 

Table 78. Quadro Road User Cost Output Example

Output Result

Average Accident Numbers (PIAa/Day)

Main route without works 0.0767

Main route with works 0.0736

Diversion 0.1385

Impacts on Road Users

Road user costs per day (dollars) $45,587

Congested hours per dayb 3

a PIA – Personal Injury Accidents.

b Delays Exceeding 15 Minutes.
197 

Overview of Surrogate Safety Measures

While traditional safety analysis primarily focuses on mitigation measures based on historical 

crash data, surrogate measures of safety addresses predictive and preventative strategies to 

improve safety within work zones. Surrogate measures of safety are especially helpful in the 

absence of reliable or accurate historical crash data. The results of surrogate safety measures 

research and studies can serve a variety of different purposes:

• Such studies can be used to determine how certain project, facility, and driver behavior 

characteristics impact the probability of accidents occurring;

• These measures can determine how certain project and/or traffic characteristics relate to 

crash severity; and

• Surrogate safety measures can provide further insight on which mitigation measures will 

be most effective in minimizing the safety impacts of the work zone.

Research Developments and Analysis of Safety Analysis Surrogate Measures 

Several research efforts have analyzed and developed surrogate safety measures with 

applications for work zones. Surrogate measures of safety can be characterized into two 

categories: conflict events and driver behavior characteristics. This section presents surrogate 

safety measures research conducted for these two specific categories.

Surrogate Safety Measures: Conflict Events

Much of the research has dealt with surrogate measures in regards to traffic conflicts. Conflict 

events can be described as potential crash situations due to safety concerns regarding the 

roadway design. Conflict events can lead to potential rear-end or angled collisions due to turns, 

merges, and lane changes. Some examples of surrogate measures regarding traffic conflicts that 

could be used to evaluate the severity of conflict within work zones include time to collision, 

post encroachment time, and deceleration rates.(52) 

There also have been development efforts for modeling and analysis tools that provide insight on 

surrogate measures of safety in relation to conflict analysis. One of these tools is the Surrogate 

Safety Assessment Model (SSAM), which is an FHWA-sponsored project that analyzes narrowly 

averted vehicle-to-vehicle collisions in order to assess and predict the safety of a traffic facility 

and prevent or reduce the frequency of accidents occurring.(53) SSAM combines 

microsimulation and automated conflict analysis to calculate the following surrogate safety 

measures:

• Minimum time-to-collision (TTC);

• Minimum post encroachment time (PET);

• Initial deceleration rate (MaxD);

• Maximum speed (MaxS);

• Maximum speed differential (DeltaS); and

• Vehicle velocity change had the event proceeded to a crash (DeltaV).
198 

SSAM provides outputs that include a table of conflicts identified in the analysis, including 

information regarding, time, location, vehicle information, and measures of conflict severity. 

Figure 39 provides a snapshot of the SSAM user interface with conflict icons color coded to 

display varying values of time-to-collision.

Figure 39. SSAM Screenshot

(Source: Federal Highway Administration, 2008.) 

Surrogate Safety Measures: Driver Behavior Parameters

Other research efforts study the relationships between surrogate measures of safety and driver 

behavior characteristics. Several of these studies provide insight on how certain driver behavior 

characteristics can impact the frequency and severity of crashes. Some examples of how driver 

behavior characteristics have been used to develop surrogate measures of safety include:

• Evaluating the relationship between driver behavior parameters and rear-end 

collisions – One study by Sun and Benekohal (2004) looked at driver gap characteristics 

for evaluating the risk of rear-end collisions for vehicles in platoons.(54) This research 

quantifies the relationship between car-following parameters and safety performance 

measures in work zones. It involves studying the variations of time headway for different 

car-following models and work zone types. 

• Evaluating the relationship between driver behavior parameters and crash 

severity – A research study by Li and Bai (2008) evaluated the use of measures such as 

deceleration rate, maximum speeds, and speed differentials at work zones to develop a 

measure that provides insight on the severity of potential collisions within construction 

zones.(55)

• Evaluating the safety at ramp junctions – Additionally, certain microsimulation tools 

can generate outputs and performance measures related to surrogate measures of safety. 

For instance, AIMSUN can be used for evaluating the safety at ramp junctions using 
199 

measures such as speed differential, maximum speed of the follower, and the deceleration 

rate of the follower vehicle during ramp-merging events.(56)

Use of Surrogate Safety Measures Within a MOTAA

For the most part, surrogate safety measures have been used at the research or academic level. 

Currently, there are very few instances of real-world applications of surrogate safety measures, 

particularly in work zones. Despite this, surrogate measures of safety can still serve a role within 

the MOTAA process. Some examples of how these measures could be used in evaluating work 

zone alternatives include the following: 

• Surrogate measures of safety can be utilized as criteria elements for evaluating and 

comparing alternatives during the decision-making stage of an MOTAA.

• Additionally, the surrogate safety measures can be used to identify safety issues 

regarding the project or work zone alternative design. Such measures can provide insight 

on how to adapt or modify a particular alternative in order to meet a certain level of 

safety.

• Alternatively, they can be used to identify and determine the effectiveness of certain 

mitigation strategies for addressing work zone safety.

Summary of Surrogate Measures of Safety

Table 79 provides a summary of the example surrogate safety measures discussed. The table 

highlights the analysis method employed, the required data inputs, outputs, and limitations in 

evaluating and utilizing the safety measure.
200 

Table 79. Summary of Safety Surrogate Measures

Measure Analysis Method Input Data

Purpose of Research 

and the Measure Limitations Reference

Crash Severity 

Index (CSI)

Statistical analysis Driver 

Characteristics, 

Time, 

Environmental 

Conditions, Road 

Conditions, Crash 

Information, 

Traffic Control

CSI models were designed to 

quantify the risk level of a work 

zone with a numerical value 

between zero and one.

A CSI of one indicates a very 

high-risk level in a given work 

zone, which infers that a fatal 

crash might take place if a crash 

occurs.

Predicted CSI values for 

fatal crash cases were not 

consistent with the actual 

severity outcomes collected 

with the research. Further 

research with crashes of 

varying severity levels is 

required to improve the CSI 

models.

Li, Y., and Y. Bai, 2008, 

Development of CrashSeverity-Index Models based 

on Work Zone Crash Severity 

Analyses, Transportation 

Research Board Conference 

Proceedings.

Platoon and Gap 

Analysis

Statistical analysis Work Zone Type, 

Hourly Volume, 

Maximum Platoon 

Size

The probability of a rear-end 

crash and how many vehicles 

might be involved in this crash.

Work zone safety 

performance is difficult to 

evaluate due to the lack of 

reliable work zone crash 

data.

Sun, D., and R. Benekohal, 

2004, Analysis of Carfollowing Characteristics for 

Estimating Work Zone 

Safety, Transportation 

Research Board Conference 

Proceedings.

Time-to-Collision 

(TTC), Post 

Encroachment 

Time (PET), and 

Deceleration Rate 

(DR)

SSAM, in 

conjunction with 

microsimulation

Conflict Point, 

Vehicle Speed, 

Acceleration, 

Driver Behavior, 

Traffic 

Signalization

Lower TTC, Lower PET, and 

Higher DR indicate higher 

probability of collision.

Large quantity of input data 

may be needed specific to 

the location.

Gettman, D., and L. Head,

2008, Surrogate Safety 

Measures from Traffic 

Simulation Models, 

Transportation Research 

Board Conference 

Proceedings.

MaxS and DeltaS SSAM, in 

conjunction with 

microsimulation

Conflict Point, 

Vehicle Speed, 

Acceleration, 

Driver Behavior, 

Traffic 

Signalization

The measures provided insight 

regarding the likely severity of 

the (potential) resulting collision 

if the conflict event had resulted 

in a collision instead of a nearmiss. Higher MaxS and DeltaS 

indicate higher severity of the 

resulting collision.

Large quantity of input data 

may be needed specific to 

the location.

Gettman, D., and L. Head, 

2008. Surrogate Safety 

Measures from Traffic 

Simulation Models, 

Transportation Research 

Board Conference 

Proceedings.
201 

Measure Analysis Method Input Data

Purpose of Research 

and the Measure Limitations Reference

Unsafety Density 

Parameter (UD)

Postprocess 

microsimulation 

modeling results

Car-following 

model parameters, 

section length, 

position, speed, and 

max breaking 

capacity of 

follower and lead 

vehicle, number 

and duration of 

simulation steps

The analysis highlighted the 

difference in safety level 

between fluid and “jerked” 

traffic flow using UD as the 

safety indicator. The analysis 

compared a ramp section under 

a metering scenario and a 

without-metering scenario. 

Results showed higher UD 

values are correlated with 

higher levels of congestion.

The analysis has primarily 

been used for comparing 

scenarios. Its use also is 

limited to evaluating 

highway segments since its 

focus is primarily on the 

potential for rear-end 

collisions. The accuracy of 

the modeling results also is 

dependent on the quality of 

the driver behavior 

parameters.

Torday, A., and A. Dumont, 

2002, Safety indicator for 

Microsimulation-Based 

Assessments, Swiss Federal 

Institute of Technology, 

Laboratory of Traffic 

Facilities, LAVOC-EPFL, 

Lausanne, Switzerland.
202 

8.0 ANALYSIS REPORT STRUCTURE

This chapter presents the essential components of an MOTAA report, including: 

• Introduction and project background; 

• Existing and future conditions; 

• Alternatives considered; 

• Alternatives analysis and decision framework methodology;

• Analysis results, recommended alternative, and mitigation strategies; and

• Post implementation and monitoring plan. 

8.1 INTRODUCTION AND PROJECT BACKGROUND

This section of the report provides background information on the project, the constraints, and 

the roles and responsibilities of staff and stakeholders involved. It contains the following 

components: 

• Project Area – This component of the project background section includes information 

on the location of the project or study area. 

• Project Description – This component describes the general scope of and the need for 

the project. It covers details regarding the project type, the proposed changes and 

concept plans for the project, and the general schedule and time line of the work 

involved. 

• Project Goals and Objectives – After providing an overview of and the need for the 

project, the agency identifies the specific goals that the agency would like to achieve in 

relation to the work zone-related efforts of the project. Additionally, it specifies the 

objectives that are need in order to achieve those goals. 

• Project Constraints – In this section, the agency details the constraints associated with 

the project, including any concerns regarding the timing of the project, impacts on 

existing facilities, communities, and businesses, political sensitivity of the project, and 

potential conflicts with adjacent projects.

8.2 EXISTING AND FUTURE CONDITIONS

This section of the report describes the existing and predicted future conditions along the project 

area. The existing conditions section provides an overview of the current facility characteristics 

(i.e., geometries, lane configurations, etc.), traffic operations (traffic controllers, meters, and 

signal timings/phasing), traffic data (volumes, speeds, and travel times), and crash data. For 

describing the future conditions of the corridor, the section includes traffic growth estimates, as 

well as traffic predictions during construction. This section also provides an overview of any 

politically sensitive issues associated with the project, as well as any potential community, 

business, and environmental concerns. 
203 

8.3 ALTERNATIVES CONSIDERED

This section of the report presents the various work zone alternatives considered for the project. 

These alternatives include options for construction sequencing or phasing/staging. The section 

should provide a description and/or concept plans for each alternative. Additionally, the section 

should detail any assessments or analysis methodology(s) used to eliminate or prioritize 

alternatives. Additionally, it also should detail any type of mitigation strategies that may be 

incorporated with the project to fully capture the range of impacts during the alternatives 

analysis/modeling stage.

8.4 ALTERNATIVES ANALYSIS AND DECISION FRAMEWORK METHODOLOGY

This section of the report should provide an overview of the methodology employed to evaluate 

and compare each of the alternatives and the decision framework used to recommend a preferred 

alternative. This section includes three components: the modeling/analysis efforts needed for 

assessing the impacts of the alternatives, any additional qualitative and quantitative 

considerations that provide supplemental information for the impact assessment, and the decision 

framework selected.

Alternatives Analysis Methodology 

The section on the alternatives analysis methodology should include the following components: 

• The necessary measures of effectiveness (MOE) and/or thresholds related to achieving 

the project goals and objectives; 

• The data collection efforts; and

• Overview of the modeling/analysis tool selected, modeling efforts needed, and 

justification for tool selection.

Qualitative and Quantitative Considerations 

This section should account for additional qualitative and quantitative considerations that may 

not be fully captured in the modeling/analysis effort identified in the previous step. This could 

account for the additional factors specified in Chapter 8 of this document. Examples of 

additional considerations include: 

• Traffic and travel demand considerations;

• Business and community impacts; 

• Environmental impacts;

• Construction/contracting strategies and considerations; and 

• Financial considerations, such as Road User Costs. 
204 

Decision Framework

The final component of this section of the report should detail how the modeling/analysis results, 

as well as the assessment of additional qualitative and quantitative considerations were factored 

into the final criteria used for comparing and choosing among the different alternatives. This 

component should describe the methodology employed to prioritize the factors used in the 

development of the criteria. The description of the decision framework selected should detail 

how the prioritized factors were incorporated into the criteria utilized in comparing and choosing 

amongst alternatives.

8.5 ANALYSIS RESULTS, RECOMMENDED ALTERNATIVE, AND MITIGATION 

STRATEGIES

This section should provide a synopsis of the analysis results and detail how a preferred 

alternative was chosen. The section provides details on the performance measures for the 

analysis and the results of the decision-making criteria employed. Additionally, this section also 

should detail any selected mitigation strategies incorporated into the recommended alternative(s) 

in order to minimize the work zone’s impact on travel, the surrounding community, businesses, 

and the environment. 

8.6 POST-IMPLEMENTATION AND MONITORING PLAN

The final section should include any plans for monitoring the performance of the work zone 

during the construction period. Monitoring post-implementation of the project aids in validating 

the impact assessment and improves future analysis efforts for similar work zone projects. The 

section should include information for the following: 

• Monitoring requirements;

• Data needs and performance measures for the monitoring effort; and 

• Plan for using monitoring data and results to feed back into the impact assessment. 

Table 80 features a checklist that summarizes the components described in this chapter. 

Table 80. MOTAA Reporting Components Checklist

A. Project Background

• Project area

• Project description

• Project type

• Proposed changes

• Concept plans

• Proposed construction staging/phasing

• General schedule and time line

• Project goals and objectives

• Project constraints

• Roles and responsibilities

• Managers, key staff, stakeholders, and review committees
205 

B. Existing and Future Conditions

• Existing facility characteristics (current geometries)

• Existing and historical traffic data (volumes, speeds, bottlenecks, etc.)

• Existing traffic operations (traffic controllers, ramp meters, etc.)

• Historical incident and crash data

• Local community and business concerns/issues

• Environmental concerns/issues

• Political sensitivity concerns/issues

• Traffic growth rates

• Traffic predictions during construction

C. Alternatives Considered

• Description of alternatives considered. Alternatives can consist of:

– Construction approach (phasing/staging strategies)

– Work zone impacts management strategies

• Preliminary alternatives analysis (i.e., fatal flaw analysis)

• Potential mitigation strategies to be incorporated into impacts assessment

D. Alternatives Analysis and Decision Framework Methodology

• Impact assessment methodology

– Measures of effectiveness and/or thresholds

– Description of data collection efforts needed

– Analysis tool/methodology selection and justification

• Other qualitative and quantitative considerations – These could include additional factors 

considered when comparing and choosing among different alternatives, such as:

– Traffic and demand considerations – These include traffic operations and travel demand 

management strategies

– Business and community impacts

– Environmental impacts

– Construction and contracting strategies

– Financial Considerations (including Road User Cost)

• Decision framework methodology

– Development of criteria: methodology used to prioritize factors used in comparing and 

evaluating alternatives

– Decision framework for comparing and choosing amongst alternatives – Selection of 

methodology and justification

E. Analysis Results, Recommended Alternative, and Mitigation Strategies

• Synopsis of analysis/modeling results and performance measures

• Recommended alternative or combination of alternatives and justification

• Selected project coordination, construction, contracting strategies

• Selected transportation operations and travel demand management strategies

• Selected safety and enforcement strategies

• Selected public and traveler information strategies

F. Post Implementation and Monitoring Plan

• Monitoring requirements

• Monitoring data needs

• Performance measures for monitoring

• Plan for feeding back monitoring results into the impact assessment methodology and analysis
206 

9.0 WORK ZONE CASE STUDIES

The following examples of work zone projects featured in this chapter were selected to 

demonstrate the MOTAA process outlined in Chapter 2 of this guide. This chapter features 

various work zone project types and analysis tools, providing a diverse set of MOTAA 

applications. This chapter follows the featured projects step by step throughout the MOTAA, 

including the planning stage, the modeling analysis, and the decision framework used in 

identifying a preferred work zone alternative. While the example projects featured in this 

chapter vary by project and modeling characteristics, they also vary based on when the MOTAA 

process was applied. For instance, while several of the examples featured in this chapter present 

real-world implementation of the MOTAA process on work zone projects, there also are some 

examples where the MOTAA process was implemented on previously completed work zone 

projects. In these cases, the work zone project was used for a study, academic research, or as an 

example that demonstrated a particular analysis methodology of conducting an MOTAA. There 

also are a few projects featured in this chapter that are hypothetical examples but are based upon 

real-world applications of the MOTAA process. Table 81 provides a summary of the case 

studies presented in this chapter. 

Table 81. Work Zone Case Studies Summary

Section

Project 

Type

Work 

Zone Type State Category of Analysis Tool Analysis Tool Note

9.1 Rural 

Arterial

Simple Flagging MD Traffic Signal Optimization Synchro Hypothetical 

project

9.2 Rural 

Freeway

Lane Closure MD Sketch Planning Lane Closure 

Analysis Program 

(LCAP)

Hypothetical 

project

9.3 Rural 

Freeway

Lane Closure TX Mesoscopic Simulation DynusT Real-world 

application

9.4 Rural 

Freeway

Lane Closure IA Microscopic Simulation CORSIM Conducted after 

WZ project 

completed

9.5 Urban 

Freeway

Lane Closure NC Analytical/Deterministic 

Tools (HCM-based)

FREEVAL-WZ Conducted after 

WZ project 

completed

9.6 Urban 

Freeway

Lane Closure MI Sketch Planning IDAS Real-world 

application

9.7 Bridge Temporary 

Two-Way OneLane Operation

MD Traffic Signal Optimization Synchro Hypothetical 

project

9.8 Urban 

Arterial

Lane Closure MD Traffic Signal Optimization Synchro/

SimTraffic

Hypothetical 

project

9.9 Interchange Ramp Closure IN Traffic Signal Optimization 

and Microsimulation

Synchro and

PARAMICS

Real-world 

application

9.10 Urban 

Freeway/

Bridge

Lane Closure OH Travel Demand Modeling TRANPLAN Real-world 

application

9.11 Urban 

Freeway

Lane Closure/

Ramp Closure

NJ Mesoscopic Simulation INTEGRATION Real-world 

application
207 

9.1 SIMPLE FLAGGING WORK ZONE PROJECTS USING TRAFFIC SIGNAL 

OPTIMIZATION TOOLS – SYNCHRO

Project Background – MD 144 Flagging Work Zone Project 

This case study, featured in the MD SHA describes the agency’s recommended MOTAA 

procedures for analyzing a hypothetical flagging work zone project.(15) This hypothetical project 

features the reconstruction of the unsignalized intersection of MD 144 (Frederick Road) and 

Dutton Avenue. Frederick Road runs east-west, while Dutton Avenue that runs north-south. 

Both roadways are two-lane, two-way, undivided roadways. The project’s base and alternative 

models were analyzed using the traffic analysis tool, Synchro. The following sections describe 

the MD SHA’s MOTAA process and detail how the agency’s process fits the recommended 

procedure described in Section 4.4 of this document. 

Application of the MOTAA Process 

Step 1 – Problem Definition, Scope, Goals, and Objectives 

For this intersection reconstruction case study, the objectives of the analysis include the 

following: 

1. Determine if the reconstruction of the intersection can be performed under flagging 

operations, or if a detour should be used; and 

2. If flagging operations is determined to be suited for this project, additional analysis is 

needed to determine during what periods of the day flagging should be used. 

Step 2 – Establishing the MOEs and Thresholds 

The Synchro model outputs extracted from the analysis include control delay and LOS. These 

outputs are then evaluated to determine if they meet the established mobility thresholds of the 

agency. In this case study, the threshold is set at a maximum LOS D and control delay of 30 

seconds. 

Step 3 – Choosing the Analysis Tool 

Most arterials and freeways across the State of Maryland have been modeled using Synchro and 

CORSIM, respectively. To reduce data collection and model development efforts, Synchro was, 

therefore, chosen as the analysis tool. 
208 

Step 4 – Identify the Alternatives

In this case study, only one work zone alternative was determined prior to running the model. 

The first work zone scenario evaluated features a four-way flagging work zone layout. The 

second work zone scenario was later established when the four-way flagging work zone 

configuration failed to meet the mobility thresholds specified in Step 3. The second work zone 

scenario features a three-way flagging work zone layout and is described in Step 6. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

The goals and objectives of the analysis were described in Step 1 of this section. After 

determining the goals and objectives of the analysis, the next step is data collection. For this 

case study, the analyst(s) obtained existing traffic control, road geometry plans, and demand 

data. There were no recent turning movement counts for the area, so the agency had new field 

counts taken at the intersection from 6:00 a.m. to 7:00 p.m. during a one-day period. 

Step 5B – Existing Conditions Model Development 

After project scoping and data collection, the next step of the MOTAA is to develop an existing 

conditions model with the appropriate geometry, controls, traffic demands, and capacities. Since 

there was not an existing Synchro model, a new model was coded using the information 

(intersection control, timing plan, and geometry) from the data collection effort. 

Data Collection Options

In this example, field observations were conducted during a one-day period. To account for the variability of 

traffic flow, field data collection could be conducted over several days. When obtaining data for existing 

conditions model development, it is also important to capture traffic conditions representative of typical travel 

based on the time of day (peak period, off-peak, weekend, holiday) the analysis is trying to model or simulate. 

For capturing traffic conditions for a typical weekday, it is recommended to collect field data on weekdays, such 

as Tuesday, Wednesday, and Thursday; and also during months, such as September through November (after 

Labor Day and before Thanksgiving) and/or February to April (excluding major holidays) since these time 

periods represent more typical commute patterns.

Analysis Tool Selection Options

Chapter 3 of this document describes various factors to consider when determining what type of analysis tool to 

use for the MOTAA. The recommended factors for identifying the appropriate modeling approach include:

• Project goals and objectives;

• Work zone characteristics;

• Agency resources;

• Performance measures;

• Data; and

• Transportation Management Plan (TMP) ( the selected tool should be able to capture the impacts of various 

traffic control, operations, and mitigation strategies).
209 

Step 5C – Existing Conditions Model Calibration 

Since the intersection had low volumes, the analysts assumed that model calibration was 

unnecessary.

Step 5D – Work Zone Base Conditions Model Development

The next step is the development of a work zone base conditions model by modifying the 

existing conditions model. In preparation for the analysis, the analyst must determine the scope 

of the modeling analysis effort. One of the first steps an analyst must take is to determine the 

limits of the study network to be analyzed. For this example, it is assumed that during the 

construction, the intersection would operate under flagging operations, which is comparable to a 

four-way stop control intersection. To model this type of control in Synchro, the intersection 

was assumed to operate under four-way split phasing with a cycle length of 180 seconds and 5-

second clearance intervals. All work was restricted to off-peak hours.

A summary of the data and assumed traffic operations for the project is presented in Table 82. 

The work zone lane configurations assumed for the Synchro model is shown in Figure 40. No 

changes to the traffic volumes were assumed for this analysis.

Work Zone Base Model Development Modifications

Although there were no changes to the traffic demands in this cases study, typical modifications incorporated 

into the development of a work zone base model include changes to road geometry, traffic controls, and traffic 

demands.

Model Calibration and Validation Options

If an existing conditions model calibration process is needed, each traffic signal optimization software package 

has a set of user-adjustable parameters that enable the analyst to calibrate the software to better match specific 

local conditions. This calibration process involves the selection of a few parameters for calibration and the 

repeated operation of the model to identify the best values for those parameters. Additionally, each agency and 

project may have different calibration guidelines and requirements. Section 4.4 of this document provides 

guidance on the considerations and steps involved in the calibration of traffic signal optimization models.

Traffic Demands and Volumes

Although not discussed in this example, an origin-destination matrix estimation (ODME) process may be needed 

in order to determine entry volumes and balanced turning movements for the project. For further information on 

handling volumes during model development using traffic signal optimization tools, refer to Chapter 4, 

Section 4.4 of this document.
210 

Table 82. Summary of Data and Assumptions

Approach

Approach 

Volume

(VPH)

Thru Green 

(Seconds)

Cycle Length 

(Seconds)

Estimated Queue Length

By Number 

of Vehicles

By Vehicle 

Length

(Feet)*

Eastbound 764 90 180 19 475

Westbound 542 60 180 9 225

Northbound 14 5 180 1 25

Southbound 41 5 180 1 25

*Average vehicle length of 25 feet assumed.

Figure 40. Four-Way Flagging Work Zone Layout 

(Source: Maryland State Highway Administration, 2008.)

Step 5E: Work Zone Base Conditions Model Calibration 

No work zone base conditions model calibration was conducted for this case study. 

Work Zone Base Conditions Model Calibration Options

Work zone model capacity and performance measures can be calibrated to field data and or prior experiences 

(from similarly implemented work zone projects). In order to calibrate to field data or case studies of similar 

work zone types, the analyst can evaluate the work zone’s queues, travel times, delays, and speeds. 

Additionally, the analyst may study parameters related to driving behaviors in work zones of similar types. Such 

observations and measures will aid the analyst in the work zone calibration process.
211 

Step 6 – Alternatives Analysis

The next step in the MOTAA is to perform model runs in order to analyze the different 

alternatives. The analysis involves running the model and obtaining outputs and measures, such 

as control delay and LOS. The results of the four-way alternative is presented in Table 83. 

Because this alternative did not meet the mobility thresholds of a maximum of LOS D and 

control delay of 30 seconds, the agency decided to develop and test another alternative.

Table 83. Alternative 1 – Synchro Model Outputs

Intersection

Control Delay

(Seconds) LOS

MD 144 at Dutton Avenue 144.9 F

The alternate work zone model, developed by the agency, features a three-way flagging work 

zone layout. For this alternative, the eastbound and westbound approaches were changed to each 

have one shared left-through-right lane. These lanes would be shifted throughout the 

construction to permit the approaches to operate concurrently. This alternative’s work zone 

configuration is shown in Figure 41. To represent the three-way flagging operations, this 

alternative was modeled by changing the existing condition’s intersection control into an 

actuated-uncoordinated signal control with split phasing for the minor approaches. The model 

results for this alternative are shown in Table 84. 

Figure 41. Three-Way Flagging Work Zone Configuration 

(Source: Maryland State Highway Administration, 2008.)
212 

Table 84. Alternative 2 – Synchro Model Outputs

Intersection

Control Delay

(Seconds) LOS

MD 144 at Dutton Avenue 22.2 C

As the results show in Table 84 Alternative 2 meets the mobility thresholds and is chosen as the 

recommended alternative. Additional analysis was performed in order to determine when it was 

best to implement the flagging operations. Table 85 presents the recommended work zone 

alternative and work hour restrictions. 

Table 85. Recommended Work Zone Alternative

Work Zone Alternative Work Hour Restrictions

Perform reconstruction in the intersection using flagging operations on all 

approaches, permitting concurrent eastbound/westbound movements

Monday-Friday: 9:00 a.m.-3:00 p.m.

9.2 RURAL FREEWAY LANE CLOSURES USING SKETCH PLANNING – 

MARYLAND STATE HIGHWAY ADMINISTRATION (SHA) LANE CLOSURE 

ANALYSIS PROGRAM (LCAP)

Project Background

This case study is based on the hypothetical application of Maryland SHA’s lane closure analysis 

methodology on I-95, an eight-lane, two-way divided interstate highway that runs in the northsouth direction through Howard County, Maryland. This case study, featured in Maryland 

SHA’s Work Zone Analysis Guide, evaluates the impacts of a resurfacing project on both 

directions of I-95 approximately between MD 216 and I-895, as shown on Figure 42.(15)

 The 

roadway resurfacing excludes the ramps. The work is to be completed by resurfacing two travel 

lanes at a time and in one-mile-long segments. 

Alternative Analysis Options

The alternative analysis process may differ based on the agency and the project. In this case, a second work 

zone alternative was considered because the primary alternative did not meet the mobility thresholds. Another 

alternatives analysis approach may involve analyzing several alternatives concurrently and then using a set of 

different performance measures to compare and choose among the alternatives. In this situation, the criteria for 

selecting the preferred alternative depended only on mobility thresholds. For other decision-making framework 

options, refer to Chapter 5 of this document.
213 

Figure 42. I-95 Howard County, Maryland Study Area Map

(Source: Maryland State Highway Administration, 2012.) 

Application of the MOTAA

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The objective of the analysis is to determine the time periods when it would be most appropriate 

for three lanes on the mainline to be closed. 

Step 2 – Establishing the Measures of Effectiveness and Thresholds 

Performance measures and outputs extracted from the analysis include maximum queue length 

across different days of the week and lane closure durations. The freeway lane closure mobility 

thresholds established by the agency are that queue length should be less than one mile for any 

duration and that queues between 1 and 1.5 miles should last less than two hours. 

Step 3 – Choosing the Analysis Tool 

Because of the simplicity of the work zone, the agency decided to perform the analysis using the 

lane closure analysis program (LCAP).
214 

Step 4 – Identify the Alternatives

The analysis will determine which combinations of days of the week and lane closure durations 

will meet the threshold indicating when a three-lane closure is acceptable. There is an alternative 

for each day of the week per direction of traffic along I-95. Therefore, there are a total of 14 

alternatives. Each of these alternatives is presented in Step 6. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the modeling development and analysis, the analysts should define the 

objectives of the analysis, the scope of the modeling or analysis effort, and identify the data 

needed for the modeling analysis effort.

• Scope – As previously mentioned this case study involves a sketch-level planning 

analysis using MD SHA’s LCAP. Because it was assumed that the resurfacing will be 

performed in one-mile-long segments, the study area will include the one-mile work zone 

length plus the buffer and taper lengths for the study area. In this case study, for a onemile-long work zone with a three-lane closure, the buffer and taper lengths is 

approximately one-half mile. Therefore, the total study area network length for the 

analysis is one and one-half miles.

• Data Collection – The data inputs for this analysis include the following: 

• Volume – Existing (preconstruction conditions) traffic volume data from the 

permanent count station near the junction of I-95 and MD 103 were obtained from 

MD SHA. Volume data were collected for the month of May since it had the highest 

traffic volumes of the year according to MD SHA Common Reports from Traffic 

Trends.

(57)

• Lane Configuration – The SHA’s Highway Location Reference manual was used to 

verify the number of mainline lanes throughout the study area.(58)

Analysis Tool Selection Options

In this case study, LCAP was chosen as the analysis methodology based on the level of complexity of the 

project. However, there are also additional factors that can be considered when selecting the analysis tool. 

Chapter 3 of this document describes these various factors in greater detail. The recommended factors for 

identifying the appropriate modeling approach include:

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control, operations, and 

mitigation strategies.). 
215 

Step 5B – Existing Conditions Model Development, and Step 5C – Existing Conditions 

Model Calibration 

The next step of the MOTAA is to develop the existing conditions model. Because simulation 

modeling was not needed to analyze the study area, there was no need for an existing conditions 

model and calibration effort. 

Step 5D and 5E – Work Zone Base Conditions Model Development and Calibration 

Due to the nature of this analysis, there was no need for model development for both the existing 

conditions as well as work zone base. Therefore, a calibration effort also was deemed 

unnecessary. For information on the analysis conducted, refer to Step 6, Alternatives Analysis. 

Step 6 – Alternatives Analysis

Typically, if modeling was involved, the alternatives analysis step included two stages: 

1) development of models to capture the scenarios or alternatives; and 2) description of how 

these models were run, the outputs extracted, and analysis of the results. In this case study, the 

stages are varied slightly. The first stage of this case study is to 1) determine the inputs to the 

LCAP analysis, and then 2) produce model outputs after running LCAP. 

Step 6A Alternatives Model Development – LCAP Inputs

For this case study, the first stage of the work zone base conditions model development was to 

determine the input to the LCAP. One of the main inputs to the program is work zone capacity. 

There are three capacity approximations methodologies featured in LCAP:

• Method 1 – SHA Work Zone Guidelines – The work zone capacity using SHA’s Lane 

Closure Analysis Guidelines is not available to analyze the capacity of a four-lane facility 

that drops down to one lane. However, the MD SHA guide does note that a facility that 

goes from three lanes to one lane is expected to have a work zone capacity of 1,170 

vphpl. 

• Method 2 – University of Maryland Equation assumes that a facility with 13 percent 

trucks, 1.5 miles work zone length, and one-foot lateral clearance will have a work zone 

capacity of 1,277 vphpl. 

• Method 3 – HCM 2000 Short-Term Work Zone equation states that assuming the facility 

has 13 percent trucks and rolling terrain, the work zone capacity is approximately 1,339 

vphpl. 

Therefore, the average of the work zone capacity from these three equations equals 1,260 vphpl, 

the work zone capacity used for this analysis.

After determining the work zone capacity, the next stage is the development of an LCAP model 

using data such as existing traffic volumes, number of lanes (existing and work zone conditions), 

and capacity (existing and work zone). The model was set up assuming at least four consecutive 

hours will be needed for mobilization, resurfacing, and demobilization. 
216 

Step 6B – Alternatives Analysis Model Runs and Results

After setting up the model with the required inputs (detailed in Step 6A), the next step in the LCAP 

procedure is to run the program. With this case study several iterations were run using different 

combinations of closure time periods and days of the week to determine which periods in the day 

could accommodate a three-lane closure that would meet the agency’s mobility thresholds. The 

trial and error process is depicted by Figure 43 and Figure 44. Figure 43 shows one trial where the 

closure along I-95 northbound lasted from 10:00 p.m. Wednesday through 5:00 a.m. Thursday. 

This scenario or trial produced queues that violated the agency’s mobility thresholds. The other 

trial shown on Figure 44 depicts a more feasible alternative. A maximum queue length was 

determined for each alternative or combination. Figure 45 summarizes the maximum queue length 

associated with the lane closure duration for each day of the week.

Based on the results shown on Figure 45, the queues for each closure period meet the mobility 

threshold specified in Step 2. Therefore, the results show that there is at least one four-hour period 

every night that can accommodate a three-lane closure along I-95 for the resurfacing project.

Figure 43. I-95 NB, Howard County, LCAP Output Trial 1 – 

Closure from 10:00 P.M. Wednesday to 5:00 A.M. Thursday 
217 

Figure 44. I-95 NB, Howard County, LCAP Output Trial 2 – 

Closure from 11:00 P.M. Wednesday to 5:00 A.M. Thursday 

Figure 45. I-95 Howard County, LCAP Model Outputs 
218 

9.3 RURAL FREEWAY LANE CLOSURES USING MESOSCOPIC SIMULATION – 

DYNUST

Project Background

This case study describes the process used by Pesti, G., et al. (2010) from Texas Transportation 

Institute (TTI) to evaluate the impacts of two work zones on separate freeway corridors in El 

Paso, Texas, using a mesoscopic simulation tool.(59) Figure 46 shows the work zone locations of 

these two projects.

1. I-10 – The construction project on I-10 requires closing one lane in this three-lane section 

from Zaragoza Road to Lee Trevino Drive in the westbound (WB) direction. The work 

zone is approximately 9,000 feet long. When the work zone is in effect, the discharge rate 

is set to 1,860 pcphpl, while the posted speed limit is lowered from 60 mph to 55 mph. 

2. Loop 375 – The work zone on Loop 375 is approximately 6,000 feet in length. It covers a 

portion of the southbound (SB) freeway segment from Pellicano Drive to Rojas Drive. 

During the construction, the discharge rate of the southbound traffic is set to 1,550 pcphpl, 

and the speed limit is reduced from 60 mph to 55 mph. 

Figure 46. I-10/Loop 375 Case Study Project Area Map 

(Source: Pesti, Chu, Balke, Zeng, Shelton, and Chaudhary, 2010.) 
219 

Application of the MOTAA

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The goal of the project was to determine the best construction sequence option that could 

minimize the disruption experienced by motorists.

Step 2 – Establishing the MOEs and Thresholds 

The impact of construction was evaluated using LOS. The LOS criteria were based on HCM 

2000. The criteria for freeways were based on densities and those for surface streets were based 

on link speeds. 

Step 3 – Choosing the Analysis Tool 

Dynamic Urban Systems for Transportation (DynusT), a dynamic traffic assignment (DTA)-

based tool, was used in the analysis. It was chosen because of its ability to evaluate the regional 

impacts of multiple work zone projects. 

Step 4 – Identify the Alternatives

Two project sequence options, with three scenarios, were evaluated in the case study; they are:

• Option 1 – Conduct one construction project at a time, which includes two subscenarios: 

• Scenario 1.1 – Construction project on Loop 375; and

• Scenario 1.2 – Construction project on I-10. 

• Option 2 (Scenario 2.0) – Conduct both construction projects simultaneously. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the modeling development and analysis, the analysts should define the 

objectives of the analysis and identify the data needed for the modeling analysis effort. For this 

case study, the following types of construction data were collected: 

• Project location;

• Capacity reduction; and

• The beginning and end time of each construction project. 

Analysis Tool Selection Options

In this case study, DynusT was chosen based upon the tool’s capabilities in evaluating the regional impacts of 

multiple work zone projects. Similar DTA-based tools can be applied for this case study as well.
220 

Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model. The DynusT network 

was created in a separate Texas DOT Interagency Contract (IAC), in which the Texas 

Transportation Institute (TTI) converted the El Paso MPO’s “Trans-Border” travel demand 

model for the year 2009 into a format that can be used by DynusT. Note that this network was 

limited to the U.S. side of the border and did not include the City of Juarez, except a few external 

zones for the border crossing. 

The DynusT network was represented by 2,862 nodes, 6,203 links, and 690 traffic analysis 

zones. The network was quite detailed and captured all freeways, interchanges, major arterials, 

and intersections that were included in the El Paso regional travel demand model. In addition, 

smaller localized minor roads were coded in the project study area. This additional level of 

detail was consistent with the scope of the project and scale of the problem that it tried to 

address.

Step 5C – Existing Conditions Model Calibration 

Major elements of the DynusT network of the El Paso region were reviewed and checked to 

ensure that they correctly represented the existing roadway system of El Paso. Where needed, 

some network elements or origin-destination (O-D) matrix values were corrected and updated. 

After verifying the most critical elements of the network, several test runs were performed. The 

test runs were performed on the verified network and used a time-varying O-D matrix calibrated 

in a previous Texas DOT IAC project using an O-D calibration method developed by the 

University of Arizona. 

Steps 5D and 5E – Work Zone Base Conditions Model Development and Calibration 

The existing conditions model was then modified to represent work zone conditions. In this case 

study, there was no work zone base conditions model developed or calibrated. 

Step 6 – Alternatives Analysis

The alternatives analysis step involves two stages: 1) development of models to capture the 

scenarios or alternatives; and 2) description of how these models were run, the outputs extracted, 

and analysis of the results. 

Step 6A – Alternatives Model Development

This case study featured three work zone alternative scenarios, which were created by modifying 

the existing conditions model. 

Work Zone Base Conditions Model Development and Calibration

Work zone base conditions models may not be necessary. In this instance, the two work zone alternatives are 

compared against each other to determine the relative merits of each. As such, there is no need for a “base” to 

check against.
221 

1. Scenario 1.1 – Construction project on Loop 375; 

2. Scenario 1.2 – Construction project on I-10; and

3. Scenario 2.0 – Conduct both construction projects simultaneously. 

Step 6B – Alternatives Analysis Model Runs and Results

The final steps of the MOTAA are to run the work zone alternative models using the selected 

traffic analysis tool and to extract select model outputs. As an example, Figure 47 shows lanemiles with LOS changes for Scenario 2.0, with C-C, N-C, and C-N representing traffic condition 

changes from congested to congested (C-C), noncongested to congested (N-C), and congested to 

noncongested (C-N).

Figure 47. I-10/Loop 375 Case Study – Lane-Miles with LOS Changes for Scenario 2.0 

*Conduct both I-10 and Loop 375 projects simultaneously.

Based on the analysis, it was concluded that conducting the construction projects simultaneously 

had similar impact on traffic as the I-10 project alone. The I-10 project had greater negative 

impact than the Loop 375 construction. A significant portion of traffic would be diverted to the 

southern portion of the Loop 375 crossing I-10 during the I-10 construction phase. Lastly, the 

two projects had similar impacts to the arterial traffic.

9.4 RURAL FREEWAY LANE CLOSURES USING MICROSIMULATION – CORSIM

Project Background

The following case study follows the research efforts and modeling analysis process employed 

by Schrock and Maze (2000) to evaluate the cost-effectiveness of delay-reducing work zone 

alternatives using microsimulation.(14) Schrock and Maze chose a completed Iowa DOT work 

zone project on rural Interstate 80 in Iowa County, Iowa, as a case study for this research effort 

and modeling analysis. This work zone project occurred in 1997 when a six-mile-long section of 
222 

I-80 was selected for pavement rehabilitation. Between 1988 and 1997, traffic levels on I-80 had 

increased by 44 percent. With age and increasing traffic levels, several of the older sections 

along this corridor were in great need of rehabilitation and repair. The Iowa DOT, therefore, 

proceeded with the pavement rehabilitation efforts in 1997, converting the facility from a fourlane highway to a two-lane, two-way operation (TLTWO) between May 31, 1997 until 

September 13, 1997, when the project was completed. Figure 48 depicts the study area location 

and detour routes used. 

Figure 48. I-80 Work Zone Study Area

Application of the MOTAA Process

The following sections describe how Schrock and Maze employed the MOTAA process into 

their research and modeling analysis of the I-80 pavement rehabilitation project in 1997. The 

process follows the MOTAA process, as described in Chapter 2 of this document, as well as the 

microsimulation modeling procedure described in Section 4.7. 

Step 1 – Problem Definition, Scope, Goals, and Objectives

The goal of this research and modeling effort was to evaluate different work zone alternatives 

through microsimulation in order to choose the most cost-effective strategy(s). Their objective 

was to compare the incremental benefits and costs of several hypothetical work zone alternatives 

against the actual 1997 I-80 pavement rehabilitation project to determine the alternative that 

would substantially reduce traffic congestion while efficiently using the resources allocated for 

the project. The researchers used the 1997 work zone data to develop a base case scenario that 

was then used to evaluate and compare the delays and costs associated with the various work 

zone alternatives.
223 

Step 2 – Establishing the MOEs and Thresholds 

After defining the goal and objective of the project, the next step in an MOTAA is to establish 

the MOEs and/or thresholds that would be used to evaluate and compare the different work zone 

alternatives. In order to compare and choose among these alternatives, the authors used MOEs, 

such as vehicle delay, travel time, user costs (costs due to delay), and agency costs. The 

researchers used these measures and converted them into dollar values for use in a benefit/cost 

analysis, which is described in further detail in Step 7. 

Step 3 – Choosing the Analysis Tool 

The next stage of the MOTAA process is to determine the type of analysis tool to be used and 

justification for the selection. In this case study, microscopic simulation was used to determine 

the amount of delay that would occur under different alternative scenarios during the lifetime of 

the work zone. Traffic Software Integrated System (TSIS) CORSIM package was chosen 

because of its ability to provide the data outputs and measures required for the benefit/cost 

analysis. The authors felt that obtaining these outputs would have been difficult to obtain from 

other analysis tools. 

Step 4 – Identify the Alternatives

After determining the MOEs and the analysis tool, the next stage of an MOTAA process is the 

identification of work zone alternatives. In this case study, the researchers developed several 

alternatives that were compared against the base case or “Do-Nothing Alternative.” The authors 

developed the following alternative scenarios: 

• Do-Nothing Alternative (Base Case) – This alternative represents the traffic 

management plan that was actually used during pavement reconstruction in 1997, the 

TLTWO work zone configuration. 

• Nonstop Work Alternative – The second alternative also represents a TLTWO work 

zone configuration, but at an accelerated pace by requiring the contractor to work 24

Analysis Tool Selection Options

Although performance measures is one factor that should be considered in determining which tool is appropriate 

for a work zone traffic analysis, there also are other factors that should be considered before making the final 

decision. Chapter 3 of this document describes these various factors in greater detail. The recommended factors 

for identifying the appropriate modeling approach include:

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• Transportation Management Plan (tool should be able to capture the impacts of various traffic control, 

operations, and mitigation strategies).
224 

hours per day, 7 days a week. A nonstop work schedule was assumed to lead to a 

completion date 56 days ahead of schedule, but at a premium of $1,000,000. 

• Four Traffic Lanes through the Work Zone – The third alternative provided two 

additional traffic lanes through the work zone by strengthening and widening the work 

zones. In order to accommodate two traffic lanes in each direction, four bridges were 

assumed to be widened in this scenario. Iowa DOT estimated that the cost of widening 

the shoulders into travel lanes and widening the four bridges would cost about 

$4,946,000. 

• Diversion Route Alternative – In this alternative, the Iowa DOT is assumed to divert a 

small percentage of vehicles off the interstate onto a diversion route. The diversion route 

consisted of using Iowa Highway 21 and U.S. Highways 6 and 151, as shown in 

Figure 48. This alternative also was divided into three sub-alternatives: 5 percent, 10 

percent, and 15 percent vehicle diversion. Because this alternative could increase the 

delay over the entire network, the interstate delay savings must outweigh the detour’s 

delay increases in order for this alternative to be beneficial. Assumptions regarding 

additional manpower and costs of running traveler information equipment and services 

were considered for determining the costs of each sub-alternative. The cost assumptions 

for the 5 percent, 10 percent, and 15 percent diversion sub-alternatives were $370,000, 

$470,000, and $670,000, respectively. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

At this stage of the MOTAA, the analyst already should have defined the objectives of the 

project and selected an appropriate tool that would be best for the project goals, objectives, and 

performance measures established. The next step of the MOTAA is defining the procedures 

necessary in preparing for modeling analysis. One of the first model preparation steps necessary 

is data collection. In this case study, the data collection efforts included: 

• Traffic Volumes – Interstate traffic volumes were collected from Iowa DOT’s Office of 

Transportation Data. Other sources of traffic volumes also include data from a 

permanent automatic traffic recorder (ATR) in the project area and on the detour route. 

• Truck Data – Based on a previous study of I-80, 20 percent of the traffic stream was 

assumed to be heavy trucks. 

• Congestion and Bottlenecks – Using data from Iowa DOT project files for the 1997 

pavement rehabilitation work, the research analysts determined the number of congested 

days that occurred during the project’s life span. According to their research, there were 

34 days of high-traffic volumes.

Demands (Entry Volumes and O-D Tables) Options

This case study did not require the need for O–D tables. When using other microsimulation software, O–D 

estimation may be needed. Refer to Chapter 4 of this document for further details regarding the O–D estimation 

process.
225 

Step 5B – Existing Conditions Model Development, and Step 5C – Existing Conditions 

Model Calibration 

The next stage of the microsimulation modeling process within an MOTAA procedure is 

typically the development of the existing conditions model, which often emulates the 

preconstruction condition in the project area. This is then usually followed by a model 

calibration effort. Because this case study conducts the MOTAA process after the project has 

been completed, the researchers do not build an existing conditions model. Instead they proceed 

to Step 5D, the work zone base conditions model development. 

Step 5D – Work Zone Base Conditions Model Development 

After the existing conditions model development and calibration effort, the following step in an 

MOTAA process is typically the development of the work zone base conditions model. If an 

existing conditions model already has been developed, the work zone conditions model usually 

involves the modification of the previous model using data regarding road and lane geometric 

configurations, signal controls, traffic demands, and capacity changes associated with the 

presence of the work zone. 

In this case study, the work zone base model or the “Do-Nothing” alternative was developed 

using the FRESIM component of the TSIS software package. The network model covers the sixmile section of I-80 with one lane blocked in each direction by an incident as a way of simulating 

the work zone location. The highways that make up the detour network were created using the 

NETSIM portion of the TSIS software. 

Step 5E – Model Calibration and Work Zone Calibration 

After the development of the work zone base conditions model, the next stage in the modeling 

effort is typically a work zone base conditions model calibration. The work zone model 

calibration’s purpose is to ensure that the model is sufficiently able to reproduce the local driver 

behavior, traffic characteristics, and work zone capacity during the project’s duration. Because 

the modeling efforts conducted in this case study occurred after the work zone project was 

completed, the authors were able to calibrate their Do-Nothing model alternative using data 

collected during the implementation of the pavement rehabilitation project. The model was 

calibrated to the traffic conditions present when the work zone was active using the traffic data 

specified in the data collection section.

To appropriately model traffic behavior within the work zone area, adjustments and tests were 

conducted by varying the modeling tool’s headway factor parameter values. To determine the 

appropriate headway adjustment factor, the researchers used a one-directional, two-lane 

interstate test section to run a sensitivity analysis that assessed the impacts of various values of 

Error Checking in Model Development

Although not specified in this case study, during model development, the analyst should make sure to perform 

error checks to identify and correct any model coding errors. The analyst can check the model network 

geometries and traffic control operations against existing plans or engineering drawings.
226 

the headway factor. A hypothetical work zone was placed in the middle of the test section 

closing one lane of traffic. The work zone section had a speed limit of 55 mph and the rest at 65 

mph. In this sensitivity analysis test, the headway factor was adjusted in increments of 10 

percent between the values of 20 percent and 60 percent. The first iteration of the test started at a 

headway factor of 20 percent. Traffic volumes were input into the model using the traffic 

generator at a rate of 1,000 vehicles per hour (vph) with 20 percent trucks. The volumes were 

then increased by small increments up to a maximum of 1,500 vph at regular time intervals 

during the simulation. The test was repeated for the other headway adjustment factor increments 

of 30 percent, 40 percent, 50 percent, and 60 percent. The results were then analyzed to 

determine when delays began for each percentage of headway increase. Figure 49 depicts the 

results of the delay analysis for the various headway factor increments.

The results of the sensitivity analysis showed that a 40 percent increase in work zone headway 

can be associated with a vehicle delay beginning at about 1,250 vph, as shown in Figure 49. The 

capacity associated with the 40 percent headway factor parameter agrees with the results of a 

study conducted by the Iowa State University’s Center for Transportation Research and 

Education (CTRE) on the capacity of rural Iowa interstate work zone, which showed typical 

work zone capacities between 1,216 to 1,302 vph. The 40 percent headway factor was, 

therefore, deemed most appropriate for the calibrated work zone model. 

Sensitivity Analysis Options

In this case study, the authors conducted a sensitivity analysis using varying headway expansion factors. Other 

driver behavior model parameters could also be used for sensitivity analyses and work zone base conditions 

model calibration processes. Chang and Zou (2009) conducted a sensitivity analysis using CORSIM testing 

three parameters: free flow speed, rubbernecking factors, and car-following sensitivity in order to calibrate a 

model to work zone conditions in the field. For further information on this research effort, refer to the Maryland 

State Highway Administration Research Report, Project SP708B4B, An Integrated Work-Zone Computer System 

for Capacity Estimation, Cost/Benefit Analysis, and Design of Control.

(60)
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Figure 49. Delay Associated with Varying Headway Expansion Factors 

Step 6: Alternatives Analysis

After the work zone model calibration, the analyst can proceed with modeling the alternatives 

analysis scenarios. The first step in this process is to create new models that reflect each 

alternative. This will require taking the “do-nothing” or base model and making the appropriate 

network model revisions. Afterwards the analyst will need to run the model alternatives multiple 

times, review the output, and extract relevant performance measures to be used in the next stage, 

the Decision-Making Framework.

Step 6A – Alternatives Model Development

CORSIM was used to develop and simulate the congested days condition for the six different 

alternatives (three alternatives and three sub-alternatives).

• Nonstop Work Alternative – For the nonstop work alternative, there were no geometric 

or control modifications to the Do-Nothing model. The only difference between this 

alternative and the Do-Nothing alternative was the project duration, since the nonstop 

alternative was completed 56 days earlier than the base case. The benefit associated with 

this alternative, therefore, is the value saved from those 56 days.

• Four Traffic Lanes Work Alternative – The only change this alternative required was a 

geometric revision to the base model through the addition of one additional lane in each 

direction. 

• Diversion Route Alternative – In this alternative, no geometric changes were made to 

the base case model. Instead there were three diversion sub-alternatives modeled: 5 

percent, 10 percent, and 15 percent diversion onto non-interstate traffic.
228 

Step 6B – Alternatives Analysis Model Runs

As previously mentioned, CORSIM software was used to perform the simulation runs for this 

stage of the analysis. Simulation runs were performed to model the 34 congested days for the six

different alternatives. Additionally, five simulation runs were conducted for each day and 

alternative using different random seed numbers. The average total delay for each of the model 

alternatives was calculated, as shown on Table 86. The table presents the average delay, 

standard deviation, minimum value, maximum value, and range of delay for each alternative. 

Table 86. Summary of Alternatives Analysis Modeling Results 

Alternative

Average 

Delay

Standard 

Deviation

Minimum 

Value

Maximum 

Value Range

Do-nothing 10,473 249 10,030 10,620 590

Nonstop work 1,428 11 1,416 1,439 23

Four-lane work zone 1,177 8 1,167 1,187 20

Five percent traffic 

diversion

7,092 18 7,071 7,111 40

Ten percent traffic 

diversion

4,589 290 4,426 5,107 681

Fifteen percent traffic 

diversion

3,029 149 2,913 3,218 304

Step 7 – Decision Framework and Recommendation of an Alternative 

After obtaining the model output and mobility performance measures through the modeling 

analysis, the next stage of the MOTAA process is the application of a decision-making 

framework or criteria for evaluating and identifying the preferred alternative. In this case study, 

the researchers used the simulation results in a benefit/cost analysis (BCA). For further 

information on BCA and other decision-making frameworks that can be used during an 

alternatives analysis, refer to Chapter 5 of this document. 

In order to convert the model delay values into dollar values, the authors used a previous Iowa 

DOT-sponsored study regarding the value of time. Table 87shows the 1997 dollar values 

associated with delay. To determine the incremental benefits of each alternative to the 

Do-Nothing Alternative, the researchers determined the travel time savings that could be 

achieved for each scenario by determining the difference between the delay associated with the 

base scenario and the work zone alternatives. The delay differences were then converted to 

dollar values using the travel time value estimates shown in Table 87. The incremental cost 

represents the difference between the alternative’s project costs when compared to the 

Do-Nothing Scenario. The comparisons between the incremental benefits and costs for each 

alternative are shown in Table 88. 

Number of Runs

In this case study, the authors decided upon five simulation runs. However, FHWA recommends an iterative 

approach that determines the appropriate number of model runs. For more information regarding this process, 

refer to Section 4.2 of this document or Traffic Analysis Toolbox Volume III.

(28)
229 

Table 87. Value of Travel Time Savings 

Minutes Average Value

0 to 5 $5.37

5 to 10 $8.06

11 or more $10.74

Table 88. Incremental Benefits and Costs for Each Alternative (in Thousands)

Do-Nothing

Diversion Nonstop 

5 Percent 10 Percent 15 Percent Work Four-Lane

Incremental Benefit $0 $480 $909 $1,123 $2,034 $2,026

Incremental Cost $0 $370 $470 $670 $1,000 $4,947

The BCA was completed in four stages as shown in Figure 50. In the first BCA step, the 

incremental benefits and costs of each alternative was compared to the Do-Nothing or Base Case 

Scenario. In the first step, the four-lane alternative was removed from consideration as the costs 

of the project far outweighed the benefits as seen by its BCA ratio of 0.41. The next option 

chosen in this step is the 5 percent Diversion alternative since it showed to have the lowest 

incremental cost as compared to the other alternatives. In the second iteration of the BCA, the 

incremental benefits and costs of each alternative are compared against the 5 percent Diversion 

alternative. The other alternatives still show a benefit over the 5 percent Diversion, with the 10 

percent Diversion as the next option. The iteration is continued until the optimal case is found. 

In this exercise, the Nonstop Work alternative has an incremental BCA ratio of 2.03 when 

compared against the Do-Nothing alternative, and continued to be the most cost-effective 

throughout several iterations of the BCA comparison steps, as shown in Figure 50. 
230 

Figure 50. Summary of Benefits and Costs for each Alternatives
231 

9.5 URBAN FREEWAY LANE CLOSURES USING HCM/DETERMINISTIC TOOLS –

FREEVAL

Project Background

The following case study, along Interstate Highway 40 in North Carolina was featured in a 

research report completed by the Institute for Transportation Research and Education (ITRE) and 

sponsored by the North Carolina DOT Research and Development Group.(61) The purpose of the 

research effort was to develop an analysis methodology for the evaluation of significant work 

zone impacts on North Carolina freeways using FREEVAL-WZ. The report demonstrates the 

capabilities of FREEVAL-WZ, a deterministic software tool that implements the freeway facility 

methodology featured in HCM 2010. 

The report utilized the I-40 road widening project (STIP I-4744) in order to demonstrate the use 

of FREEVAL-WZ. For the purposes of this document, the I-40 example will be used as a case 

study to demonstrate the MOTAA process with urban freeway lane closure projects using 

FREEVAL-WZ for analysis. This work zone project involved an 18-month road widening by 

adding travel lanes in each direction of I-40 between State Road 1728 (Wade Avenue, Milepost 

289) and the interchange with I-440/U.S. 1–64 (Milepost 293). The study area is shown by 

Figure 51. This segment of I-40 measures approximately 11.5 miles and includes a variety of 

different cross-sections (spanning two to four lanes per direction) of freeway segments, merge 

and diverge sections, and freeway weaving segments. 

Application of the Maintenance of Traffic Alternatives Analysis

Step 1 – Problem Definition, Scope, Goals, and Objectives

The research objective of the NC DOT report in conducting the work zone traffic analysis of the 

I-40 project was to demonstrate the use of FREEVAL-WZ and evaluate its capability to replicate 

field-observed work zone conditions. Therefore, the goals and objectives of this analysis differ 

from a case study project that would have or has been implemented in the field. An analyst may 

still use the methodology specified in this section in order to achieve certain project goals and 

objectives such as minimizing the mobility impacts on the freeway during construction.
232 

Figure 51. I-40 Case Study Project Overview 

Step 2 – Establishing the MOEs and Thresholds 

The facility MOEs reported for this case study included average travel time, mainline travel 

speed, and capacity. 

Step 3 – Choosing the Analysis Tool 

In the NC DOT report, the authors compared various analysis tools to FREEVAL-WZ. The 

authors first compared FREEVAL-WZ to other HCM/Deterministic tools. The authors note that 

while other deterministic tools such as QUEWZ-98 and QUICKZONE are limited to evaluating 

freeway segments, FREEVAL-WZ is able to evaluate demand changes on ramps and weaving 

segments in addition to freeways. Additionally, while the other tools have broader level, 

extended time periods for analysis, FREEVAL-WZ can be used as a peak hour analysis tool. 

Other differences between FREEVAL-WZ and other deterministic tools include output features 

and work zone-specific adjustments not available in other tools. These comparisons can be used 

by agencies and analysts to determine which deterministic tool is best suited for their project.

The authors also compared FREEVAL-WZ to simulation analysis tools such as mesoscopic and 

microscopic simulation software packages. While these tools can simulate and analyze the 

stochastic nature of traffic in relation to the work zone, there are certain advantages in using 

deterministic tools over simulation packages. For instance, the authors note that simulation tools 

can be more difficult to calibrate and are more data, time, and resource intensive as compared to 
233 

deterministic tools. Therefore, depending on the analysis objectives, the level of complexity and 

detail needed for the analysis, and resource needs, an agency can determine which tool would be 

best suited for their project. 

There also are new features and capabilities featured in FREEVAL-WZ that may add to an 

agency’s tool selection considerations. FREEVAL-WZ features a planning-level interface that 

allows agencies to customize the tool based on the agency’s needs and data resources. For 

instance, in the previous version of FREEVAL, traffic demand flows were required to be 

inputted into the tool as 15-minute increments. In the HCM 2010 version within the PlanningLevel Analysis Module, traffic flows can now be directly inputted as average annual daily traffic 

(AADT), which is commonly used in planning-level analyses.

The authors also provided a summary table for comparing the capabilities of FREEVAL-WZ and 

other analysis tools in capturing work zone-specific impacts, shown in Figure 52. 

Analysis Tool Selection Options

In this case study, FREEVAL was chosen based upon the tool’s capabilities in meeting the data and performance 

measures typically required in the agency’s work zone traffic analysis projects. Additionally, this option was 

also chosen based on the resource and data needs requirements of the tool as compared with other tool types. 

There are also additional factors that can be considered in selecting a tool. Chapter 3 of this document describes 

these various factors in greater detail. The recommended factors for identifying the appropriate modeling 

approach include:

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control and mitigation strategies.). 
234 

Figure 52. Work Zone Impacts and Analysis Tools 
235 

Step 4 – Identify the Alternatives

For the I-40 case study, several scenarios were developed, including an existing conditions 

scenario during the p.m. peak as well as nighttime and weekend lane closure scenarios. All of 

the lane closure scenarios (Scenarios 1 through 6) were evaluated from 9:00 p.m. to 12:00 a.m. 

Lane closures were scheduled to take effect at 9:00 p.m. but often did not start until 10:00 p.m. 

The lane configurations of the base and lane closure scenarios are featured in Figure 53. The six 

scenarios included the following: 

• PM Peak Base – This scenario was modeled for the 2010 p.m. peak hour. The base 

scenario was compared to conditions in August 2009 prior to the onset of construction at 

the work zone. 

• PM Peak Work Zone Base, Barrier Work – This scenario represents conditions during 

the p.m. peak when construction was active but without lane closures. In this case study, 

the work will be completed behind barriers while maintaining all travel lanes open for 

traffic, but at reduced shoulder widths. The purpose of the p.m. peak barrier scenario is 

to explore the impacts of the construction activities and reduced shoulder width. 

• Scenario 1 – Four- to Three-Lane Closure during the Off-Peak;

• Scenario 2 – Three- to Two-Lane Closure during the Off-Peak;

• Scenario 3 – Four- to Two-Lane Closure during the Weekend Off-Peak;

• Scenario 4 – Four- to One-Lane Closure during the Weekend Off-Peak;

• Scenario 5 – Three- to One-Lane Closure during the Off-Peak (FREEVAL segment 8 is 

closed); and

• Scenario 6 – Three- to One-Lane Closure during the Off-Peak (FREEVAL segment 16 is 

closed).

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

For the modeling effort of the I-40 case study, the authors developed a FREEVAL-WZ model 

that would include only the eastbound direction of the facility. In order to develop these models, 

the authors first had to conduct a data collection effort to obtain road geometry, volume, and 

traffic controls and operations data. 
236 

Figure 53. HCM 2010 FREEVAL-WZ, I-40 Case Study Scenarios 

(Source: Schroeder, Rouphail, Sajjadi, and Fowler,2011.) 

Data Collection for Development of Models 

The data collection effort for model development included inputs for the following: 

• Lane geometry information. 

• Traffic demand flows at all entry and exit points in 15-minute intervals – In order to 

develop the demand profile, the authors used the base year hourly data for the peak 

period. Using this information, the authors were able to develop a demand profile for the 

three-hour analysis period of 9:00 p.m. to 12:00 a.m. by converting the peak hour demand 

using a peak hour factor of 0.9. This effort, however, only represents volumes during 

off-peak base conditions and does not detail demand information for volumes during lane 
237 

closure conditions during the off-peak. In order to generate volume inputs for these 

conditions, the authors assumed a decreased demand pattern that was proportional to the 

peak hour distribution for each of the scenarios. The estimated demand profiles of the six 

work zone scenarios are shown in Figure 54. 

Figure 54. I-40 Work Zone Scenarios Demand Profiles 

(Source: Schroeder, Rouphail, Sajjadi, and Fowler,2011.) 

Data Collection for Validation and Development of FREEVAL-WZ Defaults

As previously mentioned, the main objective of the analysis was to evaluate how well 

FREEVAL-WZ could capture field-observed results. Therefore, in the initial stages of the 

project, significant traffic studies were performed in order to develop traffic stream models and 

capacity estimates for work zone operations specific to North Carolina. The initial studies 

involved an extensive data collection effort that analyzed sensor data regarding free-flow speeds, 

capacity, speed-flow relationships, segment speed and density, and travel time for various 

scenarios under freeway work zone conditions. During this stage of the modeling development 

and analysis, the authors utilized the following sources of information: 

• Work zone diaries – These were obtained from the construction contractors that worked 

on two projects in Raleigh, North Carolina – I-40 Widening and the I-40 Rehab Project. 

These diaries reported construction activities by mile posting location and date. The 

authors used these diaries as a guide for determining what data to download from 

Traffic.com, a site that stores mobility data collected by side-fire radar units in a central 

database.

• North Carolina DOT sensor data – The authors also obtained lane-by-lane data from 

NC DOT roadside sensors and 15-minute roadside sensor data (to confirm the time and 

location of when lane closures took effect).

• Field data travel times.
238 

The field data provided traffic operations information such as speeds, capacity, and travel times 

for various work zone scenarios (these included different work zone lane closure configurations 

at various times of the day). They also aided the authors in developing plots that explained 

expected temporal distributions and speed-flow relationships by work zone scenario. Finally, 

analysis of the data also contributed to the development of capacity adjustment factors for each 

of the work zone scenarios. Such data were used to develop defaults for FREEVAL-WZ and 

were used to validate model results against field observations.

Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model with the appropriate 

geometry, traffic controls, demands, and capacities using the information from the data 

collection effort. To input the network geometry into FREEVAL, the analysts followed the 

methodology outlined in the HCM 2010 that divides a freeway facility into separate segments 

within four categories: Basic Segment, On‐Ramp Segment, Off‐Ramp Segment, and Weaving 

Segment. As shown in Figure 55, the facility is divided into 20 analysis segments. As 

mentioned in the previous step, traffic volume data for the p.m. peak hour was received from 

North Carolina DOT. As specified in Step 5A, additional processing was required in order to 

develop demand profiles for the off-peak lane closure scenarios.

Step 5C: Existing Conditions Model Calibrations 

The base model calibration/validation effort was performed using the operational analysis 

function of FREEVAL-WZ. Calibration of peak hour conditions was conducted by comparing 

field observed data (specified in Step 5A) to FREEVAL-WZ base model performance. One of 

the key measures used for calibrating the base model was travel time. The travel time target was 

the average facility travel time for the three-hour analysis period and the maximum 15-minute 

travel time across the facility. Field observed travel times were compared to the model’s travel 

time outputs.

In addition to travel times, the base model also was calibrated to speed data. Model speeds were 

calibrated using a visual comparison of space-mean speed plots or speed contour plots. Speed 

contour plots were generated for the existing conditions model and the work zone base 

conditions model. These were then compared to speed contour diagrams developed for four 

peak hour weekday work zone scenarios (generated using sensor data). The speed contour plots 

comparison is shown in Figure 55. 

Model Calibration using Sketch-Planning and Deterministic Tools

Model calibration of sketch-planning and deterministic tools is typically conducted at a higher level and is often 

less time-consuming and resource-intensive as the calibration of macro-, meso-, and microscopic simulation 

models. The analysts’ calibration/validation procedure of comparing model outputs with field observations is 

typical for models developed with this tool type.
239 

Figure 55. Speed Contour Plot Comparisons 

(Source: Schroeder, Rouphail, Sajjadi, and Fowler,2011.) 
240 

Step 5D – Work Zone Base Conditions Model Development 

The PM Peak Work Zone Base, Barrier Work model follows the same configuration as the p.m. 

peak existing conditions model. The p.m. peak barrier scenario differs from the existing 

conditions model in that it represents construction activities along the corridor with reduced 

shoulder widths. 

Step 5E – Work Zone Base Conditions Model Calibration 

Calibration of the PM Peak Barrier Work model followed the same procedure as the existing 

conditions model. Calibration of the off-peak work zone scenarios were conducted using 

measures such as capacity, travel time, and speed contour plot comparisons. 

• Travel Time – The targets for the work zone scenarios also are average facility travel 

time for the three-hour analysis period and the maximum 15-minute travel time. 

Figure 56 summarizes the travel time results for each scenario based on field-observed or 

field-estimated values and FREEVAL model outputs before and after calibration. Before 

calibration, several of the model scenario results significantly differed from the fieldobserved travel times. The authors determined that the default HCM 2010 work zone 

capacity estimates underestimated the resulting travel time on the facilities. The authors 

made adjustments to the default capacity adjustment factors (CAF) and generated new 

model travel times results. As shown in the table, the after calibration model travel times 

appeared to be more closely matched to field-observed results. 

• Capacity – Following HCM 2010 methodology, a default work zone capacity and 

capacity adjustment factor (CAF) were estimated for each scenario. Following HCM 

guidance, the work zone scenarios were modeled by first reducing the number of lanes in 

the appropriate segments and then applying the CAF to each of the remaining lanes. 

After calibration (based on travel time results), a lower CAF was determined for most of 

the scenarios. The result of the capacity calibration step is detailed in Figure 57. 

• Space-Mean Speed Contours – The final calibration step was the visual comparison of 

field and FREEVAL speed contours. The speed contour comparisons are shown in 

Figure 58. Ideally, the model results should replicate the conditions in the field. As the 

figure shows, the model speed contour diagrams do not fully replicate the extent of the 

congestion shown in the field-based speed plots. However, the bottleneck locations 

identified in both the field and model speed contour diagrams do coincide. 

Figure 56. I-40 Case Study Work Zone Scenarios Travel Time Comparisons 
241 

Figure 57. I-40 Case Study Work Zone Scenarios Capacity Comparisons 

Step 6 – Alternatives Analysis

The alternatives analysis step involves two stages: 1) development of models to capture the 

scenarios or alternatives; and 2) description of how these models were run, the outputs extracted, 

and analysis of the results. 

Step 6A – Alternatives Model Development

The work zone alternatives or model scenarios were developed as a result of the work zone 

diaries obtained from the contractors (work zone diaries are described in further detail in 

Step 5A). Using the work zone diaries and sensor data, the authors were able to determine the 

lane closure scenarios that occurred during construction. The six-lane closure scenarios detailed 

in Step 4 resulted from this analysis. The inputs and geometric configuration of these six 

scenarios are shown in Figure 58. 
242 

Figure 58. I-40 Case Study Work Zone Scenarios Speed Contour Comparisons 

(Source: Schroeder, Rouphail, Sajjadi, and Fowler,2011.) 

Step 6B – Alternatives Analysis Model Runs and Results

The final step of the MOTAA is to run the work zone alternative models and evaluate the 

performance measures extracted. The analysts used MOEs such as capacity, travel time, and 

comparisons of space-mean speed contour diagrams. Details regarding how these measures were 

extracted from the model are provided in Step 5D. As previously mentioned, the main objective 

of the analysis is to determine the capability of the FREEVAL-WZ models to capture and/or 

replicate field-observed work zone conditions and operations. As a result, the work zone 

scenarios were compared to field-observed conditions/data instead of to each other. Results of 

these comparisons are shown on Figure 56, Figure 57, and Figure 58. 
243 

Because the purpose of the ITRE report and the case study was to demonstrate the use of 

FREEVAL for work zone traffic analysis, the authors do not provide a recommended alternative. 

Therefore, no decision-framework was applied.

9.6 URBAN FREEWAY LANE CLOSURES USING SKETCH-PLANNING TOOLS – 

ITS DEPLOYMENT ANALYSIS SYSTEM (IDAS)

Project Background

This case study features a benefit/cost (B/C) assessment of the temporary ITS used for the I-496 

reconstruction project conducted for Michigan DOT.(62) The Michigan DOT undertook a major 

effort to repair and rebuild parts of the I-496 corridor through downtown Lansing. The 

reconstruction project, which began in April 2001 with a total value of the investment of $42.4 

million, covered the I-496 corridor from the I-96 interchange on the west, to Trowbridge Road in 

the City of East Lansing. A map of the construction project is shown in Figure 59. 

Figure 59. I-496 Case Study Project Area Map

• Phase 1 – Scheduled for completion between April 2001 and August 2001. The eastern 

section of I-496 between U.S. 127 and Pine Street was entirely closed. This section is 

indicated in red in the maps shown in Figure 59. On northbound and southbound 

U.S. 127, two lanes were maintained at all times, except for a four-week period between 

Memorial Day and the Fourth of July, when U.S. 127 near Trowbridge Road was reduced 

to one. 
244 

• Phase 2 – Scheduled for completion during September and October 2001. During this 

phase, the western section of I-496 between Pine Street and I-69/I-96 was restricted to 

one lane. This section of the corridor is indicated in orange in the maps shown in 

Figure 60. During construction, this section was reduced to one lane of traffic in each 

direction. 

Figure 60. I-496 Reconstruction Project Phasing

Both stages of this massive highway construction project resulted in major changes in 

commuting patterns. Therefore, Michigan DOT embarked on a concerted effort to help 

employers and residents of the Lansing region cope with this major reduction in capacity of the 

region’s transportation system. In addition to public education and outreach campaign, the 

following were the major efforts that were undertaken by Michigan DOT to mitigate the impacts 

of the construction project: 

• Intelligent Transportation Systems (ITS) technologies were deployed to manage 

traffic and provide traveler information during the construction project. The system, 

dubbed the Temporary Traffic Management System (TTMS), utilized state-of-the-art 

traffic surveillance, detection, and communications technologies to manage traffic during 

the construction project. It provided real-time information to motorists to help them 

determine the best way to reach their destinations. Information on designated alternate 

routes (shown in green in the project maps) was distributed both in print and via 

electronic media as part of Michigan DOT’s public outreach effort; and 

• Signal system timing and operations on selected arterial corridors were upgraded to 

serve as alternate routes for the construction project. The type of improvements included 

traffic signal controller upgrades and signal interconnects between these intersections to 

implement traffic-actuated operations. Signal timing improvements were made to 

accommodate higher traffic volumes as a result of the closure of I-496. The signal 

timings on these corridors were adjusted periodically during the course of the project to 

manage the traffic diversions better.

This study focused on a benefit/cost (B/C) assessment of the TTMS used for the I-496 

reconstruction project. Michigan DOT intended to use the lessons learned through the TTMS 

deployment and its B/C evaluation to assist in decision-making for the procurement and 

deployment of such systems in the future. 
245 

Application of the Maintenance of Traffic Alternatives Analysis

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The primary goals and objectives of this case study were outlined as follows: 

• Identify the regional impacts of the TTMS on the roadway network in the Lansing region; 

• Identify the benefits of the TTMS to Michigan DOT, motorists, and the environment; 

• Perform a benefit/cost analysis for the deployment of the TTMS;

• Develop recommendations for Michigan DOT on the utility and relevance of ITS-based 

traffic management systems for improving mobility and safety in construction work 

zones; and 

• Identify the benefits of the arterial signal system improvements that were performed on 

the alternate routes.

Step 2 – Establishing the Measures of Effectiveness and Thresholds 

The following performance measures were used to identify the impacts and benefits of the 

system:

• User Mobility (Time-Savings) – These are time-savings realized by motorists through 

the use of the traveler information components of the TTMS, including the Portable 

Dynamic Message Signs (PDMS), video monitoring stations, and web-based traveler 

information. These time-savings are primarily realized through changing of travel routes, 

and (or) departure times. The mobility benefits of the TTMS are primarily realized by 

the road-users, but a significant portion of these benefits translates into customer 

satisfaction benefits that are realized by Michigan DOT. However, at that time, 

insufficient data were available to estimate the customer satisfaction benefits.

• Travel Time Reliability – These are time-savings realized by motorists under situations 

of nonrecurring congestion caused by incidents, such as crashes or vehicle breakdowns. 

ITS deployments that are aimed at improving safety and reducing the duration of 

incidents increase the reliability of the travel time in the region. The primary source of 

these benefits in the case of the Lansing TTMS are the incident detection/management 

components, including the CCTV-based incident management systems, traffic queue 

detectors and the associated communications, and information dissemination equipment. 

The construction work zone intrusion detection devices also indirectly contribute to these 

savings. Similar to the mobility benefits, travel time reliability benefits of the TTMS are 

realized primarily by road-users, but they also translate into both customer satisfaction 

and effective capacity utilization benefits for Michigan DOT. However, due to lack of 

sufficient data at that time, these benefits were not assigned explicitly to Michigan DOT.

• Fuel Consumption and Emissions – The ITS Deployment Analysis System (IDAS) 

model uses the performance statistics of the transportation network to estimate 

environmental performance measures. The model uses a series of detailed look-up tables 

that consider energy consumption and emissions rates based on specific network volume 

and traffic operating characteristics. The use of look-up tables provides the analyst with 
246 

the ability to incorporate updated emissions and energy consumption rates as they 

become available. IDAS incorporates emissions and energy consumption rates from 

currently available sources, including Mobile 5 and California Air Resources Board 

EMFAC. Fuel consumption and emissions benefits of the Lansing TTMS were realized 

through all the components of the TTMS working together. Fuel consumption savings 

were realized by the traveling public, and both fuel consumption and emissions savings 

were applicable to the environment and society in general.

• Accidents – The IDAS model provides estimates of changes in the number and severity 

of accidents resulting from the implementation of ITS strategies. Based on performance 

statistics calculated from the travel demand model runs, IDAS determines the safety 

benefits by using detailed accident rates using a series of look-up tables. Similar to the 

energy consumption and environmental benefits, the IDAS model is flexible to allow use 

of updated accident rates as they become available. The accident savings estimated 

through the Lansing TTMS were primarily due to its incident management components. 

Accident savings were realized primarily by Michigan DOT and other public agencies, 

through reduced accident handling costs, reduced personnel time, and efficient 

management of incidents and the associated ripple effects on traffic flow and safety. 

Accident savings also were realized by the traveling public and the society, through 

reduction in the number of fatalities (fatalities are downgraded to injury accidents 

through quicker identification and response to accidents). 

Step 3 – Choosing the Analysis Tool 

The analysis used IDAS, a tool used to estimate the regional impacts and benefits of ITS 

deployments. IDAS was developed by FHWA and is intended to make the estimation of impacts 

and benefits of ITS deployments compatible with the methods used for other transportation 

projects. It utilizes existing regional travel demand models as the primary inputs for the analysis. 

The IDAS model is equipped with a comprehensive “ITS Benefits Database,” which consists of 

nationally and internationally reported benefits of ITS deployments over several years. The 

IDAS ITS benefits database is the primary source of impacts and benefits.

Analysis Tool Selection Options

In this case study, IDAS was chosen based upon the tool’s capabilities in estimating the impacts and benefits of 

ITS deployments, which was required by the agency (Michigan DOT). There also are additional factors that can 

be considered in selecting a tool. Chapter 3 of this document describes these various factors in greater detail. 

The recommended factors for identifying the appropriate modeling approach include:

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• TMP (The tool should be able to capture the impacts of various traffic control, operations, and mitigation 

strategies). 
247 

Step 4 – Identify the Alternatives

This case study differs from a typical work zone analysis as it was specifically to conduct a 

benefit/cost assessment of the predetermined TTMS and arterial signal systems upgrades. 

Therefore, no other alternatives were analyzed.

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the modeling development and analysis, the analysts should define the 

objectives of the analysis and identify the data needed for the modeling analysis effort. For this 

case study, the following types of traffic data were collected:

• The presence and duration of traffic queues collected by the portable queue detectors;

• Travel time information collected by conducting test-vehicle travel time runs on alternate 

routes; and 

• Traffic volume information collected by the queue detectors and microwave detectors.

• Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model. The IDAS model was 

developed based on the travel demand networks and trip tables provided by the Tri-County 

Regional Planning Commission (TCRPC), which is the MPO for the three-county (Ingham, 

Eaton, and Clinton Counties) Lansing metropolitan region. The Lansing travel demand models 

were updated in 2000. 

Step 5B – Existing Conditions Model Calibration 

The IDAS model was validated by comparing the Vehicle-Miles Traveled (VMT) values 

generated by IDAS with those from the regional travel demand model. 

Step 5C – Work Zone Base Conditions Model Development

The travel demand models provided by TCRPC were then altered to reflect the freeway closure 

and lane closure of the two phases of the I-496 reconstruction project. A similar analysis 

performed by the Michigan DOT Central Office on an older version of the Lansing travel 

demand model was used as the template for representing the field conditions during the 

construction project. The disbenefits or negative impacts of the I-496 reconstruction project, 

obtained by running these networks in IDAS were documented. 

Model Calibration using Sketch-Planning and Deterministic Tools

Model calibration of sketch-planning and deterministic tools is typically conducted at a higher level and is often 

less time-consuming and less resource-intensive than the calibration of macro-, meso-, and microscopic 

simulation models.
248 

Step 5D – Work Zone Base Conditions Model Calibration 

There was no calibration data available for this work zone scenario. Therefore, there was no 

calibration efforts conducted that compared the work zone base with field conditions or with 

results from previous work zones of similar types. 

Step 6 – Alternatives Analysis

The ITS components were deployed on each of the travel demand models, and IDAS was run to 

estimate the impacts and benefits of the deployment. The impacts of the deployments on the 

different travel performance measures were documented and the benefit/cost ratio was then 

calculated by comparing the benefits of the deployments with the total cost.

As described earlier, the benefits of the Temporary Traffic Management System (TTMS) include 

four categories: 1) user-mobility savings; 2) travel time reliability savings; 3) accident savings; 

and 4) emissions savings. For instance, the benefits for Phase 1 of the TTMS and the Arterial 

Signal System Upgrades are summarized in Figures 61 and 62, respectively.

Because the purpose of the study was to conduct a benefit/cost assessment of the predetermined 

TTMS and arterial signal systems upgrades for work zone traffic analysis, no alternatives were 

available and, therefore, it was not necessary to provide recommended alternative. Hence, there 

is no decision-framework applied. 
249 

Figure 61. I-496 Case Study – TTMS Benefits Summary (Phase 1) 
250 

Figure 62. I-496 Case Study – Arterial Upgrades Benefits Summary (Phase 1) 

9.7 TEMPORARY TWO-WAY ONE-LANE OPERATION ON BRIDGES USING 

TRAFFIC SIGNAL OPTIMIZATION TOOLS – SYNCHRO

Project Background

This case study, featured in the MD SHA Work Zone Analysis Guide (2008), describes the 

agency’s recommended MOTAA procedures for analyzing a two-way, one-lane bridge operation 

work zone project.(15) The hypothetical project is located on MD 23, a two-lane, two-way 

roadway that runs in the east-west direction over Morse Road. As with the flagging operations 
251 

case study featured in Section 9.1, the main purpose of this and other case studies featured in the 

MD SHA Work Zone Analysis Guide is to demonstrate the application of the recommended 

MOTAA approach and guidelines.

Figure 63 depicts the study area location and nearby facilities. The nearest intersection to this 

bridge is where MD 23 terminates at MD 165, as shown in the project area map. There are no 

other access points between MD 23 and MD 165 west of Morse Road. The proposed work is to 

reconstruct the full length (100 feet) of the bridge.

Application of the MOTAA Process

Step 1 – Problem Definition, Scope, Goals, and Objectives 

As previously mentioned, this case study features the reconstruction of the MD 23 bridge over 

Morse Road. Because of the lack of access, there are no detour routes available, posing a 

challenge to mitigating the mobility impacts of the construction work. It is, therefore, assumed 

that the construction work would need to be accomplished through a two-stage process, where 

one lane at a time would be closed on the bridge, permitting traffic to flow on the other lane. 

For this bridge reconstruction case study, the objective of the analysis is to determine if the 

reconstruction of the bridge performed using the one-lane, two-way bridge operations with 

traffic signals on either end could meet the mobility thresholds. Although this process notes that 

traffic signals would be used to regulate the flow of traffic on the one open lane, the analysis 

procedure featured in this section also could be applied for flagging operations. 

Figure 63. MD 23 Case Study Project Area Map 
252 

Step 2 – Establishing the Measures of Effectiveness and Thresholds 

In this case study, the mobility threshold is set at a 15-minute travel time increase limit. The 

measures of effectiveness included control delay and travel time.

Step 3 – Choosing the Analysis Tool 

Most arterials and freeways across the State of Maryland have been modeled using Synchro and 

CORSIM, respectively. To reduce data collection and model development efforts, Synchro was, 

therefore, chosen as the analysis tool. 

Step 4 – Identify the Alternatives

In this case study, the agency did not predetermine a set of alternatives earlier on in the project. 

The first work zone scenario evaluated is the one-lane, two-way bridge operations with traffic 

signals on either end. A second work zone alternative is established should the primary scenario 

not meet the threshold. In this case study, no second work zone scenario was established 

because the primary scenario met the mobility thresholds, as shown in Step 6. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

The goals and objectives of the analysis were described in Step 1 of this section. After 

determining the goals and objectives of the analysis, the next step includes determining the scope 

of the analysis (identifying the geographic boundaries of the study area) and the data inputs 

required. 

• Scope – The study area includes the full 100 feet length of the bridge and does not 

include any adjacent intersections since there are no nearby detour routes available. 

Analysis Tool Selection Options

For this case study, Synchro was chosen because it reduced agency resource requirements and data collection 

efforts. Chapter 3 of this document describes other factors that can be considered when determining what type 

of analysis tool to use for the MOTAA. The recommended factors for identifying the appropriate modeling 

approach include:

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control, operations, and 

mitigation strategies.).
253 

Additionally, a queue length approximation analysis showed that expected queues were 

not anticipated to extend to other intersections in the study network. 

• Traffic Counts – Traffic counts from February of 2004 were obtained from MD SHA. 

Traffic volumes were then adjusted to April (the heaviest travel month of the year) 

equivalent values. Additionally, a growth factor of 2.6 percent also was applied to the 

traffic counts in order to forecast volumes for the year of the analysis, 2007. 

Step 5B – Existing Conditions Model Development 

Because there were no existing intersections at or near the study area, no model was created for 

existing conditions. 

Step 5C – Existing Conditions Model Calibration 

No existing conditions model was built. Therefore, there was no need for an existing conditions 

calibration process. 

Step 5D – Work Zone Base Conditions Model Development 

The next step in the MOTAA is the development of a work zone base conditions model. The 

one-lane bridge operations layout is shown in Figure 64. Model development for this case study 

includes considerations for geometric and signal controls. 

• Road Geometry – The work zone area is expected to be 1,050 feet long, based on a 

roadway speed of 45 mph, the buffer length of 360 feet, and the taper of 270 feet. To 

create the model in Synchro, two intersections were placed 1,050 feet apart along the link 

representing roadway, MD 23. 

• Traffic Signal Control – The two intersections were coded as fully actuated signal 

controlled. 

• Clearance Interval – Clearance timings for the two intersections were developed 

using MD SHA’s Policy for Determining Yellow Timings at Intersections and the 

Institute of Transportation Engineers (ITE) Manual of Traffic Signal Design.

(15,62) 

The results of the clearance interval calculations are shown in Table 89. 

• Cycle Length – The cycle length for the two signals were determined based on the 

requirement that the queues in each direction should clear during each cycle. This 

type of control parallels flagging operations since the flagger will only stop a 

particular direction once the queue has cleared. Cycle lengths were determined to be 

250 seconds for the a.m. peak and 220 seconds for the p.m. peak. The equation used 

for determining the minimum cycle lengths is shown by the expression: 

Model Calibration and Validation Options

If an existing conditions model calibration process is needed, each traffic signal optimization software package 

has a set of user-adjustable parameters that enable the analyst to calibrate the software to better match specific 

local conditions. The calibration process involves the selection of a few parameters for calibration and the 

repeated operation of the model to identify the best values for those parameters. Additionally, each agency and 

project may have different calibration guidelines and requirements. Section 4.4 of this document provides 

guidance on the considerations and steps involved in the calibration of traffic signal optimization models.
254 

(5) 

Where:

C = Total cycle length;

G = Cumulative green time per cycle for both directions; 

V = Total hourly volume for both directions; and 

CL = Total clearance interval for each direction.

Table 89. MD 23 Case Study – Clearance Interval Calculations 

Interval Equation

Duration

(Seconds)

Yellow (Y) 4.5

All-Red (AR) 21

Total Clearance 25.5

Vposted = Posted speed limit (45 mph); t = Perception-reaction time; W = Total work zone length; L = Average 

vehicle length; and Voperating = Operating speed (35 mph).

Figure 64. MD 23 Case Study – One-Lane Bridge Operations Layout 

(Source: Maryland State Highway Administration, 2008.)

Step 5E – Work Zone Base Conditions Model Calibration 

No work zone base conditions model calibration was conducted for this case study. 

Work Zone Base Conditions Model Calibration Options

Work zone model calibration includes the calibration of work zone capacity and performance measures. Work 

zone model capacity and performance measures can be calibrated to field data and/or prior experiences (from 

similarly implemented work zone projects). In order to calibrate to field data or case studies of similar work 

zone types, the analyst will evaluate the work zone’s queues, travel times, delays, and speeds. Additionally, the 

analyst may study parameters related to driving behaviors in work zones of similar types. Such observations and 

measures will aid the analyst in the work zone calibration process.
255 

Step 6 – Alternatives Analysis

The next step is to perform model runs in order to analyze the different alternatives. The 

analysis involves running the model and obtaining outputs and measures such as control delay. 

The control delay results for the one-lane bridge operations during both peak periods are shown 

on Table 90. 

Table 90. MD 23 One-Lane Bridge Operations – Synchro Model Outputs

Approach

Control Delay (Seconds)

AM Peak PM Peak

Eastbound 195.2 221.4

Westbound 188.3 196.2

The next step applied for this case study was to determine if the alternative meets the agency’s 

established mobility thresholds. According to the mobility thresholds for arterials established in 

the MD SHA Work Zone Analysis Guide, the work zone travel time cannot increase more than 

15 minutes over the existing condition’s travel time.(15) Based on the Synchro results, the control 

delay outputs are expected to be less than 3.7 minutes per vehicle for both approaches during 

either peak period. The control delay is, therefore, equal to the expected increase in travel time 

through the work zone and satisfies the travel time increase limit. No other alternative was, 

therefore, developed and evaluated. 

9.8 SIGNALIZED CORRIDOR LANE CLOSURES USING TRAFFIC SIGNAL 

OPTIMIZATION TOOLS – SYNCHRO/SIMTRAFFIC

Shady Grove Road, Maryland 

This case study, featured in the Maryland State Highway Administration’s (MD SHA) Work 

Zone Analysis Guide, describes the agency’s recommended MOTAA procedures for analyzing a 

hypothetical work zone project along the signalized corridor Shady Grove Road.(15) The main 

purpose of this and other case studies featured in the MD SHA Work Zone Analysis Guide is to 

demonstrate the applications of the agency’s recommended MOTAA approach and guidelines. 

This work zone example features the reconstruction of sidewalks, curbs, and gutters along the 

southbound direction of Shady Grove Road. Shady Grove Road is a six-lane, two-way, divided 

roadway running in the north-south direction. During construction, the right lane of the 

Alternative Analysis Options

The alternative analysis process may differ based on the agency and the project. In this case, a second work 

zone alternative was not considered because the primary alternative met the mobility thresholds. Another 

alternatives analysis approach may involve analyzing several alternatives concurrently. Additionally, an analyst 

can compare and choose among various alternative’s different types of performance measures. In this situation, 

the criteria for selecting the preferred alternative depended solely only on mobility thresholds. For other 

decision-making framework options, refer to Chapter 5 of this document.
256 

southbound direction of Shady Grove Road is closed, reducing the number of lanes from three to 

two. The lane closure will occur between the intersection of Shady Grove with Comprint Court 

and Gaither Road. 

Figure 65. Shady Grove Road Work Zone Area Map 

(Source: Maryland State Highway Administration, 2008.)

Application of the MOTAA Process – Shady Grove Road 

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The first step in an MOTAA is to define the goals, and objectives of the analysis. The goals and 

objectives of the work zone analysis include the following: 

• Determine if a lane closure can be permitted along Shady Grove Road (determining 

whether or not the work zone configuration will meet the arterial mobility thresholds 

established by the agency); and

• Determine what work hour restrictions are required to meet the mobility thresholds. 

Step 2 – Establishing the MOEs and Thresholds 

After defining the goals and objectives of the project, the next step in an MOTAA is to establish 

the measures of effectiveness and/or thresholds that would be used to evaluate and compare the 

different work zone alternatives. In this case study, the measures of effectiveness (MOE) are 

derived from the mobility thresholds established by MD SHA. The established thresholds 

compare pre-construction and work zone scenarios using measures such as travel time, control 

delay, and LOS. 
257 

Step 3 – Choosing the Analysis Tool 

The next stage of the MOTAA process is to determine which traffic analysis tool is best suited 

for the project and to detail the justification for that selection. Because MD SHA has developed 

models for most arterials and freeways across the State using Synchro and CORSIM, the 

Synchro/SimTraffic package was chosen as the analysis tool for this and other arterial case 

studies featured in the MD SHA Work Zone Analysis Guide. Using these pre-developed models 

served to reduce data collection and model development efforts.

Step 4 – Identify the Alternatives

After determining the MOEs and the analysis tool, the next stage of an MOTAA process is the 

identification of work zone alternatives. In this case study, a red flag analysis is first conducted 

in order to determine if the intended work zone configuration/schedule will meet the mobility 

thresholds. Should it not meet the mobility threshold, the work zone alternative will be modified 

to ensure the mobility threshold is reached. For example, in the Shady Grove case study it was 

assumed that the construction work and lane closure would occur during the midday peak period. 

Should this fail the red flag analysis, the alternative would be to evaluate the work zone as a 

weekend construction period. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the MOTAA, the analysts should identify the scope of the analysis, 

including the level of data collection effort needed. Establishing the scope of the analysis 

requires defining the limits of the study network. Because the work zone lane closure includes 

the area between the two signalized intersections, the analysis study area must include the 

impacts to these two intersections, as well as the mobility impacts that extends upstream of the 

work zone due to the lane closure. 

A queue length analysis using field data collected (including a.m. peak hour traffic volumes and 

signal timings) also was performed in order to determine how far upstream of the work zone 

Analysis Tool Selection Options

Chapter 3 of this document describes several other factors that should be considered when choosing a traffic 

analysis tool. The recommended factors for identifying the appropriate modeling approach include:

• Project goals and objectives;

• Work zone characteristics;

• Agency resources;

• Performance measures;

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control, operations, and 

mitigation strategies.).
258 

should the analysis extend. Based on this queue length analysis, the analysts determined that the 

work zone analysis should extend at least 7,000 feet upstream of Gaither Road, the southernmost 

point of the lane closure. The recommended work zone analysis area is shown on Figure 66. No 

detour routes have been included in the analysis area as there are no nearby parallel routes. Also 

as shown by the figure, the study network extends from Choke Cherry Road to the EB I-370 

On-ramp.

In addition to the identification of the size of the study area and the scope of the analysis, the 

analysts at this stage must identify the data needs required to model the alternatives. Data 

sources can include road geometry, signal timing plans, traffic demands, capacities, travel times, 

and queues. The following summarizes the data inputs to develop and run the 

Synchro/SimTraffic models:

• Road Geometry – Aerial imagery and field observations were used to confirm road 

geometry features.

• Traffic Controls – Existing signal timings for all signalized intersections in the study 

area were obtained from the Montgomery County Transportation Center. 

• Traffic Volumes Data – Turning movement counts for the intersection of Shady Grove 

Road and Frederick Road (MD 355) were obtained from MD SHA. The turning 

movement counts for the intersection with Choke Cherry Road were obtained from a 

traffic study sponsored by the Montgomery County TMC. For the remaining 

intersections, TMCs were obtained from field data collection. 

• Additional Field Observed Data – Additional data collected on the field included queue 

lengths, lane utilization, and truck percentages. 
259 

Figure 66. Shady Grove Road – Extent of Analysis

(Source: Maryland State Highway Administration, 2008.)

Field Data Collection Considerations

Although not specified in this case study, there are certain factors that should be considered when collecting 

volume and demand information from the field. First, the field analysts must consider the time periods when 

best to collect the data. For instance, if the work zone is to be completed during peak or off-peak periods, the 

time period for field data collection should be customized for when the work zone is planned/programmed to be 

active.

Second, analysts should consider the variations of daily traffic flow patterns and how this phenomenon can be 

captured through the data collection. In order to replicate the stochastic nature of traffic, the field analysts 

should plan to capture data through several days and/or multiple time periods. They should also consider the 

type of travel days (weekdays versus weekends) when data should be collected.
260 

Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model with the appropriate 

geometry, traffic controls, demands, and capacities using the information from the data 

collection effort described in Step 5A. As previously mentioned, the MD SHA has coded most 

arterials in the State using Synchro. Typically, these can be used for the existing conditions 

model granted the study area already has been coded in these pre-developed networks. However, 

there were no existing Synchro models developed for the Shady Grove study area. Therefore, a 

new model was coded using information from the data collection effort. 

Network coding of the study area was validated by field observations and supplied data by local, 

regional, and state agencies. The analysis time period also was determined from field observed 

traffic conditions. According to a fatal flaw analysis of these observations, the existing 

congestion level along Shady Grove Road in both the a.m. and p.m. peak periods would make 

the lane closure during these time periods infeasible. Therefore, the agency decided that 

construction needed to be conducted during the midday. As a result, all model scenarios were 

analyzed during the midday peak. Hence, only midday peak period signal timing, volume, and 

traffic conditions data were relevant for model development. 

Step 5C – Existing Conditions Model Calibrations 

After the development of an existing conditions model, the following step in the MOTAA 

process is model calibration. Calibration ensures that the operational performance of the model 

replicates field observed conditions. In the Shady Grove case study, the model was calibrated 

using peak hour factors, truck percentages, and lane utilization factors. Once calibrated to these 

factors, the model was validated to reflect the queue conditions and travel times observed on the 

field.

In order to obtain model outputs for the base condition preconstruction, five SimTraffic 

simulation runs of the existing conditions model were performed. The model outputs extracted 

included control delay, LOS, and travel time. The existing conditions model outputs are shown 

in Table 91. 

Table 91. Shady Grove Existing Conditions Model Outputs

Intersection

Control Delay

(Seconds) LOS

Choke Cherry Road 10.1 B

Gaither Road 34.4 C

Comprint Court 9.4 A

Pleasant Drive 27.6 C

MD 355 (Frederick Road) 72.2 E

Solid Waster Entrance 3.7 A

The Great Indoors Entrance 4.0 A

Oakmont Avenue 36.0 D

Crabbs Branch Way 35.9 D

EB I-370 On-ramp 1.7 A

SB Travel Time: EB I-370 On-ramp to Choke Cherry Road 6.8 minutes
261 

Step 5D – Work Zone Base Conditions Model Development 

The next stage of the MOTAA is the work zone base conditions model development. If an 

existing conditions model was developed it can typically be modified to develop the work zone 

base conditions and alternative models. The first work zone alternative or work zone base 

scenario for this case study is the right lane closure in the southbound direction of Shady Grove 

Road during the midday peak. As shown on the work zone layout on Figure 66, the lane closure 

occurs between the intersections of Shady Grove and Gaither Road and Comprint Court. The 

work zone layout also includes a buffer length of 360 feet and a merging taper of 495 feet. The 

main changes between the existing conditions model and this work zone scenario include the 

geometric changes needed to incorporate the lane closure into the network. No changes were 

made in the O-D data and traffic volumes.

Step 5E – Work Zone Base Conditions Model Calibration 

No work zone base conditions model calibration was conducted for this case study. 

Work Zone Base Conditions Model Calibration Options

As specified in Chapter 4 of this document, work zone model calibration includes the calibration of work zone 

capacity and performance measures. Work zone model capacity and performance measures, such as queue, 

travel time, delay, and speeds, can be calibrated to field data and or prior experiences (from similarly 

implemented work zone projects). Additionally, work zone base conditions calibration also entails that the 

analyst evaluate and identify the appropriate modeling parameters to use based on prior experiences modeling or 

analyzing work zones of similar types. Such observations and measures will aid the analyst in the work zone 

calibration process.

Additional Considerations for Model Calibration

There were few details provided regarding the model calibration procedure used in this case study. Chapter 4 of 

this document provides further details on recommended calibration procedures for models developed using 

traffic signal optimization tools. The following also lists some of the important components of a model 

calibration process:

• Model Parameters – One of the critical steps in the model calibration process is the adjustment of global 

and link-specific parameters to ensure the model behaves similarly to field conditions.

• Model Runs and Random Seeds – After the adjustments to the model, the analyst will have to extract 

outputs from the model that can be used to compare against field data. In order to mimic the stochastic 

nature of traffic analysts may need to conduct several runs of the model. Chapter 4 of this document 

presents one way of determining the appropriate number of model runs. However, an agency may also 

specify a standardized number of runs or methodology to be used in during analysis.

• Calibration Acceptance Criteria – The criteria should include mathematical targets related to traffic 

volumes and speeds that could be used to compare the model with field observed conditions. In the 

calibration process, the analyst should also determine and specify the target values that the model results/

outputs should achieve. Example criteria could include achieving model volumes and speeds within a 

certain percent difference of the field data.
262 

Step 6 – Alternatives Analysis

The alternatives analysis step typically involves two stages: 1) development of models to 

capture the scenarios or alternatives; and 2) description of how these models were run, the 

outputs extracted, and analysis of the results. In this case study, model simulation results were 

first extracted from the developed work zone base case scenario prior to the development of the 

second work zone alternative. 

Step 6A – Alternatives Model Development, and Step 6B – Alternatives Analysis Model 

Runs and Results

As previously mentioned, the work zone base case scenario model was run before the 

development of the second work zone alternative, the weekend work zone model scenario. 

Similar to the existing conditions model, five SimTraffic simulations were run for the work zone 

base case. The measures extracted from these model runs are featured in Table 92. 

Table 92. Shady Grove Road – Work Zone Base Model Outputs

Intersection

Control Delay

(Seconds) LOS

Choke Cherry Road 9.9 A

Gaither Road 96.4 F

Comprint Court 13.6 B

Pleasant Drive 52.5 D

MD 355 (Frederick Road) 72.2 E

Solid Waster Entrance 3.7 A

The Great Indoors Entrance 4.0 A

Oakmont Avenue 36.0 D

Crabbs Branch Way 35.9 D

EB I-370 On-ramp 1.7 A

SB Travel Time: EB I-370 On-ramp to Choke Cherry Road 15.0 minutes

The next step is to determine whether the work zone base model results meet the MD SHA 

mobility thresholds for arterials shown on Figure 67. The intersections with performance 

measures shown bolded in red in Table 92 have failed to meet the mobility thresholds. For 

instance, the Gaither Road intersection that had a control delay of 34.4 seconds and an LOS C 

during the existing conditions model (as shown in Table 91), must meet the mobility threshold of 

a maximum of LOS D and control delay that must be less than or equal to 45 seconds in the work 

zone conditions model. Since the level of service of this intersection worsened to an LOS F and 

increased to a control delay greater than 45 seconds, the intersection failed to meet the mobility 

threshold. The results indicated another work zone alternative or mitigation measures should be 

considered. 

Because of the nature of the work, the agency did not think it was feasible to consider other 

alternatives, such as reversible lanes or full lane closures, which would cause greater mobility 

impacts and require a greater amount of resources. Therefore, the agency decided that only work 

zone alternatives that involved lane closures during the off-peak hours would be feasible. The 

second alternative, therefore, considered was weekend construction. 
263 

Figure 67. MD SHA Work Zone Analysis Guide Mobility Thresholds for Arterials 

Weekend Work Zone Alternative Model Development and Results

In order to develop the weekend work zone model, weekend data such as weekend peak hour 

counts needed to be collected. The weekend peak hour was assumed to occur on Saturday 

midday. The existing conditions and work zone base conditions model were modified to create 

this work zone alternative by revising the model volumes and signal timings to reflect the 

weekend conditions. All lane configurations and geometric network features remained the same. 

Similar to the existing and work zone base models, five simulations also were run for the 

weekend work zone scenario. Table 93 shows the existing conditions performance measures and 

the weekend work zone model results in parentheses. Based on the results shown in this table, 

all of the intersections met the mobility thresholds. The overall corridor also met the travel time 

thresholds. 

Table 93. Shady Grove Road – Existing (Weekend Work Zone) Model Outputs

Intersection

Control Delay

(Seconds) LOS

Choke Cherry Road 39.9 (39.3) D (D)

Gaither Road 31.6 (33.7) C (C)

Comprint Court 8.7 (10.7) A (B)

Pleasant Drive 30.6 (31.9) C (C)

MD 355 (Frederick Road) 87.8 (87.8) F (F)

Solid Waster Entrance 4.3 (4.3) A (A)

The Great Indoors Entrance 6.7 (6.7) A (A)

Oakmont Avenue 20.5 (20.5) C (C)

Crabbs Branch Way 24.4 (24.4) C (C)

EB I-370 On-ramp 0.6 (0.6) A (A)

SB Travel Time: EB I-370 On-ramp to Choke Cherry Road 5.6 minutes (6.5 minutes)
264 

Step 7 – Decision Framework and Recommendation of an Alternative 

The final stage of the MOTAA is the recommendation of the preferred alternative. The decision 

criteria for the MD SHA case studies are based on whether the alternative meets the mobility 

thresholds. Based on the results shown in Table 93, the weekend work zone scenario is the 

recommended alternative.

9.9 RAMP CLOSURES AT MAJOR INTERCHANGES USING TRAFFIC SIGNAL 

OPTIMIZATION AND MICROSIMULATION – SYNCHRO AND PARAMICS

I-465 West Leg Reconstruction Project Background 

This case study is based on the I-465 west leg reconstruction project in Indianapolis, Indiana as 

detailed in HCM 2010 Manual, Volume 4, Case Study 6.(64) The reconstruction along I-465 is 

about nine miles in length with a total of eight interchanges. Figure 68 provides an overview of 

the location and extents of the project. 

This section provides an overview of the analysis methodology employed in evaluating the 

mobility impacts of the interchange reconstruction on one segment of the I-465 west leg 

reconstruction, the Rockville Road Interchange Reconstruction Project, shown in Figure 69. 

Decision Framework Options

This case study’s decision-making framework is based primarily on the mobility measures extracted from the 

Synchro/SimTraffic models. Although these results may be used in choosing a preferred scenario, there also are 

additional factors that an agency may want to consider when evaluating and comparing the alternatives. These 

factors are described in further detail in Chapter 7 of this document. A decision-making framework that 

incorporates these factors, as well as the mobility measures, can then be used to develop a methodology or 

criteria that can be used to compare the alternatives and choose a preferred option. Chapter 5 of this document 

features several different decision framework options that can fit projects of different complexities and 

resources.
265 

Figure 68. I-465 West Leg Reconstruction, Indianapolis Overview 

(Source: Transportation Research Board, 2010.) 
266 

Figure 69. I-465 Case Study Rockville Road Interchange Reconstruction Study Area 

(Source: Transportation Research Board, 2010.) 

I-465 West Leg Reconstruction Project – Application of the Maintenance of Traffic 

Alternatives Analysis Process

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The main purpose of this case study as featured in HCM 2010, Volume IV was to demonstrate 

the systemwide analysis of freeway and arterial traffic together with various lane closure options 

through the use of traffic analysis tools and procedures. Because this case study was featured in 

HCM 2010 for a specific purpose, the agency’s goals and objectives at the time of the 

reconstruction were not clearly stated. However, an example goal of an agency conducting a 

similar effort may be to find the optimal lane closure configuration that can minimize the 

mobility impacts of the work zone without sacrificing construction schedule/duration. 

Tradeoffs typically exist between the duration of construction and the level of closures. While 

lane closures can improve construction duration, the mobility impacts can generate huge 
267 

disbenefits to motorists, neighborhoods, and businesses. The objective of the modeling 

analysis, therefore, could be to use the performance measures generated from the analysis 

to compare work zone configuration options and determine the optimal alternative for the 

interchange reconstruction.

In addition, to identifying the goals and objectives of the project, another key part of this stage of 

the MOTAA is to identify the scope and extents of the analysis. The analysis extent covers the 

area shown in Figure 70. In this case study, five on- and off-ramps of the Rockville Road 

interchange will be closed during construction. These include four loop ramps and the I-465 NB 

to Rockville Road EB off-ramp. Ramps that are closed are marked by an “x” as shown in 

Figure 70. 

Figure 70. Rockville Road/U.S. 36 Interchange Ramp Closures 

(Source: Transportation Research Board, 2010.) 

Step 2 – Establishing the Measures of Effectiveness and Thresholds 

After defining the goals and objectives of the project, the next step in an MOTAA is to establish 

the measures of effectiveness and/or thresholds that would be used to evaluate and compare the 

different work zone alternatives. For this case study the mobility performance measures chosen 

include average speed and average number of vehicles present in the network, as well as average 

and cumulative network delays. Another measure that was considered in this case study was 

construction duration. 

Rockville Rd. 
268 

Step 3 – Choosing the Analysis Tool 

The next stage of the MOTAA process is to determine the type of analysis tool to be used and 

the justification for that selection. In this case study, PARAMICS was selected as the simulation 

tool and Synchro was used to develop signal timing plans. According to the HCM 2010 write-up 

of this case study, HCM methodologies are not robust enough to generate results and 

performance measures that could aid in identifying the lane closure alternative that minimizes 

impacts while improving construction duration. Therefore, the combination of traffic signal 

optimization and microsimulation analysis tools was used. 

Additionally, the analysts wanted to report measures at different levels of analysis (link-, 

facility-, and systemwide). While operational measures can be generated using HCM 

methodologies at a link-specific and intersection level, HCM is not suitable for reporting 

measures at a systemwide level such as for the interchange as a whole. On the other hand, 

microsimulation has the ability to conduct an operational analysis at link-specific, intersection, 

multi-facility, and systemwide levels. In the Rockville Road Interchange case study, 

microsimulation enables the analyst to conduct the impact analysis and generate performance 

measures for various facility sizes such as for the entire interchange, specific facilities (freeways, 

ramps, or arterials), or individual links. Additionally, microsimulation tools are useful for 

analyzing the temporal fluctuations of traffic operations. The combination of microsimulation 

and traffic signal optimization tools offered the analysts the capabilities required in order to 

generate the mobility measures identified in Step 2. 

Step 4 – Identify the Alternatives

After determining the MOEs and the analysis tool, the next stage of an MOTAA process is the 

identification of work zone alternatives. As previously mentioned in Step 1 – Project Scope, 

lane closures on Rockville Road are flexible based on the mobility impacts of the work zone. As 

a result, the objective of the analysis is to evaluate various lane closure alternatives at Rockville 

Road and determine their impacts on mobility and construction duration. The three-lane closure 

scenarios at Rockville Road included the following: 

Analysis Tool Selection Options

This case study’s tool selection process focused on the tools’ capabilities to capture certain measures of mobility 

and the scope of the analysis. There are additional factors that an analyst can consider when selecting the 

appropriate analysis tool. Chapter 3 of this document describes these various factors in greater detail. The 

recommended factors for identifying the appropriate modeling approach include:

• Project goals and objectives;

• Work zone characteristics;

• Agency resources;

• Performance measures;

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control, operations, and 

mitigation strategies.).
269 

• Full Closure – The first alternative is the complete closure of Rockville Road in addition 

to the ramp closures; 

• Partial Closure – In the partial lane closure scenario, Rockville Road maintains limited 

capacity; and

• No Closure – In this scenario, Rockville Road remains fully operational during 

construction. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the modeling development and analysis, the analysts should define the scope 

of the analysis and identify the data needed for the calibration and modeling analysis effort. Part 

of project scoping is defining the study area that will be evaluated during the analysis. In this 

case study, the analysis covers the area consisting of the entire Rockville Road Interchange and 

the two intersections at the ramp termini on both sides of the interchange, as shown in Figure 71. 

In addition to determining the scope of the analysis, at this stage of the MOTAA the analyst also 

must identify the data inputs and assumptions needed to model the scenarios. Data collection 

efforts for microsimulation models typically require data on road geometry, controls, traffic 

demands, capacities, travel times, and queues. The baseline model of this case study will be 

based on 2006 existing conditions data. The following summarizes the data collection effort for 

the interchange reconstruction scenarios:

• Field Observed Traffic Data – Traffic counts for all entry and exit points into the study 

area were collected. Additionally, the analysts also collected intersection turning 

movement counts at the two intersections adjacent to the interchange.

• Road Geometry – Data for roadway geometry came from aerial images and CADD 

drawing files.

• Traffic Controls – Data for locations of speed limits and signal timings for the adjacent 

intersections were obtained through field observations and signal timing plans from 

stakeholder agencies.

• Analysis Time Period – This case study also considered a 24-hour period for analysis in 

order to evaluate before and after peak hour conditions. 

• Demand Data – O-D trip tables were obtained from travel demand models developed by 

the agency. Figure 72 illustrates the locations of zones and Figure 73 shows an example 

Extent of Analysis Considerations

Although the particular focus of this analysis is on the Rockville Road Interchange and two signalized 

intersections at the ramp termini, the extent of the analysis can be extended to one to two interchanges both north 

and south of the study area as well as the parallel arterials on either side of the interstate. Extending the area to 

be analyzed will enable the analyst to identify potential alternative routes and assess the mobility impacts of the 

Rockville Road ramp closures not only on Rockville Road, but also on adjacent interchanges and parallel 

arterials. An example of potential model size for analysis is shown in Figure 71. 
270 

AM peak-period O-D table. However, for this case study, the analysts considered a 24-

hour period. A 24-hour demand profile was constructed using a.m. and p.m. peak-period 

data. This demand profile is shown in Figure 74. As shown on the figure, total daily 

traffic totaled 410,000 vehicles with the a.m. peak hour (7:00 a.m. to 8:00 a.m.) at 28,000 

vehicles; and the p.m. peak hour (4:00 p.m. to 5:00 p.m.) at 33,000 vehicles. 

Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model with the appropriate 

geometry, traffic controls, demands, and capacities using the information from the data 

collection effort, specified in Step 5A.

Error Checking in Model Development

Although not specified in this case study, during model development, the analyst should perform error checks to 

identify and correct any model coding and signal timing errors. The analyst can check the model network 

geometries and traffic control operations against existing plans or engineering drawings obtained in Step 5A.

Demand Data and Analysis Period Considerations

For this case study, a 24-hour demand profile was constructed in order to evaluate travel conditions before, after, 

and during the peak periods. However, due to resource requirements and simulation processing and run times 

needed for microsimulation models, a 24-hour analysis period is atypical for this type of tool. Such models 

would typically be run for specific time periods such as a three- to four-hour window (i.e., peak period or offpeak). An example AM peak period O-D table for the study area network is shown in Figure 73.
271 

Figure 71. Rockville Road Case Study – Example Model Extents

(Source: Transportation Research Board, 2010.) 
272 

Figure 72. Rockville Road Interchange Case Study – Locations of Zones for O-D Table

(Source: Transportation Research Board, 2010.) 

Figure 73. Rockville Road Interchange Case Study – Example AM Peak 

Period Existing Conditions O-D Table
273 

Figure 74. Rockville Road Interchange – Existing Conditions

Model 24-Hour Demand Profile 

(Source: Transportation Research Board, 2010.) 

Step 5C – Existing Conditions Model Calibrations 

After the development of an existing conditions model, the following step in an MOTAA process 

is model calibration. Model calibration ensures that the operational performance of the model 

best reflects field observed conditions. The existing conditions model’s volumes and speeds 

were validated with the information obtained during the data collection effort outlined in 

Step 5A.
274 

Step 5D – Work Zone Base Conditions Model Development, and Step 5E – Work Zone 

Base Conditions Model Calibration 

The next step in the MOTAA process is the development of the work zone base conditions 

model. If an existing conditions model was developed it can typically be modified to develop 

the work zone base conditions model, as well as the alternatives analysis scenarios. In this case 

study, there was no work zone base conditions model developed. Since no work zone base 

conditions model was developed, there was no need for a work zone base conditions model 

calibration.

Step 6 – Alternatives Analysis

The alternatives analysis step involves two stages: 1) the development of models to capture the 

scenarios or alternatives; and 2) the methodology that describes how the models were run, the 

process for extracting outputs from the model, and the analysis of the results. 

Work Zone Base Conditions Model Calibration Options

As specified in Chapter 4 of this document, work zone model calibration includes the calibration of work zone 

capacity and performance measures. Work zone model capacity and performance measures, such as queue, 

travel time, delay, and speeds, can be calibrated to field data and or prior experiences (from similarly 

implemented work zone projects). Additionally, work zone base conditions calibration also entails that the 

analyst evaluate and identify the appropriate modeling parameters to use based on prior experiences modeling or 

analyzing work zones of similar types. Such observations and measures will aid the analyst in the work zone 

calibration process.

Additional Considerations for Model Calibration

The model calibration effort for the Rockville Road Interchange Reconstruction was not described in great detail 

in HCM 2010. However, for additional guidance on model calibration, refer to Chapter 4 of this document. 

Additionally, the following lists the factors typically involved in a model calibration process:

• Model Parameters – One of the critical steps in the model calibration process is the adjustment of global 

and link-specific parameters to ensure the model behaves similarly to field conditions.

• Model Runs and Random Seeds – After the adjustments to the model, the analyst will have to extract 

outputs from the model that can be used to compare against field data. To mimic real-world traffic 

variations, simulation software packages introduce randomized seed numbers associated with the model 

runs. The analyst will then extract and average the output values from a determined number of model runs 

with different seed values. The analyst will therefore have to determine the appropriate number of runs to 

conduct for the model. Chapter 4 of this document presents one way of determining model runs when using 

microsimulation tools. However, an agency may also specify a standardized number of runs or 

methodology to be used in during analysis.

• Calibration Acceptance Criteria – The criteria should include mathematical targets related to traffic 

volumes and speeds that could be used to compare the model with field observed conditions. In the 

calibration process, the analyst should also determine and specify the target values that the model results/

outputs should achieve. Example criteria could include achieving model volumes and speeds within a 

certain percent difference of the field data.
275 

Step 6A – Alternatives Model Development

This case study featured three alternative scenarios, as described in Step 4. The following three 

models were coded by modifying the 2006 existing conditions model. 

• No Closure Scenario – Since this scenario does not feature any lane closures on 

Rockville Road, the main difference from this and the baseline conditions scenario are 

the five on- and off-ramp closures. Additionally due to the ramp closures, traffic passing 

through the ramps is assumed to be 15 percent lower than the existing conditions 

scenario.

• Partial Closure Scenario – In addition to the ramp closures, Rockville Road is partially 

closed with only one lane open for through traffic in this scenario. Traffic passing 

through the ramps is reduced by 15 percent from the existing conditions scenario. 

• Full Closure Scenario – In addition to the ramp closures, Rockville Road is completely 

closed during construction. Traffic passing through the ramps is assumed to be reduced 

by 30 percent from the existing conditions scenario. 

Step 6B – Alternatives Analysis Model Runs and Results

The final steps of the MOTAA are to run the work zone alternative models using the selected 

traffic analysis tool and to extract the model outputs needed to generate the MOEs established in 

Step 3. The following summarizes the resulting measures of effectiveness generated for each 

model scenario:

• Twenty-Four-Hour Profile of Average Network Speeds and Number of Vehicles –

As shown in Figure 75 overall average speeds across the network for all scenarios 

significantly decreased, while vehicle counts increased, as shown in Figure 76. The noand partial-closure scenarios had similar average speeds and vehicle counts profile, while 

the full-closure scenario resulted in the greatest mobility impacts, especially during the 

p.m. peak period.

• Average and Cumulative Network Delays – Average and cumulative network delays 

across the four scenarios is shown in Figure 77 and Figure 78. Again, the full-closure 

scenario generated the greatest amount of mobility impacts with 5,000 vehicle hours of 

delay during the p.m. peak hour and 16,000 vehicles hours of delay for the entire day.

• Construction Duration – Another measure that can be used to compare the various 

alternatives is the length of construction duration. Figure 79 shows the cumulative 

network delays considering construction duration. As shown in the figure, the no closure 

is expected to have the longest construction duration at 16 months. The partial and full 

closures scenarios are assumed to take 8 and 4 months, respectively. The figure shows 

the tradeoffs between optimizing construction schedule and mobility impacts. For 

instance, while the full closure scenario offers the shortest construction duration, it results 

in the greatest amount of mobility impacts.
276 

Figure 75. Rockville Road Interchange – Average Network Speed

(Source: Transportation Research Board, 2010.) 

Figure 76. Rockville Road Interchange – Average Number of Vehicles

(Source: Transportation Research Board, 2010.) 
277 

Figure 77. Rockville Road Interchange – Average Network Delays 

(Source: Transportation Research Board, 2010.) 

Figure 78. Rockville Road Interchange – Cumulative Network Delays 

(Source: Transportation Research Board, 2010.) 
278 

Figure 79. Rockville Road Interchange Reconstruction – Cumulative 

Network Delays during the Construction Period

(Source: Transportation Research Board, 2010.) 

Step 7 – Decision Framework and Recommendation of an Alternative 

In the HCM 2010 Manual, the case study does not indicate which of the three alternatives should 

be recommended for implementation, nor how the alternatives analysis results can be used to 

select a preferred construction scenario.
279 

Figure 80. I-465 West Leg, Rockville Road Interchange Reconstruction 

Decision Framework Example

Average Value Points Average Value Points Average Value Points

15% 10 < x ≤ 20 mph = 0 pts 22 mph 5 27 mph 10 30 mph 15

20 < x ≤ 25 mph = 5 pts

25< x ≤30 mph = 10 pts

30 mph+ = 15 pts

4000+ vehicles = 0 pts 3, 875 vehicles 2 3,050 vehicles 5 2,825 vehicles 10

15% 3500 < x ≤ 4000 vehicles = 2 pts

3000 < x ≤ 3500 vehicles = 5 pts

2500 < x ≤ 3000 vehicles = 10 pts

2500 vehicles or less = 15 pts

2000+ vehicle hours = 0 pts 3,175 vehicle hours 0

1,250 vehicle 

hours 5

1, 150 vehicle 

hours 5

10% 1500 < x ≤ 2000 vehicle hours = 2 pts

1000 < x ≤ 1500 vehicle hours = 5 pts

1000 vehicle hours or less = 10 pts

20000+ vehicle hours = 0 pts

17,000 vehicle 

hours 2

12,125 vehicle 

hours 5

11,875 vehicle 

hours 5

10% 15000 < x ≤ 20000 vehicle hours = 2 pts

10000< x ≤ 15000 vehicle hours = 5 pts

10000 vehicle hours or under = 10 pts

$0K - $25K = 20 pts $70K 5 $28K 10 $25K 20

20% $25K - $50K = 10 pts

Road User Cost - vehicle 

delay cost calculations 

only

$50K+ = 5 pts

Construction duration 12 months+ = 2 pts 4 months 10 8 months 5 16 months 2

10%

Construction duration 6 months -12 

months = 5 pts

Construction duration is less than 6 

months =10 pts

Safety 20%

WZ crash rate is 20% lower than preconstruction condition = 20 pts

WZ crash rate is equal to pre-construction 

condition = 15 pts

WZ crash rate is up to 20% higher than preconstruction = 10 pts 24% higher 5 10% higher 10 2% higher 10

WZ crash rate is 20- 30% higher than preconstruction = 5 pts

WZ crash rate is 30%+ higher than preconstruction = 0 pts

Total 100% 29 50 67

No Closure 

Criteria

Average Network Speed 

Over PM Peak Period 

Construction Duration

Cumulative Network 

Delays over the PM Peak 

Period

Average Network Delays 

over the PM Peak Period

Average Number of 

Vehicles Present in the 

Network over PM Peak 

Period

Criteria Component Full Closure Partial Closure

Percentage of Total

Decision-Framework Options

Although this case study does not feature a decision-framework, the following example shown in Figure 80 

illustrates an example showing how the alternatives analysis results can be used to develop decision-making 

criteria for identifying a preferred alternative. There are several decision-making frameworks that can be used to 

evaluate and choose among different work zone alternatives. Several of these frameworks are described in 

further detail in Chapter 5.

In the following example shown in Figure 80, a hypothetical 100-point scale was developed to score the various 

alternatives against a set of criteria. The table shows the alternatives’ mobility measures extracted from the 

alternatives analysis results. It also includes assumed values for measures such as road user costs, construction 

duration, and safety for each alternative.

In some instances, the measure shown in Figure 80 refers to evening peak traffic conditions. In order to report 

these measures using this specific timeframe, the values associated with 4:00 p.m. to 7:00 p.m. from the 24-hour 

alternatives analysis results were averaged. Each alternative’s performance measures were then compared 

against a set of criteria, which determines how many points should be allotted to each alternative based on the 

values of each MOE. According to the results of this hypothetical scoring technique, the No Closure alternative 

(highlighted in green) scored the highest of the three alternatives.
280 

9.10 CORRIDOR RECONSTRUCTION WITH COMPLEX NETWORK-WIDE 

IMPACTS USING TRAVEL DEMAND MODELING TOOLS – TRANPLAN

Cleveland Innerbelt Project Background

This case study describes the process to evaluate potential maintenance of traffic detour routes 

and a potential full closure option during the planning phase of the Cleveland Innerbelt Project in 

2004 for the Ohio DOT.(65)

The Cleveland Innerbelt is a high capacity, limited-access interstate highway extending from 

Cleveland’s Tremont neighborhood on the west side of the Cuyahoga River, across the 

Cuyahoga Valley, around the southern and eastern edges of downtown to the City’s lakefront 

district at Burke Lakefront Airport. The Innerbelt includes portions of I-71 and I-90, and 

connects to I-77, I-490, SR 2, and SR 176, as shown in Figure 81. 

A key component of the Innerbelt Freeway is the Central Viaduct Bridge, shown in Figure 82. 

The Central Viaduct Bridge is a primary river crossing, moving Interstate traffic from the south 

(I-71) and west (I-90) across the Cuyahoga River to the downtown distribution system of the 

Central Interchange and further east to the Innerbelt Trench. The near mile-long Central Viaduct 

Bridge is the critical link along this section of I-90 carrying over 130,000 vehicles per day. 
281 

Figure 81. Cleveland Innerbelt Project

(Source: Bugess & Nipple, Inc., 2004.)
282 

Figure 82. Cleveland Innerbelt Corridor Sections

(Source: Bugess & Nipple, Inc., 2004.)

Considerations for Maintenance of Traffic Alternatives Analysis

The daily recurring congestion presents a tremendous challenge for maintenance of traffic during 

construction. The entire length of the Innerbelt Freeway lacks adequate shoulders on which to 

perform or stage construction work. Further, Ohio DOT maintenance of traffic policies 

discourage long-term lane closures on this portion of I-90 due to high traffic demand and 

existing poor levels of service. As such, there is need to plan for a systematic phasing of the 

improvements, such that traffic can be maintained to the greatest extent practical.
283 

In accordance with Ohio DOT Policy Number 516-003(P) Traffic Management in Work Zones: 

Interstate and Other Freeways, Ohio DOT District 12 has developed a Permitted Lane Closure 

Map (PLCM) (shown in Figure 83) that specifies how many lanes must remain open during what 

time periods in order to minimize construction-related congestion and delay.(66)

The sequencing of construction needed to be programmed to minimize traffic delays throughout 

the corridor. Within construction areas, it was planned that traffic control measures using 

standard practices would be used. 

Applying MOTAA in the Planning Process

During the Planning Phase of this project, a Cleveland Innerbelt MOT Strategic Plan was 

developed to assist in quantifying the potential impacts of MOT activities on the study area and 

to help identify additional projects that may need to be completed as part of the overall 

implementation strategy to help mitigate these impacts. To support this effort, the primary and 

secondary maintenance of traffic routes that may be utilized by motorists during construction, 

whether officially signed or not, needed to be identified. 

The project also hosted an Accelerated Construction Technology Transfer (ACTT) review during 

the planning process. One of the issues raised at this ACTT review was the potential to 

accelerate the replacement of the Central Viaduct by initiating a full closure of the bridge and, 

thus, accommodating the full replacement of the structure on the existing alignment. The impact 

of this full closure also needed to be determined before additional project resources would be 

utilized to pursue it. As such, a “red flag”-level analysis was undertaken for this alternative.
284 

Figure 83. Ohio DOT Permitted Lane Closure Map (PLCM)
285 

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The first step in an MOTAA is to define the scope, goals, and objectives of the analysis. Based 

on the requirements of this analysis, it was necessary to identify primary and secondary 

maintenance of traffic corridors and to determine the feasibility of implementing a full closure of 

the Central Viaduct Bridge. A vital goal for controlling traffic through and around the projected 

construction work zones was to minimize the disruption of normal traffic flow and maintain 

reasonable access to all properties near the construction areas.

Step 2 – Establishing the MOEs and Thresholds 

After defining the goals and objectives of the project, the next step in an MOTAA is to establish 

the measures of effectiveness and/or thresholds that would be used to evaluate and compare the 

different work zone alternatives. For this case study, the mobility measures of effectiveness 

(MOE) selected included percent increase or decrease in traffic and volume/capacity (v/c) ratio.

Step 3 – Choosing the Analysis Tool 

The next stage of the MOTAA process is to determine the type of analysis tool to be used and 

the justification for that selection. In this case study, TRANPLAN, the regional travel demand 

model, was chosen as the operational model platform to simulate traffic assignments due to its 

ease of use, ready access, and ability to generate the measures of effectiveness determined in 

Step 2. Factors that impacted the decision to utilize a travel demand model as the analysis tool 

are as follows:

• As part of the overall study, improvements had been made to the existing regional travel 

demand model to enhance its utility as an analysis tool for the study area. This included 

splitting zones within the CBD area for better fidelity in assigning trips from the freeway 

to the arterial network due to the large number of access points in the CBD area (27 

access points in the CBD alone). To further enhance this fidelity, a parking allocation 

model was developed, which assigned vehicle origins/destinations to the zone in which a 

car mode traveler would park in the CBD as opposed to the zone which would be their 

final destination.

• Due to controversy regarding the growth projection inputs to the travel demand model 

early in the study, a Neighborhood Planning Committee (NPC) had been formed from 

key stakeholders in each of the neighborhoods impacted by the project and key staff from 

the City of Cleveland. This NPC reviewed, in detail, the growth projections for the study 

area zone-by-zone and compared them to existing community plans. Once this process 

was successfully completed, public confidence in the predictive capabilities of the travel 

demand model increased. 

• The ability of the model to react to the change in capacity due to the work zone with a 

potential change in both mode and route choice, as there is a substantial transit 

component in the study area. 
286 

Step 4 – Identify the Alternatives

After determining the MOEs and the analysis tool, the next stage of an MOTAA process is the 

identification of work zone alternatives. As a baseline for comparison, the existing conditions 

model would be utilized. In addition, two additional models will be utilized to determine 

potential impacts.

• Alternative 1: Full Closure of Central Viaduct – This alternative removed the Central 

Viaduct links from the highway link structure. This alternative will demonstrate the 

impact of a full closure of the Central Viaduct Bridge.

• Alternative 2: Partial Closure of Central Viaduct – This alternative reduced the 

capacity of the eastbound (inbound) I-90 travel lanes on the Central Viaduct Bridge from 

four lanes to two lanes in the a.m. peak period. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the modeling development and analysis, the analysts should define the 

objectives of the analysis and identify the data needed for the calibration and modeling analysis 

effort. This step was unnecessary as part of this analysis. Earlier efforts within the project to 

make improvements to the fidelity of the route assignment component of the model through 

splitting zones and adding a parking allocation model already had been completed. Further, the 

validation of the growth projections in conjunction with the Neighborhood Planning Committee 

also removed the need for additional data checks.

Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model with the appropriate 

geometry, traffic controls, demands, and capacities using the information from the data 

collection effort and the preliminary analysis. As discussed above, refinements to the existing 

model already had been undertaken as part of a parallel process during the project.

Analysis Tool Selection Options

There are additional factors that an analyst can consider when selecting the appropriate analysis tool. Section 3 

of this document describes these various factors in greater detail. The recommended factors for identifying the 

appropriate modeling approach include:

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control, operations, and 

mitigation strategies.).
287 

Step 5C – Existing Conditions Model Calibrations 

The model calibration was conducted through the Neighborhood Planning Committee growth 

projection validation process. The validation was mainly focused on v/c ratio and traffic 

volumes on freeways and major arterials. For traffic volume validation, the acceptable threshold 

between modeled volumes and field counts on major freeways and arterials were within 10 

percent.

Step 5D – Work Zone Base Conditions Model Development. 

Once the existing conditions model has been developed and calibrated, it can then be modified 

for use during the work zone base conditions model development and calibration, as well as 

during the alternatives analysis stage. In this case study, there was no work zone base conditions 

model developed. The scenarios identified in Step 4 were considered as alternatives. Therefore, 

all runs would be compared to existing conditions, as there were no work zone base conditions. 

Step 5E – Work Zone Base Conditions Model Calibration 

No work zone calibration procedure was specified for this case study.

Step 6 – Alternatives Analysis

The alternatives analysis step involves two stages: 1) development of models to capture the 

scenarios or alternatives; and 2) description of how these models were run, the outputs extracted, 

and analysis of the results. 

Step 6A – Alternatives Model Development

The analysts started with the existing conditions model and adjusted the roadway network to 

replicate each of the scenarios identified in Step 4. The adjustments included geometric 

configurations (e.g., number of lanes), capacities, and link length change to match work zone 

area. The model was run for the a.m. peak period only. 

Step 6B – Alternatives Analysis Model Runs and Results

The final step of the MOTAA is to run the work zone alternative models and evaluate the 

mobility performance measures extracted from the model results. As previously mentioned, the 

analysts used percent increase or decrease in traffic and v/c ratio as MOEs. These results were 

then thematically displayed on a series of graphics showing the impact of each of the 

Work Zone Base Conditions Model Calibration Options

As specified in Chapter 4 of this document, work zone model calibration includes the calibration of work zone 

capacity and performance measures. Work zone model capacity and performance measures such as queue, 

travel time, delay, and speeds can be calibrated to field data and or prior experiences (from similarly 

implemented work zone projects). Additionally, work zone base conditions calibration also entails that the 

analyst evaluate and identify the appropriate modeling parameters to use based on prior experiences modeling or 

analyzing work zones of similar types. Such observations and measures will aid the analyst in the work zone 

calibration process.
288 

alternatives. Figures 84 and 85show the v/c ratio thematic plots for Alternative 1 (full closure)

and Alternative 2 (partial closure), respectively.

Figure 84. Cleveland Innerbelt Study: V/C Ratio Thematic Map

(Alternative 1 – Full Closure)

(Source: Bugess & Nipple, Inc., 2004.)
289 

Figure 85. Cleveland Innerbelt Study: V/C Ratio Thematic Map

(Alternative 2 – Partial Closure)

(Source: Bugess & Nipple, Inc., 2004.)
290 

Step 7 – Decision Framework and Recommendation of an Alternative 

After obtaining the model output and mobility performance measures through the modeling 

analysis, the next stage of the MOTAA process is the application of a decision-making 

framework or criteria for evaluating and identifying the preferred alternative.

A red flag analysis of the concept of a full closure of the Central Viaduct Bridge (Alternative 1) 

was conducted. The thematic map (Figure 85 showed that by completely removing this critical 

link from the highway network, the potential detour routes were overwhelmed by diverting 

traffic. This would have had a critical impact on the ability to provide access to and from the 

CBD of Cleveland from both the south and west. As such, this alternative construction 

technique was removed from further consideration. 

Alternative 2 was recommended based on a combination of engineering judgment and public 

involvement with key stakeholders to aid in the development of the plan. The thematic mapping 

results from the travel demand model were utilized to show where potential traffic diversion 

routes could be expected. This information was shared with key stakeholders in a charrette 

setting along with additional information regarding location of key community resources, 

adjacent land use, noise susceptible properties, minority populations, low-income populations, 

zoning, historic resources, etc. Working with the community in a facilitated environment, routes 

were identified as either primary or secondary maintenance of traffic routes (see Figure 86). As 

the project moves forward, as much traffic as can safely be accommodated should remain on the 

freeway. When traffic must be diverted to the arterial street system it should be focused onto the 

primary maintenance of traffic routes and discouraged from using the secondary routes. To 

accomplish this, the MOT Strategic Plan identified additional projects or concepts that would be 

added to the overall scope of the project to support the needs of accommodating this approach. 

Examples of these types of recommendations include: 

• Quigley Road extension must be completed prior to the freeway construction phase, and 

will connect the I-71 ramps at W. 14th Street to Quigley Road. This project may include 

a roundabout at Quigley/W. 14th/Holmden and reconstruction of the interchange with 

I-490. 

• This improvement was implemented as the first construction project to come out of 

the implementation plan.

• Install a portion of the planned ITS system improvements necessary for MOT on this 

project.

• The ITS component of this study was spun off as an individual project and proceeded 

on a separate track.

• Resurface all priority alternate streets and replace or upgrade new pavement markings to 

provide a minimum of two through lanes in each direction and create separate left turn 

lanes where needed.

• Identification of park-and-ride lot expansions that could occur outside the study area to 

reduce the impact to identified detour routes. 

• The expansion of several Park-and-Ride facilities was undertaken as “go early” 

projects during the development of the overall project implementation strategy.
291 

Figure 86. Cleveland Innerbelt Study: Maintenance of Traffic Corridors Map

(Source: Bugess & Nipple, Inc., 2004.)

Decision Framework Options

The analysts in this case study used the mobility MOEs that were output by the modeling analysis to support an 

interactive process with the community for identifying primary and secondary maintenance of traffic corridors. 

In this instance, mobility measures were only a piece of the overall decision-making framework. As such, a 

formalized decision-making framework was determined to be detrimental to the process of building consensus 

with the stakeholders.
292 

9.11 CORRIDOR RECONSTRUCTION WITH COMPLEX NETWORK-WIDE 

IMPACTS USING MESOSCOPIC SIMULATION – INTEGRATION

I-80 Construction Staging Project Background 

This case study describes the process used by Kremer, et al. (2000) to evaluate several 

construction staging proposals for the project improvements completed along the I-80 eastbound 

corridor in Saddle Brook, Bergen County, New Jersey in 1999.(67) The improvements occurred 

between Interchange 62 and 64 along I-80 and included roadway widening, noise wall erection, 

and bridge deck replacement. The improvements were necessary as I-80, a key commuter route 

for the region, was in great need of additional capacity. The work zone alternatives analysis was 

conducted in order to determine how to perform the improvements necessary with the least 

possible impacts to road users. 

Figure 87 depicts the existing conditions or preconstruction configuration of I-80 eastbound. At 

one end of the study area, I-80 eastbound consisted of five lanes approaching a CollectorDistributor (C-D) Road that provided access to the Garden State Parkway and Saddle River

Road. The section then became a four-lane section that eventually turned into a split 

configuration consisting of two express lanes and two local lanes. The express lanes provided 

direct access to the New Jersey Turnpike and George Washington Bridge. The local lanes 

provided access to all interchanges in between. 

I-80 Construction Staging – Application of the MOTAA Process

The following subsections describe the work zone alternatives analysis procedure applied and 

documented in a Transportation Research Board paper by Kremer, et al. (2000) in evaluating the 

construction staging alternatives for the I-80 project improvements completed in 1999.(67) The 

purpose of the case study analysis was to present an alternative approach to the typical 

construction staging analysis. The authors described the typical construction staging analysis as 

a procedure that involved identifying critical demand and designing staging plans to ensure that 

sufficient capacity exists in the construction zone to meet that measured demand. The authors 

noted that this approach treats demand as isolated points instead of viewing flows as a system. 

Examining traffic flow at a systemwide level would ensure that the analysis accounted for both 

upstream and downstream conditions. The process they recommended offers a four-step 

approach that includes the following: 

• Targeted scope of investigation – The first step of the process involves determining the 

goals, objectives, and constraints of the project. Additionally, this also entails structuring 

the scope of the analysis to fit the goals of the project. 

• Timely data collection to identify true demand – The next step of the authors’ process 

is to establish a data collection program that makes the distinction between measured and 

true demand. 
293 

• Preliminary traffic engineering analysis – This analysis is conducted prior to the 

simulation/modeling efforts in order to review the alternatives for any fatal flaws, as well 

as to apply Highway Capacity techniques in order to estimate roadway capacity. 

• Performance measures directly relevant to motorist experience – The final step of the 

process is to ensure that the chosen performance measures extracted from the modeling 

analysis is consistent with the established goals and objectives. 

The authors’ four-step process parallels many of the concepts incorporated into the 

recommended MOTAA methodology described in Chapters 2 and 4 of this document. However, 

for the purpose of this guide, the authors’ proposed process will be tailored to fit the step-by-step 

methodology described in Chapter 4. 

Step 1 – Problem Definition, Scope, Goals, and Objectives 

The first step in an MOTAA is to define the goals and objectives of the analysis. This also 

corresponds with the authors’ first stage of their four-step process. In this case study, I-80 is a 

principal east-west travel route in New Jersey, providing vital linkages for travelers between 

local destinations within the State, as well as interstate between New York, New England, New 

Jersey, and Pennsylvania. The construction work zone was, therefore, located in one of the most 

heavily traveled sections of I-80 in the State. I-80 already is severely congested during the peak 

period and any other disruption and reduction in capacity would severely impede mobility. The 

goal of the project was, therefore, to determine the construction staging option that could 

minimize the disruption experienced by motorists through accommodating current traffic 

demand without a significant spatial or temporal shift.

Step 2 – Establishing the MOEs and Thresholds 

After defining the goals and objectives of the project, the next step in an MOTAA is to establish 

the measures of effectiveness and/or thresholds that would be used to evaluate and compare the 

different work zone alternatives. For this case study the MOEs chosen included average speed, 

travel time, queue length, and vehicle throughput. 

Step 3 – Choosing the Analysis Tool 

The next stage of the MOTAA process is to determine the type of analysis tool to be used and 

the justification for that selection. In this case study, INTEGRATION was chosen as the 

operational model platform to simulate traffic operations due to its ease of use and ability to 

generate the measures of effectiveness determined in Step 2. A mesoscopic model was chosen 

because of its ability to allow users to test various scenarios and generate a more accurate range 

of performance measures for comparison among alternative design and operational scenarios as 

compared to travel demand models. It also was chosen because of its ability to generate various 

measures of effectiveness, including travel times by vehicle or link type, queue length, duration, 

and visual observations of queue formation and dissipation. 
294 

Analysis Tool Selection Options

There are additional factors that an analyst can consider when selecting the appropriate analysis tool. Chapter 3 

of this document describes these various factors in greater detail. The recommended factors for identifying the 

appropriate modeling approach include: 

• Project goals and objectives; 

• Work zone characteristics; 

• Agency resources; 

• Performance measures; 

• Data; and

• TMP (Tool should be able to capture the impacts of various traffic control, operations, and 

mitigation strategies.). 

For this case study, the analyst chose a tool that was consistent with most of these factors. A mesoscopic 

simulation software package such as INTEGRATION was chosen based on the data needs, analysis goals and 

objectives, the transportation management plan, and performance measures required for the project.
295 

Figure 87. I-80 Eastbound Existing Conditions 

(Source: Kremer, Kotchi, DeJohn, and Winslow, 2001.)
296 

Step 4 – Identify the Alternatives

After determining the MOEs and the analysis tool, the next stage of an MOTAA process is the 

identification of work zone alternatives. Aside from the existing conditions scenarios described 

in the Project Background portion of this section, the two alternatives/construction staging 

scenarios compared and evaluated in this case study includes:

• Contract Staging Plan – The first alternative was determined by the New Jersey DOT. 

Their analysis showed that the existing four-lane configuration (two lanes of expressway 

and two lanes local) must be maintained during construction based on existing travel 

volumes. The expressway would remain the same while the local roadway would remain 

open but with reduced lane widths (from 12 feet to 11 feet) without shoulders and with 

three emergency/breakdown areas. Figure 88 shows the Contract Staging Plan 

configuration. 

• Alternative/Contractor’s Staging Plan – After the construction project was awarded, 

the Contractor developed a staging plan that would have shortened the overall 

construction period by several months. This staging plan proposed the closure of the 

local lanes, forcing all traffic to merge onto the express roadway. In this alternative, the 

C-D road also would be extended to provide a third express lane. The express roadway 

would, therefore, be configured into three 11-foot lanes without shoulders. There would 

be no breakdown areas provided. Figure 89 depicts the Contractors Staging Alternative 

configuration. 

Step 5 – Modeling Development and Application Process 

Step 5A – Project Scope and Data Collection 

During this stage of the modeling development and analysis, the analysts should define the 

objectives of the analysis and identify the data needed for the calibration and modeling analysis 

effort. Data collection efforts for microsimulation models typically require data on road 

geometry, controls, traffic demands, capacities, travel times, and queues. This step would 

correspond with the second stage of the four-step process identified by the authors. 

The scope of this case study was to design and evaluate the construction staging area along I-80 

eastbound between Interchanges 62 and 64. As previously mentioned, the objective of the 

analysis effort is to conduct the construction effort with minimal disruption to mobility. The 

analysis effort evaluated the study area during the a.m. peak period between 6:30 a.m. and 9:00 

a.m.
297 

Figure 88. I-80 Eastbound Contract Staging Plan 

(Source: Kremer, Kotchi, DeJohn, and Winslow, 2001.)
298 

Figure 89. I-80 Eastbound Contractor Staging Alternative (Three-Lane Staging Plan) 

(Source: Kremer, Kotchi, DeJohn, and Winslow, 2001.)
299 

The data collection effort included: 

• Field Observed Traffic Data – Two days of traffic data collected included traffic 

counts, speed and delay runs, and observations of queue formation and length. The count 

locations included critical entry and exit points to and from the study area. 

• Demand Data – Origin-Destination data was not available, so the analysts used traffic 

counts to generate a trip table.

• Road Geometry – Using data collected from field visits, the analysts collected 

information regarding roadway characteristics and configuration. 

A preliminary traffic engineering evaluation also was conducted to determine the roadway 

capacity and throughput in the study area. The preliminary traffic engineering evaluation 

included: 

• Fatal Flaw Analysis – A fatal flaw analysis of the staging plans and traffic flow was 

conducted to ensure that existing traffic movements could be accommodated and that 

worker and motorist safety were not compromised with either of the staging plan 

alternatives. No fatal flaws were found. However, it was recognized that without an 

emergency/breakdown area it would be very difficult for traffic to recover from an 

accident or disabled vehicle.

• Capacity Analysis – A capacity analysis using 2000 HCM procedures was used to 

determine whether a three-lane expressway could accommodate the prevailing demand. 

Based on this evaluation, it was determined that the three-lane cross section was 

insufficient to meet the measured demand. However, the authors chose to keep the 

alternative and proceed with the modeling analysis efforts. 

Step 5B – Existing Conditions Model Development 

The next step of the MOTAA is to develop the existing conditions model with the appropriate 

geometry, traffic controls, demands, and capacities using the information from the data 

collection effort and the preliminary analysis. The process used in the case study for network 

coding and error checks included: 

• Road Geometry – A GIS-based network editor was used to build the network. Error 

checks were conducted to ensure accurate coding of the network. The analysts used field 

visits to verify their network coding. They also used data from the New Jersey DOT 

Straight Line Diagrams as a quality control tool to verify the accuracy of the coded 

roadway characteristics and configuration. The edited network was then converted into 

an ASCII format and then to a format compatible with INTEGRATION. 

• Traffic Demands – As previously mentioned in Step 5A, trip tables were generated 

using observed traffic volumes. The network consisted of seven entry/exits in the 

corridor. The analysts’ first step in generating the trip table was to generate traffic 

volumes at all links and nodes, balance these counts, and distribute them throughout the 

corridor. Initial distribution assumptions were made based on observed traffic patterns 

and flows. The final trip table was divided into five 30-minute intervals and two vehicle 

trip types, auto and truck trips. 
300 

Step 5C – Existing Conditions Model Calibrations 

The model calibration criteria consisted of three sources of existing conditions data: traffic 

counts, average travel speeds, and queuing observations. The calibration was determined to be 

within acceptable limits. Results of the calibration effort were not shared in the document.

Step 5D – Work Zone Base Conditions Model Development, and Step 5E – Work Zone 

Base Conditions Model Calibration

Once the existing conditions model has been developed and calibrated, it can then be modified 

for use during the work zone base conditions model development and calibration, as well as 

during the alternatives analysis stage. In this case study, there was no work zone base conditions 

model developed. The analysts assumed the same demand levels for all three scenarios (existing 

conditions, contract staging plan, and alternative staging plan). No work zone calibration 

procedure was specified for this case study.

Additional Considerations for Model Calibration

For this case study, the criteria measures used to compare model behavior with field observations were 

established. However, there are several other considerations involved in the model calibration process, 

including:

• Model Parameters – One of the critical steps in the model calibration process is the adjustment of global 

and link-specific parameters to ensure the model behaves similarly to field conditions.

• Model Runs and Random Seeds – After the adjustments to the model, the analyst will have to extract 

outputs from the model that can be used to compare against field data. To mimic real-world traffic 

variations, simulation software packages introduce randomized seed numbers associated with the model 

runs. The analyst will then extract and average the output values from a determined number of model runs 

with different seed values. The analyst will, therefore, have to determine the appropriate number of runs to 

conduct for the model. Chapter 4 of this document presents one way of determining model runs when using 

microsimulation tools. However, an agency may also specify a standardized number of runs or 

methodology to be used in during analysis.

• Calibration Acceptance Criteria – The criteria should include mathematical targets related to traffic 

volumes and speeds that could be used to compare the model with field observed conditions. In this case 

study, the authors have specified the measures used to compare the model with field data. In the calibration 

process, the analyst should also determine and specify the target values that the model results/outputs should 

achieve. Example criteria could include achieving model volumes and speeds within a certain percent 

difference of the field data. The criteria and acceptance targets may vary based on agency standards and/or 

preference.
301 

Step 6 – Alternatives Analysis

The alternatives analysis step involves two stages: 1) development of models to capture the 

scenarios or alternatives; and 2) description of how these models were run, the outputs extracted, 

and analysis of the results. 

Work Zone Base Conditions Model Calibration Options

As specified in Chapter 4 of this document, work zone model calibration includes the calibration of work zone 

capacity and performance measures. Work zone model capacity and performance measures such as queue, 

travel time, delay, and speeds can be calibrated to field data and or prior experiences (from similarly 

implemented work zone projects). Additionally, work zone base conditions calibration also entails that the 

analyst evaluate and identify the appropriate modeling parameters to use based on prior experiences modeling or 

analyzing work zones of similar types. Such observations and measures will aid the analyst in the work zone 

calibration process.

For many work zone projects, the absence of reliable work zone conditions data to calibrate has caused many 

agencies to abandon a work zone base calibration effort. Instead, many of the same assumptions, parameters, 

and demands are consistent across the existing conditions and work zone conditions models. However, in 

reality, demands and driver behavior can be very different in a work zone versus a typical driving commute. 

While this case study does not provide a work zone base calibration effort, the following suggested procedure 

provides some recommendations and guidance on the calibration of a work zone base model.

1. Identifying Calibrations Measures and Thresholds

In the existing model calibration, field measures and/or observations such as volumes, speeds, queues, and 

bottlenecks were used to calibrate/validate model outputs. Since typically the work zone base condition model 

development occurs prior to project implementation, there is typically no field data to calibrate to. Analysts can, 

therefore, use similar measures for calibration/validation, but must rely on sources outside of field data. One 

example of a measure that can be used for work zone calibration is capacity during work zone conditions. 

Chapter 4 of this document describes how work zone capacity can be determined. Analysts can determine work 

zone capacity using any of the following methods/sources:

• Implemented work zone projects with similar characteristics.

• HCM 2010 methods for calculating work zone capacity (discussed in Chapter 4). This also includes the use 

of tools, such as Q-DAT (presented in Chapter 7) that use HCM methodologies to determine capacity.

• Other work zone capacity studies and best practices.

2. Parameter Adjustments

The next step entails the adjustment of model parameters until the model is able to replicate or reflect the 

conditions identified by the previous step. For instance, if the measure being used for the calibration procedure 

is capacity, the analyst can conduct a sensitivity analysis that will identify which combinations and values of 

model parameters are able to produce the expected capacity for the work zone. Some example model parameters 

that could be adjusted for work zone base calibration in INTEGRATION and other mesoscopic, as well as 

microscopic tools include:

• Rubbernecking factors;

• Lane changing parameters;

• Driver behavior; and

• Traveler awareness and compliance parameters.

Through a work zone base calibration procedure, the model can better reflect the expected capacity, demands, 

queues, and diversions that may occur as result of the work zone.
302 

Step 6A – Alternatives Model Development

The analysts used the existing conditions model and adjusted it to develop the two alternatives 

analysis scenarios according to design plans. The analysts assumed that the same trip table, 

developed in Step 5A would be used for all three models: existing conditions, contract staging 

plan, and the contractor’s alternative staging plan. The analysis time period remained at 6:30-

9:00 a.m., the morning peak period. 

Step 6B: Alternatives Analysis Model Runs and Results

The final step of the MOTAA is to run the work zone alternative models and evaluate the 

performance measures extracted from the model results. As previously mentioned, the analysts 

used MOEs such as average speed, travel time, maximum queue length, and total vehicle 

throughput to evaluate and compare the work zone alternatives. The results of the analysis are 

shown in Figure 90. 

Figure 90. I-80 Eastbound Alternatives Analysis Results 

Step 7 – Decision Framework and Recommendation of an Alternative 

After obtaining the model output and mobility performance measures through the modeling 

analysis, the next stage of the MOTAA process is the application of a decision-making 

framework or criteria for evaluating and identifying the preferred alternative. In this case study, 

the authors chose the alternative that would generate the least amount of negative impacts on 

mobility along the corridor. According to the results shown in Figure 90, the Alternative Staging 

Plan would reduce the average travel speed by half, add 12 minutes to the travel time, and reduce 

throughput by about 3,000 vehicles as compared to the Contract Staging Plan. Therefore, the 

preferred alternative was the Contract Staging Plan.

Determining the Number of Runs

One of the key steps in the alternative analysis is determining the appropriate number of models runs as 

specified earlier in Step 5C, existing conditions model calibration. Section 4.7 of this document also discussed 

how to determine the number of runs.
303 

Decision Framework Option/Example

The analysts in this case study chose the preferred alternative based on the mobility measures extracted from the 

modeling analysis. There are also additional factors outside of mobility measures that an agency may want to 

consider when evaluating the alternatives. These factors are described in further detail in Chapter 7 of this 

document. A decision-making framework that incorporates these factors, as well as the mobility measures from 

the simulation results can then be applied to compare the alternatives and choose a preferred option. Chapter 5 

of this document feature several different decision-framework options that can fit projects of different 

complexities and resources.

The following illustrates the application of a decision-making framework, the Kepner-Tregoe (KT) Method, on 

the I-80 case study using the results from the alternatives analysis, as well as hypothetical values and 

assumptions for additional factors/measures. As discussed in Chapter 5 of this document, the KT Method 

involves the following steps:

1. Prepare the decision statement;

2. Define MUST and WANT objectives;

3. Assign weights to WANT objectives;

4. Identify candidate MOT alternatives;

5. Summarize the findings of the work zone impact assessment;

6. Evaluate the alternatives against the MUST objectives;

7. Evaluate the alternatives against WANT objectives;

8. Calculate the weighted scores of the alternatives;

9. Evaluate adverse consequences; and

10. Select the preferred MOT strategy.

Step1 – Prepare Decision Statement

The purpose of the decision analysis is to identify the most appropriate construction staging alternative for 

minimizing the project’s impacts on motorists and the community.

Step 2 – Define MUST and WANT objectives

As described in Chapter 5, MUST objectives include all mandatory requirements that the alternatives must meet. 

WANT objectives includes the desired measures that will be used to weigh/rank alternatives. The following 

example includes lists of objectives that serve as hypothetical examples for the purpose of demonstrating the KT 

Method. These objectives are not representative of established thresholds, requirements, or objectives set by the 

New Jersey DOT during the implementation of this I-80 project.

Decision Framework Option/Example

The list of MUST objectives include:

• Maintain at least two lanes of traffic within the work zone;

• The maximum allowable queue length for any work zone duration is two miles; and

• Delays must be less than 30 minutes for complex projects.

The WANT objectives include:

• Minimize delay costs;

• Minimize vehicle operating costs;

• Minimize construction duration; and

• Maintain emergency services/provisions (rated at poor, average, or good). 
304 

Step 3 – Assign weights to WANT objectives

In the next step of the KT method, weights are assigned to WANT objectives that reflect their relative 

importance in the decision-making process. A score of 1 indicates “least preferred” and a score of 10 indicates 

“most preferable.”

No. WANT Objective Assigned Weight

1 Delay Costs 10

2 Vehicle Operating Costs 8

3 Construction Duration 10

4 Maintenance of Emergency Services 6

Step 4 – Identify candidate MOT alternatives

The next step of the KT method is to identify the alternatives that will be compared and measured against the 

MUST and WANT objectives. The two work zone alternatives include the Contract Staging Plan and the 

Alternative Staging Plan.

Step 5 – Summarize the findings of the Work Zone Impact Assessment

The following table summarizes how the alternatives measure against the MUST objectives:

Decision Framework Option/Example

MUST Objectives

Alternative Evaluation

Contract

Staging Plan

Alternative

Staging Plan

1. Maintain at least two lanes of traffic within the work zone Yes Yes

2. The maximum allowable queue length for any work zone 

duration is two miles Yes Yes

3. Delays must be less than 30 minutes for complex projects Yes Yes

The following table summarizes the impact assessment of the two work zone alternatives against the WANT 

objectives established in Step 2 of the KT Method:

WANT Objectives

Alternative Evaluation

Contract

Staging Plan

Alternative 

Staging Plan

1. Delay costs $39,400 $54,700

2. Vehicle operating costs $2,200 $3,000

3. Construction duration 16 months 8 months

4. Maintain emergency services/provisions (rated at poor, 

average, or good) Good Poor
305 

Step 6 – Evaluate Alternatives against MUST Objectives

The results shown on the MUST objectives table from the previous step indicate that both alternatives satisfy the 

mandatory requirement and/or thresholds. Therefore, both alternatives proceed to the next step.

Step 7 – Evaluate Alternatives against WANT Objectives

In this step of the KT Method, the alternatives are assigned a score (value between 1 and 10) against each 

WANT objective based on how well the alternative meets that objective. The following table shows the results 

of this evaluation:

WANT Objectives

Alternative Score

Contract

Staging Plan 

Alternative

Staging Plan

1. Delay costs 8 6

2. Vehicle operating costs 9 7

3. Construction duration 6 10

4. Maintain emergency services/provisions 

(rated at poor, average, or good) 10 5

Step 8 – Calculate the Weighted Scores of the Alternatives

This step applies the weights established in Step 3 to the results of the alternatives evaluation for WANT 

objectives (previous step) in order to generate weighted scores for each alternative. For example, the weight of 

WANT objective “Delay costs” is 10. In order to calculate the weighted score, the weight of the objective is 

multiplied with the alternative score. For example, the weighted score for the Contract Staging Plan for the 

“Delay costs” objective is 10*8 or 80. The weighted scores for each alternative against each objective is shown 

on the following table:

WANT Objectives

Weighted Alternative Score

Contract

Staging Plan 

Alternative

Staging Plan

1. Delay costs 80 60

2. Vehicle operating costs 72 56

3. Construction duration 60 100

4. Maintain emergency services/provisions 

(rated at poor, average, or good) 60 30

Total weighted score 272 246

 
306 

Step 9 – Evaluate Adverse Consequences – Risk Assessment

This next step involves weighing the feasible alternatives against potential risks identified in the work zone 

impact assessment. The scores from this risk assessment is two-fold and incorporates considerations for the 

likelihood of the event occurring and the severity of the impact. The scores range from values of 1 through 10. 

When evaluating the probability of a risk or event occurring, a score of 1 indicates that the particular risk is 

unlikely to occur while 10 means most probable. When evaluating the severity of impact, a score of 1 indicates 

that the impact is “inconsequential” and a score of 10 indicates that the impact is “very severe.” The probability 

and the severity scores are multiplied in order to obtain the adverse consequence score by risk/event for each 

alternative. Once more, this example presents several hypothetical risk considerations. These risk 

considerations serve as examples for demonstrating the decision framework. These are not in any way 

representative of actual factors considered during the implementation of the I-80 project. Risks considered for 

this analysis include:

• High severity crashes;

• Emergency evacuation due to a natural catastrophe; and

• Flooding.

The following table summarizes the results of the Adverse Consequences Assessment for the two alternatives:

Adverse Consequence

Contract Staging Plan Alternative Staging Plan

Probability Severity Score Probability Severity Score

Flood Impact 3 5 15 3 5 15

High-Severity Crashes 5 4 20 5 4 20

Emergency Evacuation 1 6 6 1 8 8

Total Adverse Consequence Score 41 43

Step 10 – Select the Preferred MOT Strategy

In the final step of the KT Method, the total weighted score and the adverse consequence score are jointly 

considered in comparing the feasible alternatives. In this final step, each feasible alternative is ranked based on 

preference. For example, after weighing in on the weighted and risk assessment score, an analyst may choose to 

go with the Contract Staging Plan since it scored the highest total weighted score and had the lowest adverse 

consequence score.

Alternative

Total

Weighted Score

Total Adverse 

Consequence Score Rank

Contract Staging Plan 272 -41 1

Alternative Staging Plan 246 -43 2
307 

REFERENCES

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and Simulation – A Guide for Decision-Makers. Publication FHWA-HOP-08-029, Federal 

Highway Administration, U.S. Department of Transportation, Washington, D.C., August 

2008. 

2. Hardy, M., and K. Wunderlich. Traffic Analysis Toolbox Volume IX: Work Zone Modeling 

and Simulation – A Guide for Analysts. Publication FHWA-HOP-09-001, Federal Highway 

Administration, U.S. Department of Transportation, Washington, D.C., March 2009. 

3. Work Zone Safety and Mobility. Maryland State Highway Administration (MD SHA), 

Maryland. http://www.sha.maryland.gov/index.aspx?pageid=403&d=88. Accessed 

January 11, 2012. 

4. Traffic Mitigation Guidelines for Work Zone Safety and Mobility. New Jersey Department of 

Transportation, New Jersey, October 2007. http://nj.gov/transportation/eng/documents/BDC/

pdf/attachmentbdc07t07.pdf. Accessed January 11, 2012. 

5. Ullman, G.L., and T.J. Lomax. A Primer on Work Zone Safety and Mobility Performance 

Measurement. Publication FHWA-HOP-11-033, Federal Highway Administration, U.S. 

Department of Transportation, Washington, D.C., September 2011. 

6. Sankar, P., K. Jeannotte, J.P. Arch, M. Romero, and J.E. Bryden. Work Zone Impacts 

Assessment – An Approach to Assess and Manage Work Zone Safety and Mobility Impacts of 

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