1

Work Zone Performance Measures 

Pilot Test 

April 2011 
Notice 

The Federal Highway Administration 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-11-022 

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

 4. Title and Subtitle 

WORK ZONE PERFORMANCE MEASURES PILOT TEST 

 5. Report Date 

April 2011 

 6. Performing Organization Code 

 7. Author(s) 

Gerald L. Ullman , Timothy J. Lomax , and Fan Ye -TTI 

Tracy Scriba – FHWA 

 8. Performing Organization Report No. 

 9. Performing Organization Name and Address 

Texas Transportation Institute 

The Texas A&M University System 

College Station, Texas 77843-3135 

10. Work Unit No. (TRAIS) 

11. Contract or Grant No. 

DTFH61-06-D-00007 

12. Sponsoring Agency Name and Address 

U.S. Department of Transportation 

Federal Highway Administration 

Office of Operations 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

13. Type of Report and Period Covered 

14. Sponsoring Agency Code 

HOTO 

15. Supplementary Notes 

Project Leader: Tracy Scriba, FHWA. 

Project carried out under contract to Battelle for the FHWA Office of Operations. 

16. Abstract 

Currently, a well-defined and validated set of metrics to use in monitoring work zone performance do not 

exist. This pilot test was conducted to assist state DOTs in identifying what work zone performance 

measures can and should be targeted, what data they will need to collect to compute those measures, and 

what methods exist to obtain that data. Work zone activity and traffic data from five work zone projects 

were gathered and analyzed. Multiple data sources and collection methods were examined and utilized to the 

extent available at each project. These sources were field crew personnel manually documenting queue 

presence, length, and duration; traffic surveillance data from a transportation management center or from 

portable work zone ITS; and third-party probe vehicle data (in this test, large truck speed data obtained via 

the FHWA Office of Freight Management).  

The results of the pilot test indicate that manual documentation of queuing by field personnel, and the use of 

permanent or portable traffic sensor data can be used effectively to measure work zone impacts, given that 

information as to the time and location of work activities is known. Probe vehicle data is also believed to be 

a viable source of data, but sample size issues did limit is applicability in this pilot test. Average and 

maximum queue lengths and duration, duration of queues exceeding pre-determined thresholds, vehicle 

exposure to queues, and vehicle delays when queues are present were among the several performance 

measures tested and demonstrated as viable indicators of work zone mobility impacts. A number of lessons 

learned through this pilot test effort are also included in the report. A primer on how to select and compute 

work zone performance measures is being developed based on the findings documented in this report. 

17. Key Words 

WORK ZONE SAFETY AND MOBILITY, TRAFFIC 

IMPACTS, PERFORMANCE MEASURES 

18. Distribution Statement 

No restrictions. 

19. Security Classif.(of this report) 

Unclassified 

20. Security Classif.(of this page) 

Unclassified 

21. No. of Pages 

112 

22. Price 

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

 
v 

ACKNOWLEDGEMENTS 

The authors of this report gratefully acknowledge the contributions by several representatives of 

the following public agencies during the data collection portion of this project: 

• Nevada Department of Transportation 

• Regional Transportation Commission of Southern Nevada, Freeway and Arterial System 

of Transportation (FAST) 

• North Carolina Department of Transportation 

• Pennsylvania Department of Transportation 

• Washington State Department of Transportation. 

The authors are also appreciative to staff in the FHWA Office of Freight Management and at the 

American Trucking Research Institute for their efforts in providing truck transponder speed data 

from each of the pilot test sites. Finally, the assistance of iCone® staff in an attempt to obtain 

speed and volume data at the I-95 site near Lumberton, NC is also gratefully acknowledged. 
vi

TABLE OF CONTENTS 

1. INTRODUCTION ................................................................................................................. 1

WHY MONITOR WORK ZONE PERFORMANCE? ........................................................... 2

WHY CONDUCT A WORK ZONE PERFORMANCE MEASURE PILOT TEST? ........... 3

2. BACKGROUND AND DESCRIPTION OF THE WORK ZONE 

PERFORMANCE MEASURE PILOT TEST .................................................................... 5

BACKGROUND ..................................................................................................................... 5

DESCRIPTION OF THE WORK ZONE PERFORMANCE MEASURE PILOT TEST ...... 8

3. IDENTIFYING THE WORK ZONE PERFORMANCE MEASURES 

OF INTEREST .................................................................................................................... 11

EXPOSURE MEASURES OF INTEREST .......................................................................... 12

QUEUE MEASURES OF INTEREST ................................................................................. 14

DELAY MEASURES OF INTEREST ................................................................................. 16

TRAVEL TIME RELIABILITY MEASURES OF INTEREST .......................................... 16

SAFETY MEASURES OF INTEREST ................................................................................ 18

4. DATA COLLECTION METHODS .................................................................................. 21

ELECTRONIC TRAFFIC MONITORING .......................................................................... 22

Conditions at a Particular Point on the Roadway .................................................................. 22

Travel Time Data along a Roadway Segment ....................................................................... 24

MANUAL WORK ZONE MOBILITY MONITORING ..................................................... 25

SAFETY MONITORING DATA ......................................................................................... 26

5. PERFORMANCE MEASURE COMPUTATIONAL PROCEDURES ......................... 29

POINT MEASUREMENT ELECTRONIC TRAFFIC SURVEILLANCE DATA 

COMPUTATIONS ................................................................................................................ 29

TRAVEL-TIME BASED ELECTRONIC SURVEILLANCE DATA COMPUTATIONS 32

MANUAL QUEUE LENGTH DATA COMPUTATIONS ................................................. 32

6. PILOT TEST LOCATION #1: I-95, LUMBERTON, NC .............................................. 35

DESCRIPTION ..................................................................................................................... 35

DATA SOURCES ................................................................................................................. 35

RESULTS .............................................................................................................................. 37

Exposure Measures ................................................................................................................ 37
vii

Queuing Measures ................................................................................................................. 39

Delay Measures ..................................................................................................................... 42

KEY FINDINGS FROM I-95 LUMBERTON PILOT TEST .............................................. 44

7. PILOT TEST LOCATION #2: I-405, SEATTLE, WASHINGTON .............................. 45

DESCRIPTION ..................................................................................................................... 45

DATA SOURCES ................................................................................................................. 45

RESULTS .............................................................................................................................. 48

Exposure Measures ................................................................................................................ 48

Queuing Measures ................................................................................................................. 50

Delay Measures ..................................................................................................................... 54

KEY FINDINGS FROM I-405 SEATTLE PILOT TEST .................................................... 57

8. PILOT TEST #3: I-95, PHILADELPHIA, PA ................................................................. 59

DESCRIPTION ..................................................................................................................... 59

DATA SOURCES ................................................................................................................. 59

RESULTS .............................................................................................................................. 61

Exposure Measures ................................................................................................................ 61

Queuing Measures ................................................................................................................. 63

Delay Measures ..................................................................................................................... 67

KEY FINDINGS FROM I-95 PHILADELPHIAPILOT TEST ........................................... 69

9. PILOT TESTS #4 AND #5: I-15, LAS VEGAS, NV ........................................................ 71

DESCRIPTION ..................................................................................................................... 71

DATA SOURCES AND ANALYSES ................................................................................. 74

RESULTS .............................................................................................................................. 76

Exposure Measures ................................................................................................................ 76

Queuing Measures ................................................................................................................. 79

Delay Measures ..................................................................................................................... 93

Travel Time Reliability Measure ........................................................................................... 95

KEY FINDINGS FROM THE I-15 PILOT TESTS ............................................................. 97

10. FINDINGS AND LESSONS LEARNED .......................................................................... 99

DATA COLLECTION AND ANALYSIS FINDINGS AND LESSONS LEARNED ........ 99

Manual Documentation of Queues by Field Personnel ......................................................... 99
viii

Use of Electronic Traffic Surveillance Data ........................................................................ 101

Truck Probe Spot Speed Data .............................................................................................. 102

Comparison across Possible Data Collection Methods ....................................................... 103

FINDINGS AND LESSONS LEARNED REGARDING PERFORMANCE MEASURE 

COMPUTATION AND INTERPRETATION ................................................................... 106

Exposure Measures .............................................................................................................. 106

Queue Measures .................................................................................................................. 107

Delay Measures ................................................................................................................... 108

Travel Time Reliability Measure ......................................................................................... 109

Safety Measures ................................................................................................................... 109

NEXT STEPS ...................................................................................................................... 109

11. REFERENCES .................................................................................................................... 111

 
ix

LIST OF TABLES 

Table 1. Exposure Measures of Interest ....................................................................................... 13

Table 2. Queuing Measures of Interest ........................................................................................ 15

Table 3. Delay Measures of Interest ............................................................................................ 17

Table 4. Safety Measures of Interest ............................................................................................. 19

Table 5. Overview of Data Collection Methods for Work Zone Mobility Data. .......................... 21

Table 6. Data Collection Form for Manual Documentation of Queue Lengths and Duration ..... 27

Table 7. Queue Length Computations Based on Point Measurement Data ................................. 30

Table 8. Truck Transponder Sample Size, I-95, Lumberton ....................................................... 37

Table 9. Hours of Work Activity, I-95 Lumberton ..................................................................... 38

Table 10. Roadway Capacity Loss Measures, I-95 Lumberton ................................................... 39

Table 11. Vehicle Exposure Measures, I-95 Lumberton ............................................................. 39

Table 12. Comparison of Queue Measures, I-95 Lumberton ...................................................... 40

Table 13. Comparison of Queue Conditions During Travel Time Studies, I-95 Lumberton ....... 42

Table 14. Comparison of Delay Measures, I-95 Lumberton ........................................................ 43

Table 15. Comparison of Speed and Delay During Travel Time Studies, I-95 Lumberton ......... 44

Table 16. Spot Sensor Location Northbound, I-405 Seattle ......................................................... 46

Table 17. Spot Sensor Location Southbound I-405 Seattle .......................................................... 47

Table 18. Truck Transponder Sample Size, I-405, Seattle ........................................................... 48

Table 19. Hours of Work Activity, I-405 Seattle ......................................................................... 49

Table 20. Roadway Capacity Loss Measures, I-405 Seattle ........................................................ 49

Table 21. Vehicle Exposure Measures, I-405 Seattle .................................................................. 50

Table 22. Comparison of Queue Measures, I-405 Seattle ........................................................... 51

Table 23. Comparison of Queue Conditions During Travel Time Studies, I-405 Seattle ............ 53

Table 24. Comparison of Delay Measures, I-405 Seattle ............................................................ 55

Table 25. Comparison of Delays During Travel Time Studies, I-405 Seattle .............................. 56

Table 26. Spot Sensor Locations Northbound, I-95 Philadelphia ............................................... 60

Table 27. Spot Sensor Locations Southbound, I-95 Philadelphia ............................................... 61

Table 28. Truck Transponder Sample Size, I-95, Philadelphia .................................................... 61

Table 29. Hours of Work Activity, I-95 Philadelphia .................................................................. 62

Table 30. Roadway Capacity Loss Measures, I-95 Philadelphia ................................................. 63

Table 31. Vehicle Exposure Measures, I-95 Philadelphia ........................................................... 63

Table 32. Comparison of Queue Measures, I-95 Philadelphia .................................................... 65

Table 33. Comparison of Queue Conditions During Travel Time Studies, I-95 Philadelphia ..... 66

Table 34. Truck Transponder Queue Indicators, I-95, Philadelphia ............................................ 67

Table 35. Comparison of Delay Measures, I-95 Philadelphia ..................................................... 68

Table 36. Comparison of Delay Conditions During Travel Time Studies, I-95 Philadelphia ...... 69

Table 37. Location and Dates of Long-Term Lane Closures in South Project Limits ................. 74

Table 38. Truck Transponder Speed Sample Size, I-15, Las Vegas ............................................. 77

Table 39. Roadway Capacity Loss Measures, I-15, Las Vegas ................................................... 78
x

Table 40. Vehicle Exposure Measures, I-15, Las Vegas .............................................................. 79

Table 41. Summary of Queue Measures, I-15 Las Vegas ............................................................ 89

Table 42. Comparison of Queue Threshold Measures, I-15 Las Vegas ...................................... 90

Table 43. Comparison of Delay Measures, I-15 Las Vegas ........................................................ 93

Table 44. Travel Time Data Comparison, I-15, Las Vegas .......................................................... 95

Table 45. 95th Percentile Buffer Indices, I-15, Las Vegas ........................................................... 96

Table 46. 80th Percentile Buffer Indices, I-15, Las Vegas ........................................................... 97

Table 47. Comparison of Data Collection Methods for Work Zone Monitoring 

and Performance Measurement ................................................................................................... 104

 
xi

LIST OF FIGURES 

Figure 1. Traffic Mobility Performance Measure Used by Missouri DOT (7). ............................ 6

Figure 2. Ohio DOT Work Zone Safety Performance Measures (recreated from 9). ................... 7

Figure 3. Illustration of a Portable Traffic Sensor Data Collection Device. ................................ 23

Figure 4. Illustration of Traffic Surveillance Estimates using Spot Sensor Data. ....................... 29

Figure 5. Example of Sensor Speed Analysis to Determine Duration and Length of Queue. ..... 31

Figure 6. Components of Work Zone Delay. ............................................................................... 33

Figure 7. Portable Traffic Data Collection Device at Pilot Test Location. .................................. 36

Figure 8. Work Exposure Measures, I-95 Lumberton. ................................................................ 38

Figure 9. Comparison of Queue Length Measures, I-95 Lumberton. .......................................... 41

Figure 10. Work Exposure Measures, I-405 Seattle. ................................................................... 48

Figure 11. Comparison of Queue Length Measures, I-405 Seattle. ............................................. 52

Figure 12. Work Exposure Measures, I-95 Philadelphia. ............................................................ 62

Figure 13. Comparison of Queue Length Measures, I-95 Philadelphia. ..................................... 64

Figure 14. I-15 Express Lanes Widening (South) Project. .......................................................... 72

Figure 15. I-15 Interchange and Freeway Widening (North) Project. ......................................... 73

Figure 16. Percentage of Calendar Nights Involving Temporary Lane Closures 

at the South Project, I-15 Las Vegas. ................................................................................ 78

Figure 17. Changes in Average Speeds Northbound Before and During Evaluation Period, 

I-15 Las Vegas. ................................................................................................................. 81

Figure 18. Changes in Average Speeds Southbound Before and During Evaluation Period, 

I-15 Las Vegas. ................................................................................................................. 83

Figure 19. Effects of the Pilot Test Projects on Hours of Congestion, I-15 Las Vegas. ............. 85

Figure 20. Effect of the Pilot Test Projects on Average Queue Length, I-15 Las Vegas. ............ 87

Figure 21. Effects of Pilot Test Projects on Maximum Queue Lengths, I-15 Las Vegas. ............ 88

Figure 22. Percent of Daily Traffic That Encounters Queuing, I-15 Las Vegas. ........................ 92

xiii

EXECUTIVE SUMMARY 

Transportation professionals are increasingly pressed to demonstrate sound management 

decisionmaking and resource allocation. Performance management is a method to quantify and 

improve performance, and engage and communicate with citizens and other stakeholders. One 

of the challenges facing state Departments of Transportation (DOTs) is to determine how to best 

measure and track safety and mobility impacts associated with highway work zones. The 

development and use of work zone safety and mobility performance measures can be valuable to 

agencies in several ways, including the following: 

• They allow agencies to quantify how work zones are impacting motorists, and how 

actions being taken (management strategies, technologies deployed) to mitigate those 

impacts are or are not working. 

• They assist agencies in making investment decisions, developing and improving policies, 

and defining program priorities. 

• They assist agencies in communicating with elected officials and with the public. 

This report describes the results of a pilot test conducted to assist state DOTs in identifying the 

following: 

• What work zone performance measures can and should be targeted? 

• What data is needed to compute the measures? 

• What methods exist to obtain that data? 

In addition, the test was designed to generate practical guidance, lessons learned, useful tips, etc., 

that state DOTs could use to initiate and/or improve upon a successful work zone performance 

measurement program. 

A series of potential work zone performance measures were developed to address potential state 

DOT needs under each of the following five priority categories: 

• Exposure 

• Traffic queuing 

• Traveler delay 

• Travel time reliability 

• Safety 

For the pilot tests, emphasis was placed on collecting the needed data and performing the 

necessary computations for the measures in four of the five categories. Safety performance 

measures were excluded from the pilot test effort because of the potential for significant lag 

times in obtaining crash data at the pilot test locations, and because safety performance measures 
xiv

themselves are fairly well-defined and understood. For the remaining categories, the proposed 

performance measures in each category were pilot tested at five different projects nationally: 

• I-95, Lumberton, North Carolina 

• I-95, Philadelphia, Pennsylvania 

• I-405, Seattle, Washington 

• I-15/US95 Design-Build Project, Las Vegas, Nevada 

• I-15 Express Lane Project, Las Vegas, Nevada 

In addition, two different methods for collecting the data necessary to compute the performance 

measures were also tested (to the extent possible) at each project: 

• Electronic traffic surveillance data of conditions (e.g., speeds) at particular points on the 

roadway, or of elapsed travel time along a particular roadway segment 

• Manual observations of queue durations and lengths by field personnel during work 

activities. 

The performance measures computed with each type of data were compared to each other, and to 

ground-truth measurements of travel times, delays, and queues made by research staff. Key 

findings with regard to the computation and use of work zone performance measures were as 

follows: 

• Work activity measures (percent of days worked, average hours per day of work) is 

useful in tracking and comparing contractor level of effort. 

• Capturing lane closure hours (percent of hours involving 1, 2, etc. lanes closed) is 

relevant only if total lanes remaining open is also captured. 

• The percent of time when queues exceed selected threshold values should be useful as a 

potential performance specification for work zone traffic control and impact mitigation. 

• The measure “percent of traffic encountering a traffic queue” provides a direct indication 

of the breadth of impacts of the work zone to the motoring public. 

• The average duration of queues, average queue length, and maximum queue length all 

have value for assessing both project-level and process-level traffic management 

decisions pertaining to work zone mobility. 

• The average delay per queued vehicle, along with the percent of traffic encountering a 

queue, appears to be a straightforward way to account for both the intensity and breadth 

of work zone impacts on motorists. 

• The buffer index, as a travel time reliability measure for work zone performance 

monitoring, appears to offer useful insights into another dimension of user impacts due to 

the work zone at locations where recurrent traffic congestion already exists prior to the 

start of the project. 
xv

Key lessons were also learned with respect to data collection and analyses of work zone 

exposure and mobility data, and include the following: 

• Estimates of queue lengths need to include a description of the location of the queue 

relative to the lane closure (upstream, beyond taper and into work zone, partially 

upstream and partially within the work zone, etc.). 

• Field personnel documentation of when and where lane closures were placed and hours 

of work activity will still be needed to compute mobility performance measures, even if 

electronic traffic surveillance data is being used to monitor traffic conditions. 

• It is important to make sure that the traffic sensors themselves will remain operational 

when construction begins. 

• The level of accuracy in work zone mobility performance measurement achievable with 

electronic traffic surveillance data depends heavily on the design of the system 

(particularly traffic sensor spacing). 

To assist practitioners in applying the findings and lessons learned from this pilot test effort, a 

primer on selecting and computing work zone performance measures is being developed to 

accompany this report. 

 
xvi
1

1. INTRODUCTION 

On September 9, 2004, the Federal Highway Administration (FHWA) amended its regulation 

(23 CFR Part 630) that governs traffic safety and mobility in highway and street work zones (1). 

The updated rule requires state departments of transportation (DOTs) to consider and establish 

three key components as part of an overall work zone safety and mobility program: 

• The required implementation of an overall, state-level work zone safety and mobility 

policy. 

• The development and implementation of standard processes and procedures to support 

policy implementation, including procedures for work zone impacts assessment, 

analyzing work zone data, training, and process reviews. 

• The development and implementation of procedures and transportation management 

plans (TMPs) to assess and manage work zone impacts on individual projects. 

One of the more challenging provisions in the rule is the requirement for states to collect and 

analyze both safety and operational/mobility data to support the initiation, assessment, and 

enhancement of agency-level processes and procedures addressing work zone impacts. 

Specifically, states are encouraged to develop and implement systematic procedures that assess 

work zone impacts in project development, and states need to manage safety and mobility during 

project implementation (1). In addition, 

“States shall use field observations, available work zone crash data, and 

operational information to manage work zone impacts for specific projects during 

implementation. States shall continually pursue improvement of work zone safety 

and mobility by analyzing work zone crash and operational data from multiple 

projects to improve state processes and procedures. States should maintain 

elements of the data and information resources that are necessary to support these 

activities” (1). 

This provision in the rule does not require states to necessarily collect new data during project 

implementation, but to make use of whatever data they have available. However, FHWA does 

suggest that states may need to establish or improve processes to access, collate, and analyze that 

information to support safety and mobility policy activities and may need to collect additional 

data if limited data are currently not available (2). States are free to enhance whatever data they 

do collect to improve their evaluation and monitoring procedures. Obviously, the challenge 

facing state DOTs is to determine how to best measure and track safety and mobility impacts, 

and to assess how practices implemented by the DOT affect the level of impacts. Those activities 

need to support each agency’s policy and procedural benchmarking and evaluation in a manner 

consistent with FHWA requirements. 
2

WHY MONITOR WORK ZONE PERFORMANCE? 

Transportation professionals are increasingly pressed to demonstrate sound management 

decision-making and resource allocation. Performance management is a method to quantify and 

improve performance, and engage and communicate with citizens and other stakeholders (3). 

Overall, performance measures to gauge agency efforts are currently being used by state DOTs 

in the following areas: 

• Asset preservation 

• Mobility and accessibility 

• Operations and maintenance 

• Safety 

• Security 

• Economic development 

• Environmental 

• Social equity (i.e., which user groups are impacted) 

• Transportation delivery 

Depending on the topic of interest, an agency may use “output” measures of performance that 

describe how much effort has been made to address a particular issue or concern, or “outcome” 

measures that reflect the actual effects or results experienced with respect to that issue. In fact, 

both types of measures are often needed to fully characterize an issue. 

From the state DOT perspective, the use of work zone safety and mobility performance measures 

are valuable for the following reasons: 

• Work zone performance measures allow agencies to quantify how work zones are 

impacting motorists, and how actions being taken (management strategies, 

technologies deployed) to mitigate those impacts are or are not working. This relates 

directly to the intent and the requirements of 23 CFR Part 630. 

• Work zone performance measures assist agencies in making investment decisions, 

developing and improving policies, and defining program priorities. Information about 

the effectiveness or ineffectiveness of strategies and technologies is valuable to state 

DOTs when determining whether or not to include them on upcoming projects. 

Performance measures can also aid agencies in refining work zone policies and 

procedures (e.g., Is setting a specific maximum allowable delay per vehicle a useful 

policy objective? Do the traffic impact analysis tools being used accurately reflect what 

actually occurs in the field?). Finally, performance measures emphasize accountability 

by the agency, since what gets measured typically gets done (or is at least considered). 
3

• Work zone performance measures assist agencies in communicating with elected 

officials and with the public. State DOTs can use work zone performance measures to 

“tell their story” and ensure that everyone has the correct information about how safety 

and mobility is being affected by roadway construction and maintenance efforts under 

their jurisdiction. The story includes both what is being done and how well what is being 

done is working. This type of agency transparency facilitates public understanding and 

can improve acceptance of the impacts that do occur and builds trust that any subsequent 

funding will be spent wisely. 

As the discussion implies, work zone performance measures can be of interest or value to a wide 

range of audiences. More importantly, these different audiences may need somewhat different 

performance measures. 

WHY CONDUCT A WORK ZONE PERFORMANCE MEASURE PILOT TEST? 

Although the importance of having and tracking work zone performance measures is evident, a 

well-defined and validated set of metrics to use in monitoring work zone performance do not 

currently exist. This pilot test was conducted to assist state DOTs in identifying the following: 

• What work zone performance measures can and should be targeted 

• What data they will need to collect to compute those measure 

• What methods exist to obtain that data. 

In addition, the test was designed to generate practical guidance, lessons learned, useful tips, etc., 

that state DOTs could use to initiate and/or improve upon a successful work zone performance 

measurement program. 

Although there have been a few efforts to monitor and evaluate the safety and mobility impacts 

of work zones to date, emphasis on systematic collection and analysis of objective data to 

develop quantitative measures of work zone performance is still lacking. Therefore, a pilot test 

effort to develop and utilize quantitative measures at several real-world work zone projects was 

undertaken. 

5

2. BACKGROUND AND DESCRIPTION OF THE WORK ZONE 

PERFORMANCE MEASURE PILOT TEST 

BACKGROUND 

In a recent study (4), several state DOTs were queried as to their efforts and interest in work 

zone safety and mobility performance measures. In general terms, many states compute delay, 

queuing, and road user costs at some level as part of their work zone planning and design 

procedures. These are accomplished via simple traffic volume-to-work zone capacity 

comparisons or application of macroscopic or microscopic traffic simulation analyses. States 

may use some of these measures or values in establishing incentives and disincentives for 

contracting purposes. Meanwhile, efforts to actually measure traffic and safety impacts during 

work zones are extremely limited. A few states did indicate that they monitor work zones in the 

field to make sure a performance threshold (such as a maximum queue length or maximum delay 

time) is not exceeded at a project. If conditions do get worse than expected, the agency may 

terminate the work activity (typically a lane closure) to allow traffic congestion to disperse or 

assess penalties, although it is also possible that the field crews may not do anything and allow 

conditions to continue. Traditionally, though, the conditions that lead to these actions and the 

performance measure values often do not get documented anywhere other than in the daily 

project diary, and are simply used in follow-up negotiations between the agency and the 

contractor regarding project payments, time charged, or other features. 

Two states (Indiana and Michigan) have recently initiated the reporting of queues back to district 

or headquarter offices in their agency on a more formal basis (5, 6). The intent of this reporting 

is to determine why the queues occurred, since both agencies conduct traffic analyses during 

project design to avoid generating queues. The ultimate goal is to refine and improve the inputs 

and assumptions being used in the analysis tools, such as the work zone capacity assumed or the 

expected hourly demand volumes to the work zone. However, some basic metrics may be 

generated by the agencies for tracking purposes, such as the percent of work zones each year that 

meet the delay expectations for that particular project. Both agencies noted the challenges that 

exist in attempting to monitor and analyze queues occurring in urban work zones due to the 

interdependence between the queues that develop and the diversion to other routes that then 

occurs. 

The Missouri DOT (MoDOT) conducts regular reviews of its work zones statewide and 

compares the traffic conditions existing at those work zones with their expectations from traffic 

analyses made earlier in the work zone planning and design process (7). Figure 1 shows an 

example of this performance measure. These observations are qualitative rather than quantitative 

in nature. Consequently, the relationship between “meeting expectations” and amount and 

duration of delay and congestion that occur is not immediately apparent. However, this approach 

does address a key concern heard from field personnel that performance measures need to be tied 
6

to what is considered acceptable (or the “target”) for that particular work zone or category of 

work zones, and not considered independently from the other decisions that go into the 

successful completion of a particular project. Other mobility/operational measures sometimes 

considered by state DOTs include vehicle delays measured as part of the inspector drive-through 

of the project, and user complaints (8). 

Figure 1. Traffic Mobility Performance Measure Used by Missouri DOT (7). 

With regard to work zone safety performance monitoring, many states do monitor the number of 

fatalities that occur in their work zones annually. Unfortunately, agencies acknowledge that 

without exposure data (e.g., number of work zone hours or work periods, or the traffic volumes 

traveling through the work zones) to normalize these numbers, changes in crash frequencies from 

year to year are difficult to interpret. In addition, the relatively small numbers of work zone 

fatalities that occur in most states often do not yield meaningful insights into problems or 

possible corrective actions that should be taken (some states do enlarge the database considered 

by including injury crashes in the analysis). Also, most crash databases do not include much 

information concerning the work zone characteristics at the time of the crash (e.g., the type of 

temporary traffic control [TTC] in place, the proximity of workers and equipment to traffic), 

which limits what can be extracted from the data. 

Even if an adequate data sample is available for work zone safety performance monitoring, 

another key concern that plagues most agencies is the time lag that typically exists in obtaining 

crash data from individual projects. Interestingly, the Ohio DOT (ODOT) manually collects 
7

police accident reports every two weeks from high-profile projects in its jurisdiction and 

compares to crashes during construction to the three-year average existing before the project 

began (9). ODOT personnel scrutinize those segments where the current work zone crash rate is 

much higher than the three-year average, believing the higher crash rate is an indicator of 

potential traffic management and control concerns. Figure 2 illustrates an example of the work 

zone crash monitoring activities by ODOT. 

   

Figure 2. Ohio DOT Work Zone Safety Performance Measures (recreated from 9). 

A number of researchers have examined work zone crashes in traditional before-during or work 

zone versus no-work zone analyses in recent years, attempting to identify and quantify the 

factors that contribute to crashes and/or to evaluate countermeasures to reduce crash risk (e.g., 

10, 11, 12). Generally speaking, these studies utilize data from multiple work zones in a region 

or state, and may even include data from multiple states. These types of analyses are fairly 

complex and not typically attempted by state DOT personnel; rather, universities or consultants 

are contracted to perform the study. These studies generally suffer from delays in obtaining 

FRA-SR-161 Work zone Crash Location-Frequency Chart

1

12

48

0

0

5

10

15

20

25

30

35

15.20-

15.69

15.70-

16.19

16.20-

16.69

16.70-

17.19

Crash Logpoint (MP 40-4-42.4)

Number of Crashes

16.0

22.0

31.0

3.3

Boxes Contain Pre Work Zone 3-Year Averages by Section (April 15-

November 15) 

FRA-SR-161 Count of Crashes in Work Zone by Month

6

23

32

00000

0

5

10

15

20

25

30

35

12345678

Month (Numerical Value)

Number of Crashes

3.3

8.0

12.3

8.7 9.0

14.0 12.7

4.3

61

72

0

10

20

30

40

50

60

70

80

Num ber of Crashes

2005 Work Zone Crashes Pre Work Zone 3-Yr Average
8

access to the crash data, although some states have implemented electronic crash report data 

entry systems which can significantly reduce this delay time. Even if crash data access concerns 

can be overcome, the design features, operating strategies, and management techniques utilized 

during each work zone project of interest must be manually extracted from project files, 

inspector diaries, or other data sources and combined with the crash data before analyses can be 

performed. This process itself can be extremely time-consuming, and the various combinations 

of these features/strategies/techniques possible make it difficult to establish a uniform dataset 

with enough projects to allow meaningful conclusions to be drawn. Given the challenges 

associated with the use of crash data for performance measurement, some agencies rely on 

surrogate measures such as TTC quality inspection scores, average speeds or speed variance at a 

project, or frequency of fire department responder calls (8). 

DESCRIPTION OF THE WORK ZONE PERFORMANCE MEASURE PILOT TEST 

For this pilot test, a set of desirable work zone safety and mobility-related performance measures 

was first identified. Those measures pertaining to work zone exposure and mobility were then 

targeted to be computed for each of five work zone projects nationally. Safety performance 

measures were not computed in this pilot test effort for two main reasons. First, many state 

DOTs report very lengthy lag times (several months, or even years in some cases) between when 

crashes occur and when a data set of information about the crashes can be obtained by the 

agency for analysis purposes. Waiting for such data from every project was not judged to be a 

worthwhile use of time for this pilot test. The second, and perhaps more important, reason for 

not including work zone safety performance measures in the test is that such measures are 

already fairly well-defined and understood by practitioners. Although it may be challenging for 

some agencies to obtain the data and to find the time to compute crash rates and other work zone 

safety performance measures, the measures once computed are recognized as useful. In contrast, 

it is less clear what types of exposure and mobility-related work zone performance measures can 

be computed with data sources that are typically available to agencies, and how best to interpret 

those measures once they are computed. 

Consequently, the goals of the pilot test project were to identify and compute various types of 

work zone exposure and mobility-related performance measures, and to do this with different 

data collection methodologies that could be used for work zone mobility performance 

measurement. Specifically, the pilot test targeted a proposed manual method of data collection 

that relies on field crew personnel to document queues that develop during work activities, and 

the use of electronic traffic surveillance technology that may be available at certain work zone 

projects. Within the electronic category are project locations where transportation management 

centers (TMCs) already in place have point measurement devices (traffic sensors) that can 

measure traffic volumes, speeds, and detector occupancy values; locations where a portable work 

zone intelligent transportation system (ITS) has been deployed for incident detection and 

response, driver advisories, or other purposes; and point-to-point travel time measurement 
9

technologies that are becoming more and more affordable and prevalent. Given that neither type 

of data has been sufficiently evaluated as a potential source for work zone performance 

measurement data, the pilot tests were designed to have both types of data available at the same 

time so that direct comparisons of the measures estimated from each could be made. Groundtruth data collection by research staff was also obtained to assess how well each data source can 

represent actual conditions at a particular work zone. 

These alternative methods of obtaining and computing the mobility-related performance 

measures were tested at the following five work zone project locations across the country: 

• I-95, Lumberton, North Carolina 

• I-95, Philadelphia, Pennsylvania 

• I-405, Seattle, Washington 

• I-15/US95 Design-Build Project, Las Vegas, Nevada 

• I-15 Express Lane Project, Las Vegas, Nevada 

This report describes the results of those pilot test efforts, and highlights the lessons learned and 

factors to consider when developing these types of work zone performance measures. 

11

3. IDENTIFYING THE WORK ZONE PERFORMANCE MEASURES 

OF INTEREST 

The first step of the pilot test process was to identify a desired set of performance measures to 

target. Input was solicited from members of the AASHTO Subcommittee on Traffic Engineering 

(SCOTE) and the Subcommittee on Systems Operations and Management (SSOM) work zone 

technical teams. Field personnel comments obtained through the recent Texas DOT work zone 

performance monitoring study were also reviewed (4). These efforts identified three key points 

with regards to work zone safety and mobility performance measure identification: 

• The measures selected must relate to the safety and mobility goals and objectives that 

the agency has established for itself. Examples of such goals and objectives established 

by agency policy or procedures include maximum tolerable queue lengths and duration, 

maximum motorist delays, target reductions in work zone crash rates, and minimum 

customer satisfaction ratings. 

• The measures must adequately capture both the breadth and depth of motorist impacts 

so that the trade-offs between accommodating motorists’ needs and other requirements 

of a project (time, access, cost, etc.) can be adequately balanced. The effect that a 

project has upon motorists can vary dramatically from one project to the next. One 

project may result in a few work periods of intense motorist congestion and delays with 

several other work periods of no impacts to motorists, whereas another project may have 

a fairly small but consistent impact upon motorists throughout the duration of the project. 

When examining across all motorists passing through the work zone during the project, 

the average vehicle delay for those two projects may be quite similar. However, they 

would likely be perceived quite differently by the public. Focusing on measures that only 

examine one aspect of those impacts (e.g., maximum individual motorist delay) may not 

accurately reflect the overall picture of what happened, and lead to erroneous decisions as 

to how to best complete the work, or the mitigation strategies that may be needed. 

Rather, several measures are often needed. 

• The measures must be sensitive to the alternative strategies available to agencies for 

accommodating traffic. Over the course of a project, certain tasks or phases may 

generate some impacts while others have little or no impact. When considering how to 

best accommodate travel during a work zone project, agencies have the option of 

choosing when work should occur, how much of the roadway to allocate to the work and 

how much to leave for traffic to use, and what (if any) techniques or strategies that may 

be used to enhance roadway capacity, improve safety and/or traffic flow, etc. Measures 

should be selected to allow agencies to assess whether such decisions did or did not work. 

It may be desirable to compute measures separately by project phase; by work activity 

condition (during work activity when lane closures are present, during work activity 

without lane closures, during periods of no work when lane closures are present, during 
12

periods of no work activity and no lane closures are present); and by various time periods 

during the day or night (peak-period, daytime off-peak, nighttime, weekend, etc.). 

• Different audiences may need or desire different performance measures. For instance, 

measures useful to DOTs for work zone mobility impacts (e.g., percent of work zones 

meeting the agency queue threshold) may be different than those used to describe impacts 

to the public, local residents, or nearby businesses (e.g., average work zone delay). 

Together, these issues indicate the importance of having a suite of performance measures that 

can be tailored as desired to the needs of a particular agency. The measures need to reflect 

project level as well as individual traveler level impacts, and the strategies used to accommodate 

travel. Methods of stratifying these impacts within project phases, periods of work activity, or 

when specific traffic-handling strategies are in effect are also needed. Ultimately, these 

measures may be aggregated across multiple projects for regional and statewide assessments. 

With these requirements in mind, measures were developed to address these needs under each of 

the following five priority categories: 

• Exposure 

• Traffic queuing 

• Traveler delay 

• Travel time reliability 

• Safety 

EXPOSURE MEASURES OF INTEREST 

Exposure measures describe the amount of time, roadway space, and/or vehicle travel that a 

work zone (or a collection of work zones) affects or requires. Both output and outcome exposure 

measures of performance can be useful. For example, output measures of exposure are needed 

for tracking contractor activity and efficiency. The contractor typically has considerable leeway 

in determining when and how specific tasks are performed. Exposure measures that capture how 

much effort (work activity) is being expended, and when such work is being accomplished, 

relative to the total time available for doing the work, is a key indicator of the level of 

importance the contractor is giving to the project. Another dimension of exposure that relies on 

output-based measures is the roadway capacity restrictions required. The number of lanes closed 

(relative to the number of lanes normally open), the hours when the lane closures occur, and the 

lane-mile-hours of closure are additional ways to capture highway agency and contractor 

decisions on when and how work was accomplished. Whereas some work zone design features 

(crossovers, lane shifts) may decrease capacity slightly through the work zone, the magnitude of 

decrease will typically be much less than that experienced by a full lane closure. Table 1 presents 

a list of proposed performance measures pertaining to work zone exposure. Both output- and 

outcome-level measures are described, as is the rationale for including each one in the table. 
13

Table 1. Exposure Measures of Interest 

Measure Measure Type Definition Use 

% Calendar Days (or Nights) with Work Activity Output 

∑Days of Work

∑Days in Evaluation Period

Some projects are issued based on a such projects, comparison of work e

days is appropriate. 

% Available Working Days (or Nights) with Activity 

Output ∑Days of Work

∑AllowableWork Days in Evaluation Period

Other projects are issued with restrinights work can occur; therefore, coshould be based on when work is al

Average hours of work per day (or night) 

Output ∑Hours Worked

∑Days of Work

The amount of time typically used bcan then be used to extrapolate total project, or across similar projects. 

%Work Activity Hours with: 1 lane closed, 2 lanes closed, 3 lanes closed, etc. 

Output ∑Hours Worked with 1, 2,3, etc. lane closed

∑Hours Worked

The amount of time multiple lanes acompared against the amount of delto evaluate the adequacy of lane clo

Average Lane Closure Length 

Output ∑ Lane Closure Length each work period

∑ work periods with a lane closure

Average lane closure length can be uand projects not being monitored as 

Lane-mile-hours of closures 

Output ෍Hours worked with i lanes closed × miles of i lanes closed ௡

௜ୀଵ

Lane-mile-hours of closures can be contractor and work crew productiv

Vehicles passing through the work zone in evaluation period during: -Work activities -Lane closures -Inactive times 

Outcome ∑vehicles entering work zone during periods of 

(activity, lane closures, inactivity, etc.) 

Vehicle exposure during various timestimate delays on a per-vehicle basmay be desirable to assess safety imbasis as well. 

Vehicle-miles-of travel in evaluation period during: -Work activities -Lane closures -Inactive times 

Outcome ∑vehicles entering work zone during periods of 

(activity, lane closures, inactivity, etc.) × length of (work zone, activity area, lane closure, etc.) 

Vehicle-miles-traveled is a traditionestablish vehicle crash rates. 
14 

Indicators of vehicular travel that passes through the work zone (both the number of vehicles and 

the corresponding vehicle-miles traveled) are outcome-level exposure measures. These 

measures allow mobility and safety impacts to be normalized on a per-vehicle or per-vehiclemile basis, and can be further stratified by other output-level exposure measures listed above 

(i.e., vehicle-miles-traveled (VMT) during hours of work activity). These statistics are outcome 

measures (as opposed to output measures) because the creation of significant congestion and 

delays due to roadwork can significantly alter driver route choice diversion decisions, which 

affects the amount of traffic traveling through the work zone. 

One of the challenges of utilizing VMT as an exposure term is in defining what length or limits 

should be used in the computations. For major roadway rehabilitation and reconstruction 

projects, temporary geometric changes over the limits of the project suggest the use of the project 

limits as the basis for estimating VMT exposure. For these types of projects, essentially all 

traffic and VMT would be considered affected by the work zone (travel and shoulder lane width 

restrictions and other geometric constraints would be present even when no work is occurring). 

However, for other projects, vehicle exposure during the times when temporary lane closures are 

occurring (during hours of a hot-mix asphalt overlay job, for example) may be the only exposure 

of interest. Here, the limits of the project may not be as relevant as the length of actual lane 

closures each day or night. Monitoring and computation of VMT exposure to this activity is 

straightforward; relating this value to the total VMT in the section of roadway would be less so. 

For these projects, estimating exposure in terms of the percentage of average daily traffic on the 

facility (or in terms of the total amount of traffic on the roadway during the project) would likely 

be more relevant in determining the percentage of the driving public that likely traveled through 

the work zone. 

QUEUE MEASURES OF INTEREST 

Both queuing and traffic delays reflect the effect of work zones on traveler mobility, and are 

obviously correlated with each other. However, safety considerations relative to the formulation 

of queues (i.e., increased risk of rear-end crashes, ensuring that sufficient advance warning 

signing is located upstream of the start of queuing, etc.) make direct monitoring of their length 

and duration important as well. Also, the fluctuations in traffic demands and other factors will 

affect queuing patterns that develop during each work shift, and across work shifts over the 

duration of a project. Measures that can assess how frequently specific levels of queuing are 

being exceeded (and by how much) are important indicators of these fluctuations. Table 2 

presents a suite of queue measures of performance identified for this pilot test effort. As noted in 

the table, these measures can be defined relative to specific agency thresholds, such as the 

frequency of queues at a project that exceed a given length for a given duration. 

 
15 

Table 2. Queuing Measures of Interest 

Measure Measure Type Definition Use 

% of work activity periods when queuing occurred 

Outcome ∑ Work Periods with a Queue

∑Work Periods in Evaluation Period

This measure can also be defined rthreshold (i.e., the percent of work queues exceeded a given length or 

Average duration when a queue was present 

Outcome ∑ Queue duration each work period with queue

∑Work periods with queue in evaluation period

The average duration of queues cadeciding how far upstream from thwarning motorists about possible d

Average length when a queue was present 

Outcome ∑ Average queue length per work period

∑Work periods with queue in evaluation period

The average length of queues can average vehicle delays if travel timcollected to directly measure delay

Maximum length of queue during evaluation period 

Outcome Max (queue length measurements during evaluation 

period) 

Maximum queue lengths are trackadvance warning signing is being upstream of the lane closure to ademotorists. 

% of work activity when queue > 1 mile 

Outcome ∑ time when queue length ൐ 1 mile

∑Hours of work activity in evaluation period

The threshold distance (1 mile) caagency policy objectives. Also, mmultiple thresholds (i.e., 1 mile, 2 computed to give a more complete patterns occurring at a site. 

Amount (or %) of traffic 

that encounters a queue 

Outcome ∑ traffic when a queue is present during time period 

of interest

Assessing the number of vehicles that encounter a queue is useful fobeginning and ending times of temperiods. 
16 

These measures are defined relative to a no-queue condition during the periods of work activity; 

that is, the assumption is that any queues that develop are the direct result of the work activity 

and temporary lane closures required. If queues were occurring during the same times before the 

project began, these measures would need to be defined in terms of changes from their pre-work 

zone levels in order to isolate the effects of the work zone from these normal congestion impacts. 

Most agencies strive to avoid closing lanes when congestion already exists at a location, 

reducing the need to factor in existing queues. 

DELAY MEASURES OF INTEREST 

From an agency perspective, delays are directly relevant in estimating road user costs caused by 

work activities, which in turn drive decisions regarding bidding approaches and contracting 

strategies employed, incentives and/or disincentive provisions of the contract, etc. From this 

perspective, total delays summed across all users are of most interest. Conversely, from a user 

satisfaction perspective, delays experienced by individual motorists encountering the work zone 

are better indicators of mobility impacts. Recognizing that individual delays can vary 

significantly over the course of a project or even hours of a particular work shift, multiple 

indicators may be needed to capture both the extreme and “typical” impacts are of interest. 

Another measure, percent of work activity time when motorist delays are exceeding some 

threshold will be useful to agencies that have identified a maximum tolerable level of motorist 

work zone delay. Table 3 presents the delay measures of performance targeted in this pilot test. 

TRAVEL TIME RELIABILITY MEASURES OF INTEREST 

Another dimension of assessing travel quality pertains to the reliability of trip travel times on a 

given roadway or route. Drivers want dependable travel times so that they can better plan their 

departure and arrive at a destination near a desired time (13). A given roadway may have an 

average travel time associated with it, but frequently have incidents or other events occur that 

temporarily increase the travel time. Roadways with highly variable travel times require that 

motorists “buffer” in more time in their departure time decision to ensure that they are likely to 

arrive on time, even though there is a chance that they will arrive much earlier than necessary if 

travel conditions are favorable. The prevailing approach to measuring travel time reliability is to 

compare the average travel time for roadway segment in a particular time period (i.e., peak 

period) to the 95th percentile travel time in that time period; the greater the difference between 

the average and the 95th percentile travel time, the more unreliable travel conditions on that 

roadway are considered to be. This difference is usually divided by the average travel time to 

normalize it as a percentage, and is then referred to as the buffer index: 

  ൌ ݔ݊݀݁ܫ ݎ݂݂݁ݑܤ

ܽ݃݁ ܶܶݎ݁ݒܣ െ ݈݅݁ ܶܶݐܿ݁݊ݎ݁݌௛௧95

ܽ݃݁ ܶܶݎ݁ݒܣ
17

Table 3. Delay Measures of Interest 

Measure Measure Type Definition 

Total delay during entire evaluation period 

Outcome ∑Vehicle-hours delay in evaluation period This measure can be m

time to estimate additcaused by the project. 

Total delay per work period 

Outcome ∑ Vehicle െ hours delay in evaluatiom period

∑Work Periods in evaluation period

This measure is usefuvalue of time) for estiachieved by accelerattechniques. 

Total delay per work period when queues are present 

Outcome ∑ Vehicle െ hours delay in evaluation period

∑Work periods with queuing in evaluation period

This measure can be uthe total delay per wovariability in work zoare occurring from wo

Average delay during work activities per entering vehicle 

Outcome ∑ Vehicle െ hours delay during work activity

∑ Vehicles arriving during work activity

This measure can be ufairly frequently at a wcompared fairly easily 

Average delay during work activities per queued vehicle 

Outcome ∑ Vehicle െ hours delay during work activity

∑ Vehicles arriving when queues are present during work activity

This measure can be uoccur sporadically duthe subset of vehicles queue. 

Maximum individual delay during evaluation period 

Outcome Max (individual delay per vehicle during evaluation period) The upper bound on m

experienced during thresponding to public cperceived level of wo

% of vehicles experiencing delays greater than 10 minutes 

Outcome  ∑ Vehicles experiencing ൐ 10 minutes delay during work activity

∑ Vehicles arriving during work activity

This measure indicateexperiencing greater tminutes should be chaagency’s acceptable d
18 

The use of the 95th percentile travel time as the upper limit implies that someone who allows that 

amount of time for their trip would arrive late no more than once every 20 days (but would 

typically be early). Other upper limits could be used as well and interpreted similarly. For 

example, use of the 80th percentile travel time in the above computation would correspond to 

arriving late no more than once every five days (i.e., once in a typical work week). 

Work zones can also temporarily reduce the capacity of the roadway, and influence the reliability 

of travel times on a roadway. This measure is of most interest where there is already some 

degree of travel time unreliability on the roadway segment, and a question exists as to whether 

(and how much) a work zone further affected reliability. In locations where work occurs on 

roadway segments and during times that are normally congestion-free, the relevance of a change 

in travel time reliability is less since the impacts of the work zone are fully characterized through 

the frequency and extent of delays and queues that develop. Consequently, a travel time 

reliability measure was only examined at two pilot test locations where traffic congestion and 

queuing was already occurring on a regular basis prior to the start of the construction project. 

SAFETY MEASURES OF INTEREST 

Safety measures of performance are needed to assess changes in crash risk relative to pre-work 

zone levels for both the individual motorist and for the driving public in total. Crash-based 

performance measures will not be very useful for work zones that are short in length or duration, 

or occur on lower-volume roadways, as the numbers of crashes themselves will be too small to 

draw solid conclusions. Although some agencies may propose to monitor operational indicators 

such as speeds or changes in speed in lieu of actual crash data, there does not yet exist credible 

research that correlates work zone speeds (or other operational measures) to safety impacts. 

Table 4 presents two proposed traffic safety measures of work zone performance. These changes 

could be stratified by time-of-day and/or work activity type if desired. Individual crash rates, 

expressed in terms of crashes per million-vehicle-miles traveled (MVMT) or changes in that 

crash rate when a work zone is present, capture the effect that the work zone had upon individual 

motorist risk. Conversely, the change in crash costs of a particular project or project phase 

represents the effect upon the driving public in total. A project done primarily at night may have 

a higher crash rate increase per MVMT, but may result in far fewer crashes in total than if the 

project had been done during daytime hours, due to the much lower traffic volumes present at 

night. Similarly, construction strategies implemented to reduce project or project phase duration 

could result in a higher crash rate per MVMT, but again lead to fewer total crashes if the project 

duration (and thus the amount of traffic passing through the work zone in total) was reduced 

significantly. 
19 

Table 4. Safety Measures of Interest 

Measure Measure Type Definition Us

% change in crash rate during work zone -total -severe (injury + fatal) 

Outcome ൬ # crashes in wz # vehicles in wz െ # crashes normally expected # vehicles normally expected ൰

൬ # crashes normally expected # vehicles normally expected ൰

Changes in crash rates per mtravelled reflect the additionexperienced by a motorist tzone. 

Change in crash costs from expected no-work zone crash costs 

Outcome ෍ሺ∆crash severity type i୵୸ ൈ $ per crash type i Differences in total crash co

in frequency and severity dtogether in one measure. 

Δ crash severity type iwz = change in number of crashes of a given severity in the work zone from what would ha

the same time period at that location if the work zone were not present 
20 

In addition to a convenient way to characterize the total effects of a project (or group of projects) 

on safety, the use of crash costs best captures the trade-offs that may exist in establishing policy 

decisions or safety mitigation procedures that influence crash severities more so than total 

crashes (certain strategies may increase certain types of less-severe crashes but decrease the 

more severe crashes that could occur). This approach was used in a recent comparison of 

nighttime and daytime work zone safety (14). If used, a decision must also be made as to how 

crash costs themselves will be estimated. Recent FHWA guidelines could be used as a starting 

point (15). 
21 

4. DATA COLLECTION METHODS 

The development of work zone performance measures requires data about the project, work 

activities, crashes, and traffic operations. Project data will come primarily from the project plans 

or files, and daily inspector diaries. Crash data will most likely come from each state’s crash 

database, although an agency may choose to gather crash reports directly from law enforcement 

offices for key projects (such as is done by the Ohio DOT) and may have its field personnel 

collect additional data for some crashes that occur. Finally, to measure mobility impacts, some 

type of traffic operations data must be obtained. Two basic types of such data exist: 

1. Electronic traffic surveillance data of conditions (e.g., speeds) at particular points on the 

roadway, or of elapsed travel time along a particular roadway segment; or 

2. Manual observations of queue durations and lengths by field personnel during work 

activities. 

A summary of the main options for collecting mobility-related data at a work zone is provided in 

Table 5. These options are described in greater detail in the sections that follow. 

Table 5. Overview of Data Collection Methods for Work Zone Mobility Data 

Method Permanent or 

Temporary Devices? 

Electronic or Manual 

Data Collection? 

Point Measures or 

Segment Measures? 

Existing Transportation 

Management Center 

Speed Sensors 

Permanent Electronic Point 

Work Zone Intelligent 

Transportation Systems Temporary Electronic Point 

Automatic Vehicle 

Location (AVL) data Temporary Electronic Can be either, depending on use 

Automatic Vehicle 

Identification (AVI) 

data 

Permanenta

 Electronic Segment 

License Plate 

Recognition data Permanent or temporary Electronic Segment 

Cellular 

Telephone/Bluetooth 

Tracking data 

Permanent or temporary Electronic Segment 

Field Personnel Queue 

Documentation Temporary Manual Segment 

a Portable AVI readers could be developed for temporary applications, if desired. 
22 

ELECTRONIC TRAFFIC MONITORING 

Conditions at a Particular Point on the Roadway 

Traffic surveillance data obtained at a point location generally relies on spot sensors. These 

types of sensors can provide the following: 

• Volume counts 

• Speeds 

• Detector occupancies 

Sensors can be permanently installed or temporary, and may be fixed or portable. The sensors 

may be inductive loops cut into the pavement, video detection cameras1

, or microwave radar 

detectors located next to or over the roadway. For work zone performance monitoring, it is 

generally desirable to have such sensors located at periodic intervals along the roadway segment 

where a work zone exists, including the length upstream where queues due to the work zone may 

extend. Examining the data from several sensors along the roadway allows estimates of such 

things as queue lengths and travel times (computational procedures for doing this are described 

in the next chapter). The quality of these estimates depends on the spacing between sensors and 

other factors. 

In many urban areas, spot sensor data can be easily obtained via existing transportation 

management systems that have already been installed for general roadway monitoring purposes. 

Unfortunately, some work zone activities will interfere with the normal operation of these 

systems because of the need to move communication lines, turn off power to the sensors from 

time to time, pave over sensors, etc. If such activities are planned for the project or if the project 

is located where permanent sensors are not available, it is necessary to rely on alternative data 

sources. For example, a deployment of a portable work zone ITS can be used to gather and store 

traffic sensor data for work zone performance measurement, even if the system is installed 

primarily for other purposes such as incident detection and response or driver advisories (16). 

Also, portable traffic surveillance devices have recently been developed that allow traffic sensor 

data to be easily collected for use in work zone performance measurement. One such device is 

illustrated in Figure 3. The device consists of a rechargeable power supply, wireless 

communication capabilities, radar, and a global positioning system (GPS) antenna, all selfcontained within a standard traffic drum. The vendors of this device gather the data from each 

device, process it, and post it to an Internet site for access by the highway agency personnel or 

whoever else has been authorized to access. A smaller subset of that data can also be made 

available for viewing on a public website as well. 

                                                           

1 Cameras used for visual monitoring of traffic conditions do not gather traffic data
23 

Figure 3. Illustration of a Portable Traffic Sensor Data Collection Device. 

 For both permanent and portable systems that rely on point measurements, the choice of sensor 

spacing will ultimately affect the accuracy of the analysis. A sensor spacing of one mile would 

yield an average queue length error of 0.5 mile, sensors spaced at 0.5-mile intervals would have 

an average queue length error of 0.25 mile, etc. Since queue lengths and delays are related, 

sensor spacing also affects the accuracy of estimated delays due to the work zone. The increased 

accuracy of closer sensor spacing must be balanced against the additional costs for more sensors 

and staff time to monitor and process the data. Also, it is more important to have sensor data 

over the entire length of congestion at a longer spacing than to have only a portion of the 

congestion that develops be monitored at a closer sensor spacing. Once a traffic queue extends 

upstream beyond the last sensor, it becomes impossible to estimate (even with a greater average 

error) the length of the queue or the delay that is generated. 

Another unique data source that was examined as part of this pilot test effort was truck speed 

data obtained via transponders located within the trucks themselves. The database is used by 

private-sector companies to track freight movement. This is an example of automatic vehicle 

location (AVL) technology, which other types of private-sector fleet systems also use to monitor 

their fleet vehicle speeds and position. Transponders periodically upload vehicle position, 

direction of travel, and current speed via satellite to a database. The FHWA Office of Freight 

Management and Operations is sponsoring ongoing research on the use these data for freight 

performance measurement initiatives (17). For the work zone pilot test effort, available truck 

speed readings corresponding to the dates, times, and locations of work zone activity at the pilot 

tests were extracted from the overall database and “binned” in one-mile intervals along the 

roadway. Conceptually, this approach is similar to that of obtaining data at a point location and 
24 

then assuming it represents conditions over some length (based on the distance between 

successive sensors). However, the selection of the roadway “bin” length must take into 

consideration the amount of truck traffic with transponders utilizing the roadway to allow for a 

large enough sample size that traffic conditions can be reasonably monitored. A drawback to the 

use of truck transponder data (as well as the segment-based travel measures discussed in the 

section below) is that it does not provide a way to obtain the traffic volume data necessary for 

estimating system-level impacts such as total vehicle-hours of delay and similar measures. 

Travel Time Data along a Roadway Segment 

Another traffic surveillance approach that can be used to monitor work zone mobility is the 

collection of vehicle probe data in the traffic stream to obtain speed and travel time information. 

A wide range of approaches exist on how such data can be obtained. These include: 

• Automatic vehicle identification (AVI) technology 

• License-plate recognition technology 

• Cellular telephone tracking technology 

• Blue-tooth tracking technology 

It should be noted that, in addition to tracking current position and speeds at a point, AVL 

technology can also track instrumented vehicles continuously as they traverse a route and obtain 

elapsed travel times over a given roadway segment. Unfortunately, a very small portion of 

vehicles in a traffic stream will be outfitted with this type of technology. Furthermore, gaining 

access to that data from the fleet owners can be quite time-consuming, if access can be obtained 

at all. 

Different than AVL technology, automatic vehicle identification (AVI) technology relies on 

antennae mounted at specific locations that can detect a uniquely-numbered sensor in a vehicle at 

each antennae location, and compute elapsed travel times between antennae locations. 

Electronic toll tag technology is perhaps the most common type of AVI system in use for this 

purpose. The real-time traffic map in Houston (see http://traffic.houstontranstar.org/layers/) 

relies on toll tags to estimate average freeway speeds throughout the region. The sensitivity of 

these data to changes in traffic conditions at specific locations along a route (such as at a work 

zone lane closure) depends on the spacing of the antennae used to track the vehicle probes, and 

the market penetration of the devices being monitored within the traffic stream. In some 

situations, it may be worthwhile to deploy portable antennae at strategic locations within and 

upstream of the work zone to capture AVI data and allow for a travel time measurement at a 

finer level of detail than would be possible by using travel time data over the longer segment 

length that is constantly being monitored. To be successful, this type of temporary supplemental 

deployment would need to occur prior to the start of the work zone so that comparable data was 

obtained before the start of construction. 
25 

Other technologies can also accomplish point-to-point travel time estimates, such as electronic 

license-plate recognition systems with plate number matching software. A license-plate 

recognition and matching system was successfully used for real-time travel time monitoring at a 

work zone in Arizona (18). 

Most recently, researchers have been experimenting with the tracking of cellular telephone 

signals and/or devices that emit a Bluetooth signal as a way to obtain elapsed travel times (19, 

20). These types of technologies are appealing to agencies because they do not require large 

investments in equipment to deploy. As with AVI and AVL systems, their effectiveness is 

dependent upon the level of market penetration and volume levels on the roadway segment. In 

addition, the accuracy with which the technology can pinpoint vehicle location influences the 

accuracy of the data that can be obtained. It should also be noted that, in some locations 

nationally, travel time data based on probe vehicles and other sources of travel-time data can be 

purchased from private-sector providers. 

Regardless of the technology used, though, one primary drawback to using these types of probe 

systems is that they do not gather traffic volume data in any way. Consequently, traffic flows 

must either be estimated based on historical data or other information in order to estimate 

system-level impacts such as total vehicle-hours of delay. 

MANUAL WORK ZONE MOBILITY MONITORING 

Although there are various electronic traffic surveillance mechanisms available for use in 

monitoring work zone mobility impacts, a large number of work zone locations occur on 

roadways without such traffic surveillance capabilities. Furthermore, the functionality of spotsensors and other technologies may actually be interrupted during construction activities due to 

the loss or relocation of power or communication lines, loss of structures to attach the sensors or 

antennae to, etc. Another approach to monitoring mobility impacts in work zones is to have 

personnel in the field document the duration and length of traffic queues that develop at the work 

zone. As stated earlier, the existence of a queue should be a key performance measure for work 

zones, as it pertains both to mobility concerns (queues create traffic delays) and safety concerns 

(the presence of queues is associated with increased rear-end crash potential). Direct monitoring 

and documentation of queues as a performance measure makes most sense at locations that 

normally have no congestion and queuing present. In this way, the queues that are documented 

can be attributed solely to the presence of the work zone. If queues and traffic congestion are 

normally present, some amount of “before” data will be required to factor out the pre-work zone 

impacts from what is observed during the work zone itself. 

A simple form that can be used for documenting queue lengths by field personnel for this pilot 

test is provided in Table 6. The more detail that is gathered during each work zone activity that 

creates a traffic queue (generally, this will be periods when travel lanes are temporarily closed), 
26 

the more accurate the estimates of the impacts upon work zone mobility. For the pilot test, field 

personnel were asked to measure or estimate queue lengths hourly as well as to document the 

times when queues began and ended for each work activity period. The personnel were asked to 

complete the form each work shift, regardless of whether queues developed, as a way to extract 

the other work zone exposure data of interest (hours of work activity and length of lane closures) 

and to verify that queues were monitored but just were not present during that particular shift. 

Although some field staff will faithfully document lane closure information during each work 

shift, previous studies suggest that the success of this practice varies widely from worker to 

worker (4). 

Although the manual approach appears fairly simple, several questions remain unanswered as to 

its effectiveness for work zone performance measurement. First and foremost of these is 

whether field personnel can reasonably document queue conditions with enough accuracy and 

precision to be useful to agencies. Intuitively, the answer to this question likely depends on the 

level of importance given to the queue documentation task by field crew supervisors and 

managers, the workload of the person assigned to perform the documentation, and the extent of 

queuing itself. 

SAFETY MONITORING DATA 

The safety-related work zone performance measures listed in Table 4 imply the need for traffic 

volume data, project data, and actual crash data. Certainly, traffic volume data being collected 

through one of the electronic traffic monitoring methods for work zone mobility monitoring 

would be appropriate for work zone safety monitoring as well. If traffic volume data were not 

collected during the project, historical traffic counts or planning estimates of traffic volumes on 

the facility could be used as long as an assumption that traffic volume patterns on the facility 

were not changed by the work zone is reasonable. Project data would likewise be the same as is 

needed for work zone mobility monitoring. 

The challenge exists with respect to work zone crash data. All states do have electronic crash 

databases that can be accessed to obtain crashes occurring in and around the limits of work zone 

limits over the duration of the project. Once that data is obtained, traditional crash rate 

computations can easily be made. However, timely access to crash data hampers many state 

DOTs for work zone safety performance measurement purposes. Many states have a six-month 

to one-year lag time in obtaining crash data. Such lag times limits analysis of work zone safetyrelated performance measures to post-project before-during comparisons performed months or 

years after a project has been completed. Given that the issue is one of safety data timeliness and 

not necessarily the data gathering techniques or computational procedures that challenge 

agencies, this pilot test effort focused exclusively on mobility-related work zone data acquisition, 

performance measurement computation, and interpretation. 
27 

Table 6. Data Collection Form for Manual Documentation of Queue Lengths and Durat

Project: _____________________________________________________________________________________

Date 

Times of Work Activity Times of Work Activity with Lane Closures Queuing During Work Activity with L

Time 

Begin 

Time 

End 

Dir of 

Travel 

Loc of

Work

Time 

Begin

Time 

End 

Dir of 

Travel

Loc of 

Closure # Lns Clsd Time Q Starts Time Q Ends Q Lngth Hr 1 Q Lngth Hr 2 Q Lngth Hr 3 Q Lngt

Hr 4 

Notes: Estimates of queue lengths approximately every hour are desired. However, the time can be adjusted slightly as necessary, as long as th

reporting time is noted. Locations of work and lane closures can be noted using mile markers, stations, etc. 
28 
29 

5. PERFORMANCE MEASURE COMPUTATIONAL PROCEDURES 

In this chapter, various procedures used to compute the work zone performance measures of 

interest are described. Some basic computations must be made once the necessary data is 

obtained to translate that data into the performance measures of interest. For example, data from 

point measures of an electronic traffic surveillance system will have actual speeds at several 

locations, but these must be extrapolated across multiple locations in sequence to estimate travel 

times and delays. Those speeds must also be compared between point measure locations to 

determine the estimated length of queue that exists during each time interval of interest. If a 

travel time-based surveillance system is being used, travel times and delays are being measured 

directly, but queue lengths must somehow be approximated based on those data. If queue 

lengths are being collected directly by field inspectors using the manual documentation 

technique, computations are needed to estimate how travel times may be affected by the length 

and duration of the queues that are documented. The following sections describe these 

computational procedures in detail. 

POINT MEASUREMENT ELECTRONIC TRAFFIC SURVEILLANCE DATA 

COMPUTATIONS 

It is generally assumed that traffic flow characteristics obtained at a particular point 

measurement location represent conditions for some distance upstream and downstream of that 

measurement location. Therefore, a roadway section is divided into a series segments, with 

conditions in each segment assumed to be represented by its corresponding spot sensor data, as 

illustrated in Figure 4. The travel time at any point in time j across a region i (TTij) is simply the 

length of that region (Li) divided by the speed at that point in time obtained from the sensor. 

Summing the individual travel times for each region together provides a total travel time over the 

length of roadway of interest at that point in time. 

Figure 4. Illustration of Traffic Surveillance Estimates using Spot Sensor Data. 

Li Li+1 Li+2 Lengths

Vij, Uij, Oij Vi+1j, Ui+1j, Oi+1j Vi+2j, Ui+2j, Oi+2j Volumes, Speeds, 

Occupancies 

TTi TTi+1j=Li+1j/ Ui+1j TTi+2j=Li+2j/ Ui+2j j=Lij/ Uij 

Travel Times 

during period j
30 

To estimate work zone delays, the travel times estimated in a pre-work zone condition can be 

directly compared to those estimated from the speeds measured during the work zone over the 

same summed length of interest, and the difference between the two considered the individual 

motorist delay being created by the work zone. As an alternative, a desired speed through the 

work zone can be defined and travel times based on that desired speed used as a baseline to 

compare against actual work zone travel times. Such an approach might be used if the agency 

has posted a reduced speed limit through the work zone, and does not want delays measured 

against the normal, non-work zone speed limit and operating speeds. With individual motorist 

delays identified, the number of motorists encountering these delays can be multiplied by this 

individual delay and summed over the duration of the work activity (and eventually, the project) 

to estimate total delay. 

Next, to approximate queue lengths from point measurements, the speeds at each measurement 

location are examined in sequence and over time to identify regions in which speeds drop below 

a selected threshold (for 70 mile-per-hour [mph] roadways, a 35 mph speed would be 

recommended as the threshold denoting queued conditions). The time period when speeds are 

below the threshold represent the duration of the queue in each point measurement segment. To 

estimate the length of queue over time, speeds at successive point measurement locations are 

examined together, and the length Li for each segment denoted as being below the threshold is 

added together for each time interval of interest. 

Figure 5 helps illustrate this process. In this example, point measurements are located 0.2 mile, 

0.8 mile, and 1.3 miles upstream of the temporary lane closure. Project diary information 

indicates that a lane closure began at 9:00 AM and ended at 3:30 PM. The analysis of speeds at 

the upstream sensor locations indicates that a queue began to develop at approximately 11:30 

AM at the first sensor, which grew upstream and reduced speeds at the second sensor at about 

12:30 PM. The queue did not extend back to the third sensor, since speeds never did drop below 

30 mph at that location during the hours of work activity. Therefore, the queue length each hour 

would be estimated as shown in Table 7. 

Table 7. Queue Length Computations Based on Point Measurement Data 

Time Estimated Location of Upstream 

End of Queue 

Estimated Queue Length 

11:00 am None 0 

12:00 pm Between Sensors #1 and #2 0.2 + (0.6/2) = 0.5 mile 

1:00 pm Between Sensors #2 and #3 0.2 + 0.6 + (0.5/2) = 1.05 mile 

2:00 pm “ 1.05 mile 

3:00 pm “ 1.05 mile 

4:00 pm None 0 
31 

Figure 5. Example of Sensor Speed Analysis to Determine Duration and Length of Queue. 
32 

TRAVEL-TIME BASED ELECTRONIC SURVEILLANCE DATA COMPUTATIONS 

If travel time information is obtained directly from an electronic surveillance system, the 

estimation of individual motorist delay over the roadway segment of interest is straightforward. 

The difference in travel times between normal, non-work zone conditions (at the same time-ofday) and those measured when the work zone is present is the individual motorist delay that 

exists at that particular time period. Multiplying the individual delays in a given time interval by 

the number of vehicles passing through the work zone, and accumulated over the days and time 

periods of interest, yields the total delay (in vehicle-hours) caused by the work zone. 

Unfortunately, travel-time based electronic surveillance methods themselves do not also count 

traffic data. Consequently, volumes occurring through the day (and night) on the roadway 

segment must be measured by other means (such as temporary traffic counters) or approximated 

based on historical volumes developed for planning and programming purposes. In many states, 

only total daily traffic volumes are estimated for most roadway segments. If this is the case, a 

method of dissecting that 24-hour count into estimated hourly counts by direction will be 

required. Fortunately, most state agencies have directional and hourly distribution factors 

developed for various roadway classes that can be used for this purpose. In other locations, 

nearby automated traffic recording (ATR) stations may be used to generate directional and 

hourly volume distribution values. 

Travel-time based electronic surveillance data is also more limited with respect to estimating 

queue lengths and durations. If antennae are located close enough together, conditions within 

each segment may be similar enough to allow for a segment-by-segment comparison as 

described above for spot sensor data. However, as the length between successive antennae 

increases, the possibility for missing any queuing that occurs increases. This is because while 

speeds may drop dramatically in the region where congestion and queuing exists, its effects are 

diluted with the other portions of the segment that may be operating at higher speeds, either 

normal (non-work zone) speeds prior to reaching the start of the queue, and/or at speeds near 

capacity flow conditions through the work zone itself. While it would be mathematically 

possible to define equations to relate the lengths of the non-work zone, queued, and work zone 

subsections with a given roadway segment based on assumed or estimated speeds of each 

subsection, the level of effort required and potential magnitude of errors possible suggest that it 

would be preferable to simply rely on such data for travel time delays, and to have field 

personnel manually document queue lengths and durations as described in the next section. 

MANUAL QUEUE LENGTH DATA COMPUTATIONS 

For locations in which field crew documentation of queue lengths present during a work shift is 

the primary traffic flow data available, historical estimates of traffic volumes must be obtained as 

discussed above for travel-time based electronic traffic surveillance systems. In addition, 
33 

computational methods are needed to estimate how travel times are affected by the presence of 

the queue. 

The manual queue documentation approach will work best if applied at locations when and 

where, in the absence of the work zone, congested traffic flow conditions would not exist. If this 

is the case, then the queues as well as the travel times that result because of the queues will be 

attributable to the work activity in the work zone. Of course, it is possible that an incident or 

adverse weather conditions in or immediately upstream of the work zone could also contribute to 

the queue and delays of a work zone. If such incidents occur, it would be important for field 

personnel to make a special note about it on the data collection form so that the queue and delay 

numbers can be appropriately interpreted. 

To estimate how the documented queues result in travel time delays, it is assumed that 

both the queue itself and the work zone result in slower speeds for some travel distance. This is 

depicted in Figure 6. If a queue has formed upstream of the work zone (at the lane closure 

bottleneck), it is realistic to assume that the flow rate through the work zone is at or near 

capacity, such that the speed at capacity flow can be assumed to govern through the work zone. 

For simplicity purposes, an assumption of a linear relationship between speeds and density 

would suggest that the capacity flow speed would be one-half of the free-flow speed on the 

facility. Upstream of the work zone, the queue that develops would be flowing at a speed less 

than the capacity flow speed. Again using a simple linear speed-density relationship, the 

following equation produces an estimate of the average speed in queue as a function of the 

normal roadway capacity and the capacity through the work zone (21): 

 (1) 

Figure 6. Components of Work Zone Delay. 

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟ − − ⎠

⎞ ⎜

⎝

⎛ = 2

1

1 1

2 AverageSpeed in Queue Normal RoadwayCapacity

Free FlowSpeed Work ZoneCapacity
34 

The capacity of the work zone can be estimated using procedures in the Highway Capacity 

Manual (HCM) (22). The HCM also provides procedures to estimate the normal traffic-carrying 

capacity of the roadway segment. For the degree of accuracy being targeted through these 

computations, the following approximations will usually suffice: 

• For 65- and 70-mph roadways: 2200 vehicles per hour per lane * number of lanes on the 

facility 

• For 60-mph roadways: 2000 vehicles per hour per lane * number of lanes on the facility 

Assuming that these speeds are maintained, on average, through the entire length of the queue 

and work zone documented, estimates of average delays per vehicle through the queue can be 

computed as a function of the length of queue. Some threshold (most likely the desired speed or 

the posted work zone speed limit [UWZSL]) would serve as the basis against which the longer 

travel times through the queue would be computed. This queue delay would then be added to the 

delay that would be generated as vehicles pass through the remainder of the work zone at 

capacity flow speeds (30-35 mph): 

஽௘௟௔௬

௏௘௛௜௖௟௘ ൌ ܮ ௤൬ ଵ௨೜

െ   ଵ

௎ೈೋೄಽ൰ ൅ ܮ௪௭ ቆ ଵ

ೠ೑

మ

െ   ଵ

௎ೈೋೄಽቇ  (2)

Once the average delay per vehicle is estimated for each time interval that a queue is noted on 

the documentation form, the total vehicle-hours of delay is computed simply by multiplying the 

normal hourly volume by these average delay values. If the begin and end times of the lane 

closure and queue do not occur exactly on the hour, extrapolation techniques should be used to 

estimate the delays during that portion of an hour. 

The use of historical volumes for these computations implies that any diversion that occurs 

because of the work zone will result in delays for diverted motorists that are approximately equal 

to those being experienced by motorists remaining on the roadway and passing through the 

queue and the work zone. Such an approximation is likely to be fairly reasonable given the 

approximate level of accuracy anticipated in the queue length and duration estimation process. If 

actual traffic counts during the work activity are available, it may still be more appropriate to 

utilize historical volumes on the facility so that the mobility effects of traffic diversion are taken 

into consideration to some degree. In general, diversion concerns will only exist in urban areas 

where there are frequent access and egress points to a roadway, and multiple alternative paths to 

use as diversion routes. In rural areas, the options are much more limited, and most traffic will 

have to pass through the queue and work zone. 
35 

6. PILOT TEST LOCATION #1: I-95, LUMBERTON, NC 

DESCRIPTION 

The first work zone location where the proposed work zone performance data were gathered and 

measures computed was on Interstate 95 in southern North Carolina, near the town of 

Lumberton. A 10-mile section of the roadway (milepoint 0 to milepoint 10) was undergoing 

resurfacing of travel lanes, improvements to the shoulder and other roadside appurtenance 

improvements in both directions of travel. When work was occurring, one of the two lanes in a 

given direction of travel was closed to traffic. The length of the lane closure varied each work 

period, depending on the anticipated work tasks to be completed. 

The posted speed limit on this roadway segment is 65 mph. As of 2007, this section of I-95 

reportedly serviced approximately 47,000 vpd, a significant portion of them large trucks. The 

North Carolina Department of Transportation (NCDOT) decided to allow the contractor to 

perform the work at their discretion anytime between Monday and Friday morning in the 

northbound direction, and between 10 am and 5 pm Monday through Thursday in the 

southbound direction; weekend lane closures were not allowed. Although the NCDOT analysis 

indicated that queues would not be a problem, contract documents also specified that the 

contractor was not to allow traffic queues due to a lane closure to grow beyond three miles in 

length. 

DATA SOURCES 

This section of I-95 is a fairly rural portion of North Carolina, and thus was not within the limits 

of an existing transportation management center (and its associated data collection devices). 

Consequently, the primary source of data for this pilot test was manual documentation of queues 

by field personnel on site each day of work activity. The manual documentation was done using 

the data collection form previously shown in Table 6. Work zone activities were recorded by 

project field crews between the dates of August 4 and November 25, 2008. A total of 66 days of 

work activity occurred during that time period. The summer of 2008 turned out to be extremely 

wet for southern North Carolina. As a result, the contractor was rained out on many of the 

potential weekdays that work could have occurred. 

At this location, an attempt was also made to utilize the portable traffic surveillance devices 

previously described (see Figure 7). For this pilot test, the plan was to deploy six of these 

devices approximately 1-mile apart from each other, three of them upstream of the temporary 

lane closure taper, and three within the work zone itself. 
36 

Figure 7. Portable Traffic Data Collection Device at Pilot Test Location. 

Initially, the device did experience some data communications problems. These problems were 

subsequently corrected. Unfortunately, the end result of the efforts to get these devices to the 

field was that they were used occasionally over the study period, but not each and every work 

shift. In addition, the days that the devices were deployed happened to occur when no queues or 

congestion developed at the site. Consequently, the data that was available from these devices 

could not be applied to this particular pilot test. 

A second source of traffic operational data gathered for this site was truck transponder 

information from the FHWA Office of Freight Management data collection effort (also 

previously described). For this work zone monitoring effort, a request was made to query the 

database on dates of queuing reported by the field crews, and to provide that data in one-mile 

increments within and upstream of the work zone each of those days. As shown in Table 8, the 

query yielded five days of truck transponder data. Unfortunately, the sample size at this site was 

very low, averaging fewer than one truck speed per hour per mile of roadway investigated. 

There were a few instances where multiple observations were obtained during the same hour in 
37 

the same mile segment, but those were the exception rather than the rule (and so were not 

identified specifically in the table). 

Table 8. Truck Transponder Sample Size, I-95, Lumberton 

Date 

Hours of 

Closure 

Examined 

Length of 

Section 

Examined 

Truck 

Transponder 

Observations 

Observations 

per Mile per 

Hour 

August 4, 2008 6 hr 9 mi 55 1.0 

August 28, 2008 10 hr 9 mi 63 0.7 

November 3, 2008 6 hr 9 mi 31 0.6 

November 20, 2008 10 hr 9 mi 72 0.8 

November 24, 2008 10 hr 9 mi 47 0.5 

5-Day Average 8.4 hr 9 mi 53.6 0.7 

RESULTS 

Exposure Measures 

Based on the work day restrictions set forth in the contract documents, a total of 66 days were 

allowed for the contractor to work during the four-month evaluation period of this pilot test. Of 

these, the field crew documentation tables indicated that work actually occurred on 31 of those 

days. As shown in Figure 8, this represents less than 50 percent of the days allowable in the 

contract. If this effort is compared to all days in the entire four-month period, work actually 

occurred slightly more than one out of every four days. 

Intuitively, such numbers seem fairly small, and would normally be cause for an agency to 

investigate more closely and/or potentially look into its current methods of estimating work 

duration and contracting language. However, the rainy weather alluded to previously certainly 

had much to do with this low level of activity. Because this pilot test is for illustrative purposes 

only, rain days were not extracted from the total allowable days in the analysis. For actual 

monitoring purposes, such exclusions might be appropriate in the final calculations by an 

agency. 
38 

Figure 8. Work Exposure Measures, I-95 Lumberton. 

Table 9 summarizes the hours of work activity reported on the field crew data collection forms. 

Over the 31 days of activity, the contractor performed 290 hours of work, averaging 

approximately 9.3 hours per work shift. Based on these data, it does appear that the contractor 

was making full use of available work windows when they did mobilize and initiate a work shift 

on this project. 

Table 9. Hours of Work Activity, I-95 Lumberton 

Work Exposure Measure Value 

Total hours of work activity during evaluation period 

Average number of hours of work per day 

290 

9.3 

The next facet of exposure data gathered and computed for the North Carolina project were the 

roadway capacity losses. These are provided in Table 10. At this project, all work activity that 

occurred involved the closure of one lane of travel in either the northbound or southbound 

direction. Furthermore, when work crews were not on site, all lane closures were removed from 

the site. One does see that over the 31 days of work in the evaluation period, a total of 558 lanemile-hours of roadway capacity loss were required for the project, or an average 1.9-mile lane 

closure each day of work activity. 

27.2%

47.0%

0% 50% 100%

Percent of all days worked

Percent of allowable days worked
39 

Table 10. Roadway Capacity Loss Measures, I-95 Lumberton 

Capacity Loss Exposure Measure Value 

Percent of work hours involving 1of 2 lanes closed in a 

given direction 

Percent of inactive hours involving lane closures 

Lane-Mile-Hours of closures in evaluation period 

Average lane closure length per work activity period 

100% 

0% 

558 ln-mi-hr 

1.9 miles 

The relatively infrequent work activity of this project during the evaluation did have the positive 

consequence of limiting motorist exposure to the work activity and lane closures. As shown in 

Table 11, approximately 348,000 vehicles passed through active work zone lane closures during 

the evaluation period of this project. Overall, this corresponds to less than 7 percent of the total 

amount of traffic that utilized this section of I-95 during the evaluation period. A total of 

661,200 vehicle-miles of travel occurred through the work zone during the evaluation period. 

Table 11. Vehicle Exposure Measures, I-95 Lumberton 

Capacity Loss Exposure Measure Value 

Number of vehicles passing through active lane 

closures in evaluation period 

Percent of total traffic in evaluation period 

encountering work activity and lane closures 

Total vehicle-miles-traveled past active work zone lane 

closures in evaluation period 

348,000 

6.5% 

661,200 veh-mi 

Queuing Measures 

Table 12 and Figure 9 both summarize queue measure characteristics at this site. Although this 

section of I-95 serves a significant amount of traffic during the typical daytime period, the 

temporary closure of a travel lane did not always result in the creation of queues and congestion 

at the site. According to field crew documentation, queues developed only about one out of 

every four days of work activity at the site. On average, queues that did develop on certain days 

lasted 1.3 hours before dissipating, and were approximately 0.6 miles long. The maximum 

queue length documented by the field crews during the evaluation period was 2.0 miles, 

significantly below the 3-mile threshold established by NCDOT. These measures highlight the 

value of monitoring, gathering, and evaluating work zone mobility-related data on an ongoing 
40 

basis. In the absence of such numbers, it would be difficult for NCDOT to decide, based on this 

project, whether to continue to allow daytime lane closures on this section of I-95 on future 

projects. 

Table 12. Comparison of Queue Measures, I-95 Lumberton 

Queue Measure Based on Field 

Crew Data 

Based on 

Electronic 

Truck 

Transponder 

Data 

Days of work activity when queuing occurred 

 

Amount of work activity time when queue > 1 mi 

Amount of work activity time when queue > 3 mi 

Amount of traffic volume through active work zone that 

encounters a queue 

25.8% 

1.4% 

0.0% 

4.8% 

5.6% 

2.5% 

0.7% 

3.6% 

For comparison purposes, Table 12 and Figure 9 also provide the same queue measures 

estimated from the available truck transponder data at this location during the evaluation period. 

It should be noted that the sample size for these estimates was very small, as only one or two 

truck speed samples were typically available in any given hour in any mile interval within the 

study segment. In many cases, no data were available when and where queues were documented 

by the field personnel. Consequently, fewer instances of queuing could actually be identified 

with the truck transponder data. As Table 12 illustrates, the field crews indicated queuing 

approximately once every four days of work activity, while the truck transponder data indicated 

the presence of queues on about once every 20 days of work. Similarly, the queues had to be 

somewhat larger and longer in duration in order to be detected through the truck transponder data 

because of the one-mile interval that was used to group the truck transponder speeds. Estimates 

of average and maximum queue lengths and queue duration via the truck transponder data were 

somewhat higher than those reported by the field crews, and the truck transponder data did yield 

a small amount of time (but more than was reported by the field crews) when the queues 

exceeded the contract-stated threshold of 3 miles. 
41 

Figure 9. Comparison of Queue Length Measures, I-95 Lumberton. 

In addition to the field crew and truck transponder data sources obtained for this pilot test 

location, a limited amount of “ground truth” travel time and queue length data were gathered 

during one week in October 2008. Comparison of these data to the field crew documentation 

indicates reasonable, but not excellent, agreement and accuracy by field personnel in estimating 

queue conditions at the site. During that week, the field crews reported queues on two of the 

four days of travel time studies. Unfortunately, truck transponder data were not available to 

determine how well that data source compared to actual queue lengths at this site. Table 13 

summarizes the comparison between field crew and ground-truth measures of queue duration and 

length on three of the days of travel time data collection. Travel time studies on October 13th

supported field crew documentation that no queues developed during work activities on that day. 

Overall, field crew documentation and travel time studies correlated well on two of the three 

days in terms of queue duration and maximum queue lengths. However, travel time studies did 

show some queuing on October 15 that was not documented in the field crew data collection 

forms. 

1.3

0.6

2.0

4.0

2.8

4.0

0

1

2

3

4

5

Average Queue

Duration, hrs

Average Queue Length,

mi

Max queue length, mi

Field crew documentation Truck transponder data
42 

Table 13. Comparison of Queue Conditions During Travel Time Studies, I-95 Lumberton 

October 14, 2008 October 15, 2008 October 16 2008 

Based on 

Field 

Crew 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Data 

Measured 

via TT 

Studies 

Queue Duration 

(Hrs) 0.5 0.8 0.0 1.8 2.0 1.5 

Maximum 

Queue Length 

(Mi) 

1.0 1.0 1.0 0.6 1.5 2.1 

TT= travel time 

Delay Measures 

Computations of delays from the queue length data at this site uncovered a major source of 

variability that was not anticipated or considered in the pilot test design. Specifically, it became 

clear that simply noting the existing of a queue and its approximate length over time was not 

sufficient to reasonably approximate delays being experienced by motorists who are passing 

through the work zone. This is because delays are highly dependent upon the average speed 

assumed to exist within the length of queue. In the computational procedures previously 

described, it was assumed that a queue would develop at the point of a lane closure bottleneck 

and grow upstream of that point across all approach lanes, leading to very slow speeds in queue. 

In reality, bottleneck locations often developed farther into the work zone lane closure, and grew 

upstream from that point in the remaining open lane. The ramifications of this is that the average 

speed within a queue that forms within the work zone would be expected to be flowing at speeds 

closer to the capacity flow of traffic than at the previously-defined average speed in queue. If the 

queue were to grow to a point where it extended beyond the lane closure taper, the section 

upstream of that taper would then be expected to begin operating at a much slower average speed 

as vehicles fill in both lanes and begin to take turns moving into the open lane and through the 

work zone. 

The ramifications of the location of the queue (and thus whether it is assumed to be operating at 

a capacity flow speed or an upstream traffic queue speed) upon delay estimates is dramatic. As 

noted in Table 14, estimates of the average delay per vehicle caused by the work zone traffic 

queues that developed vary by a factor of almost 10, depending on the assumptions as to where 

the queues were located (within or upstream of the lane closure taper). Comparing the truck 

transponder data, one sees that the delay estimates tended towards the upper end of the range of 

values computed from the field personnel queue length documentation. It should be 

remembered, though, that the delays estimated from the truck transponder data correspond to 

queue lengths that were much longer than those documented by the field personnel. 
43 

Consequently, the correlation between the two data sources is smaller than it actually appears in 

the table. 

Table 14. Comparison of Delay Measures, I-95 Lumberton 

Delay Measure 

Based on Field 

Crew Data 

Dataa 

Based on Truck 

Transponder 

Datab 

Average delay per entering vehicle during work 0.0-0.4 min/veh 0.3 min/veh 

Average delay per queued vehicle during work 0.9-8.9 min/veh 9.7 min/veh 

Maximum delay per queued vehicle during work 2.0-17.5 min/veh 13.7 min/veh 

 

Total delay during work over entire evaluation period 246-2485 veh-hrs 944 veh-hrs 

Total delay per day of work 8-80 veh-hrs 63 veh-hrs 

Total delay per day of work when queues occur 31-311 veh-hrs 944 veh-hrs a

 The lower end of the range shown was computed assuming the queue was located within the work zone and 

moving at capacity flow speeds; the upper end was computed assuming the queue was located upstream from the 

lane closure taper and moving at the computed reduced speed in queue from equation 1. 

b

 This dataset represents fewer days of queuing, and captures only the longer duration and lengthier queues. 

The travel time data collected during October 2008 was also compared to these delay estimates 

and the assumptions regarding the average speed in queue. The results, presented in Table 15, 

indicate that the speed in queue on October 14 matched more closely to the capacity flow speed 

(i.e., the queue was located within the work zone). On October 16, the average speed in queue 

measured through the travel time studies was something between a capacity flow speed and a 

speed in queue when located upstream of the lane closure taper. This would imply that part of 

the queue was located within the limits of the work zone, and part extended upstream of the lane 

closure taper. In fact, notes taken during the travel time studies on that date indicate that the 

queue was indeed located partially beyond, and partially upstream, of the work zone lane closure 

taper. 
44 

Table 15. Comparison of Speed and Delay During Travel Time Studies, I-95 Lumberton 

October 14, 2008 October 15, 2008 October 16 2008 

Based on 

Field 

Crew 

Data a

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Dataa 

Measured 

via TT 

Studies 

Average Speed in 

Queue (Mph) 6.3-30 27 n/a 25 6.3-30 17 

Average Delay per 

Vehicle (Min) 0.4-8.5 0.4 0.0 0.6 0.9-10.0 3.0 

a

 The lower end of the range shown was computed assuming the queue was located within the work zone and 

moving at capacity flow speeds; the upper end was computed assuming the queue was located upstream from the 

lane closure taper and moving at the computed reduced speed in queue from equation 1. 

TT= travel time, n/a=data not available 

KEY FINDINGS FROM I-95 LUMBERTON PILOT TEST 

Based on the data obtained and measures computed with this pilot test, it does appear that field 

personnel were able to document queues fairly accurately when such documentation occurred. It 

does appear that there may have been a work shift or two in which queue documentation was not 

performed (as suggested in Table 12 and Table 13). Given that field personnel have multiple job 

duties and responsibilities at most project sites, an occasional miss of queues was not 

unexpected. 

Overall, the exposure, queue, and delay measures as defined earlier in this report were able to be 

computed using the data gathered by the field personnel, and yielded results which, for the most 

part, appeared reasonable and potentially useful for agency work zone policy and procedure 

assessments in the future. However, a key limitation in the proposed data collection and analysis 

procedures was identified relative to the use of field personnel documentation of queue durations 

and lengths at project sites. Specifically, it will be necessary for field crews to not only 

document the lengths and durations of any queues that develop, but also to assess whether the 

queue is located upstream of the work zone lane closure, or within the lane closure in the work 

activity area. Speeds in queue vary by a factor of 5 to 10 depending where it is located relative 

to the lane closure taper, and so the location of the queue will dramatically alter the delays that 

would be computed based on a given length and duration of queue. 
45 

7. PILOT TEST LOCATION #2: I-405, SEATTLE, WASHINGTON 

DESCRIPTION 

The next performance measure pilot test was located on Interstate 405 in Bellevue, WA (a 

suburb of Seattle) between SE 8th street on the north and 112th Avenue SE to the south (2.6 

miles). This project was a multi-year reconstruction and widening project to add a travel lane in 

each direction, widen or reconstruct several bridges in the section to accommodate the additional 

capacity, and remove the Wilburton tunnel. As of 2007, the most heavily traveled section of I405 was near SE 8th Street in downtown Bellevue, with an average of 200,000 vpd. Within this 

section, there are generally three to four travel lanes per direction, some of which are auxiliary 

lanes between adjacent ramps. The leftmost lane in each direction is designated as an HOV lane 

that is operational between 5 am and 7 pm (that is, it can be used as a regular travel lane at 

night). The posted speed limit on this roadway segment is 65mph. 

The overall project began in July 2007 and was scheduled to be completed in late 2009. Many of 

the project tasks involved work activities away from travel lanes behind concrete barriers, on 

cross-streets to the freeway, etc. For purposes of this pilot test, work activities involving 

temporary lane closures and potential traffic queuing were targeted. The pilot test duration 

lasted between July 9 and October 9, 2008 (92 days). During this study period, work activities 

required the closure of one or more travel lanes at night. A single lane of travel in each direction 

was allowed to be closed beginning at 8 pm, a second lane could be closed at 10 pm (if 

necessary), and a third lane could be closed at 11 pm. Over this time period, a total of 43 nights 

of work activity occurred in the southbound direction of travel, and 39 nights of work activity in 

the northbound direction. The contractor was not allowed to close lanes on Friday or Saturday 

nights. 

DATA SOURCES 

At this location, project field personnel were again asked to fill out a field data collection form to 

obtain nights and hours work activity, lane closure information (location, duration, number of 

lanes closed and open), and approximate hourly queue length data on those nights when queues 

formed. These lane closure and queue length data were then used to estimate work zone 

exposure measures, as well as average individual vehicle delay and total vehicle-hours of delay 

during congestion. 

In addition to the manual queue length documentation by field personnel, electronic traffic 

sensor data was available from the regional TMC that has been operational in the Seattle region 

for a number of years. Along the I-405 segment of interest, a series of inductive loop and nonintrusive traffic sensors have been installed which measure volumes, speeds (at some locations), 
46 

and sensor occupancies (which are correlated to traffic density). Two types of sensor stations 

exist in this system; 

• Single loop sensors which collect vehicle count data only in the travel lane 

• Dual-loop sensor stations that can measure speeds and collect vehicle count data in the 

travel lane 

Non-functional sensors at some sensor locations meant that only one or two lanes of traffic were 

being recorded. Table 16 and Table 17 provide a summary of the location and types of sensors 

in this roadway segment. Although the mixture of single and dual loop sensors may serve the 

TMC functions adequately, they do present a bit of a challenge from the standpoint of 

monitoring work zone performance. Recall that speed data provide the primary indicator of 

queuing and is critical to the estimation of travel times (and changes thereof) upstream and 

through the work zone. Thus, although sensor stations were located at approximately 0.33 to 

0.5-mile intervals in this roadway segment, speed sensors were available at about every other or 

every third set (i.e., at about 1 mile intervals). Further complicating the situation was the fact 

that individual sensor stations in the vicinity of the lane closures were sometimes non-functional 

at night when work was occurring, or only provided vehicle count data instead of both vehicle 

counts and speeds. It is not known whether temporary power disruptions required by work 

activities were responsible for this loss of data, or whether the quality control algorithms used by 

the TMC detected perceived “anomalies” in the data and simply discarded them from the 

database. Either way, the end result was a lack of data on certain nights at critical locations 

upstream and within the lane closure section. 

Table 16. Spot Sensor Location Northbound, I-405 Seattle 

Sensor 

Location Milepoint Sensor Type 

NE 37th 7.00 V only 

NE 44th 7.45 V only 

SE 70th 8.03 V & S 

SE 64th 8.40 V & S 

SE 59th 8.90 V & S 

112th Ave SE 9.21 V only 

SE 47th 9.75 V only 

Coal Crk Pkwy 10.13 V only 

SE 40th 10.55 V & S 

SE 39th 10.79 V only 

I-90 11.21 V only 

SE 30th 11.47 V only 

SE 20th 12.05 V only 

SE 4th 13.06 V & S 

NE 4th 13.60 V only 

V = volume, S = speed 
47 

Table 17. Spot Sensor Location Southbound I-405 Seattle 

Sensor 

Location Milepoint Sensor Type 

SR 520 14.63 V & S 

NE 14th 14.27 V & S 

NE 4th/8th 13.33 V only 

SE 4th 13.06 V only 

SE 20th 12.05 V & S 

SE 30th 11.47 V only 

I-90 11.21 V & S 

SE 40th 10.55 V & S 

Coal Crk Pkwy 10.13 V only 

SE 47th 9.75 V & S 

112th Ave SE 9.21 V only 

SE 59th 8.90 V & S 

SE 64th 8.40 V & S 

SE 70th 8.03 V only 

NE 44th 7.40 V only 

NE 37th 7.00 V only 

V = volume, S = speed 

For this study, manual field personnel documentation of queue duration and length, and TMC 

speed and volume data were all collected each night of work activity in order to allow 

comparisons of the results. In addition, actual travel time studies were conducted during the 

week of September 1, 2008 to assess how each type of data compared to these “ground truth” 

delay and queue length measurements. Finally, a small sample of truck transponder data were 

obtained for three nights of work activity (September 15-17, 2008) over a 7-mile segment of 

roadway (in each direction). A comparison set of truck data were also provided for that roadway 

segment from March 2009. Unfortunately, as was the case for the I-95 Lumberton project in 

North Carolina, the sample size obtained at this site was extremely small (see Table 18). Many 

mile segments had no observations in many hour intervals (93 percent of the mile-segment-hours 

had no speed observations). In only one instance was there more than two speed observations in 

a given mile segment in a given hour of work activity. 
48 

Table 18. Truck Transponder Sample Size, I-405, Seattle 

Date 

Hours of 

Closure 

Examined 

Length of 

Section 

Examineda

Truck 

Transponder 

Observations 

Observations per Mile 

per Hour 

Average Maximum 

Sep. 15, 2008 9 hr 14 mi 18 0.1 6 

Sep. 16, 2008 9 hr 14 mi 10 0.1 2 

Sep. 17, 2008 9 hr 14 mi 15 0.1 2 

3-Day Average 9 hr 14 mi 14.3 0.1 6 

a 7 miles in each direction 

RESULTS 

Exposure Measures 

As shown in Figure 10, the field crew documentation tables indicated that work actually 

occurred on slightly more than one-half of all of the nights during the evaluation period. 

Considering only Sunday through Thursday nights when the contractor was actually allowed to 

be on I-405 and close lanes, work activity occurred on slightly more than 70 percent of the 

allowable nights. 

Figure 10. Work Exposure Measures, I-405 Seattle. 

Table 19 summarizes the hours of work activity reported on the field crew data collection forms. 

Over the 47 nights of activity in the pilot test evaluation period, the contractor performed 407 

hours of work, averaging approximately 8.7 hours per work shift. Overall, it was apparent that 

51.1%

71.2%

0% 50% 100%

Percent of all nights worked

Percent of allowable nights

worked
49 

the contractor was making full use of available work windows at night when they did mobilize 

and initiate a work shift on this project. 

Table 19. Hours of Work Activity, I-405 Seattle 

Work Exposure Measure Value 

Total hours of work activity during project 

Average number of hours of work per day 

407 

8.7 

The next facet of exposure data gathered and computed for the Washington project were the 

roadway capacity losses. These are provided in Table 20. Tracking and consolidating lane 

closure data for this project proved to be a fairly significant task, given the acceptable times of 

lane closures allowed at night in the contract. In addition, there were a few nights when work 

crews were in both directions of travel. Overall, one sees in Table 20 that over one-half of the 

lane closure time involved the closure of three out of four available lanes. The maximum 

number of lanes present within the limits of the work zone each night was used as the baseline, 

rather than attempting to characterize each auxiliary lane or lane drop segment separately. Also, 

there are a very few number of hours included in Table 20 where the entire roadway section was 

closed for a few minutes while overhead beams were moved into place or other tasks occurred 

which required full closure of the roadway. 

Table 20. Roadway Capacity Loss Measures, I-405 Seattle 

Capacity Loss Exposure Measure Value 

Percent of work hours involving: 

 1 of 4 lanes closed in a given direction 

 2 of 4 lanes closed in a given direction 

 3 of 4 lanes closed in a given direction 

 4 of four lanes closed in a given direction 

Percent of inactive hours involving lane closures 

Lane-Mile-Hours of closures in evaluation period 

Average lane closure length per work activity period 

13.8% 

27.4% 

57.1% 

0.2% 

0% 

5,456 

5.5 miles 

Over the 47 nights of work in the evaluation period, a total of 5,456 lane-mile-hours of roadway 

capacity loss were required for the project. This equates to an average of 5.5 miles of lane 
50 

closure each night of work activity. Often, this length of lane closure was split between both 

directions of travel. 

The decision to work at night did minimize the extent to which Seattle drivers were exposed to 

the nightly lane closures on this roadway segment. Still, the absolute exposure numbers were 

quite significant. As shown in Table 21, approximately more than 1.3 million vehicles passed 

through active work zone lane closures during the evaluation period of this project. Overall, this 

corresponds to less than 10 percent of the total amount of traffic that utilized this section of I-405 

during the evaluation period. Meanwhile, nearly 5 million vehicle miles of travel occurred 

through the temporary lane closures over the duration of the evaluation period of this project. 

Table 21. Vehicle Exposure Measures, I-405 Seattle 

Vehicle Exposure Measure Value 

Number of vehicles passing through active lane 

closures in evaluation period 

Percent of total traffic in evaluation period 

encountering work activity and lane closures 

Total vehicle-miles-traveled past active work zone 

lane closures in evaluation period 

1,385,500 

9.3% 

4,846,400 veh-mi 

Queuing Measures 

Table 22 and Figure 11 both summarize queue measure characteristics at this site as documented 

by field personnel on the data collection form, and through analysis of available traffic sensor 

data from the regional TMC. Because work activity typically occurred simultaneously in both 

directions of travel each night, separate analyses were performed for each direction. The 

restriction of temporary lane closures to nighttime hours no doubt did reduce the frequency and 

severity of queues and congestion that might have otherwise developed. However, as noted in 

Table 22, field personnel still reported the creation of queues on 80 percent of the nights when a 

lane closure was implemented in either direction. The traffic sensor data at this site was not as 

sensitive to detecting queue presence, as analyses found evidence of queuing on only 35 to 59 

percent of the nights of work activity. This result was expected given the limited number of 

sensor locations that had functional volume and speed data available. Looking back at Table 16 

and Table 17, the effective spacing between traffic sensors with both volume and speed was 

nearly 1 mile in either direction. Furthermore, there are several segments where the distance 

between useful sensors is several miles (i.e., between mile points 10.55 and 13.06 in the 

southbound direction, between mile points 14.27 and 12.05 in the northbound direction). The 

large spacing of sensors severely limited the accuracy of the estimates of queue length and 

duration that could be made. 
51

Table 22. Comparison of Queue Measures, I-405 Seattle 

Queue Measure 

Southbound N

Based on Field 

Crew Data 

Based on 

Traffic Sensor 

Data 

Based on Fi

Crew Dat

Days of work activity when queuing occurred Amount of work activity time when queue > 1 mi Amount of work activity time when queue > 2 mi Amount of traffic volume through active work zone that encounters a queue 

81.4% 17.1% 11.8% 19.0% 

34.9% 

1.2% 0.0% 4.3% 

82.9% 21.3% 12.6% 13.6% 
52 

The effect of the longer distances between usable speed sensors is evident in the other measures 

as well. Figure 11 illustrates that both queue durations and maximum queue lengths in both 

directions were shorter based on available traffic sensor data than what the field personnel 

documented on their data collection forms. Similarly, the percentage of work activity time when 

queues reportedly exceeded the 1-mile and 2-mile thresholds was much lower for the traffic 

sensor data than what the field crews reported, as was the amount of traffic estimated to have 

encountered a queue when traveling through the nightly work zones during the evaluation period. 

Figure 11. Comparison of Queue Length Measures, I-405 Seattle. 

Floating-vehicle travel time studies were performed during September 2-4, 2008 in order to 

provide a means of comparing the two data collection approaches to a “ground-truth” measure of 

queues and delays. During that week, the field crews reported small queues on September 2nd

and 3rd in the southbound direction, and on all three nights in the northbound direction. Using 

the available traffic sensor data, however, queues were detected only on September 2nd in both 

the southbound and northbound directions. Table 23 summarizes the comparison between field 

crew, traffic sensor and ground-truth queue measures. Overall, the field crews were better able at 

identifying when queuing conditions developed each night (as would be expected). In terms of 

queue lengths and duration, the two data sources appeared to be equally accurate compared to the 

ground-truth travel time data. Although field personnel were not interviewed as part of this 

testing process, it is likely that there was some degree of approximation occurring in terms of 

queue lengths, which could explain some of the deviations from the ground-truth values. 

2.1

1.0

4.0

1.1

0.7

1.4

2.5

1.1

4.0

1.4

0.9

3.1

0

1

2

3

4

5

Average Queue Duration,

hrs

Average Queue Length,

mi

Max Queue Length, mi

SB Field Crew Data SB Traffic Sensor Data

NB Field Crew Data NB Traffic Sensor Data
53

Table 23. Comparison of Queue Conditions During Travel Time Studies, I-405 Seattle 

September 2, 2008 September 3, 2008 Sept

Based on 

Field 

Crew 

Data 

Based on 

Traffic 

Sensor 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Data 

Based on 

Traffic 

Sensor 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Data 

Southbound: Queue Duration (Hrs) Max. Queue Length (Mi) 

1.0 0.3 

0.3 0.5 

0.5 1.1 

1.0 0.5 

0.0 0.0 

0.5 0.9 

1.0 0.3 

Northbound: Queue Duration (Hrs) Max. Queue Length (Mi) 

1.0 0.3 

0.3 0.4 

0.5 0.5 

1.0 0.5 

0.0 0.0 

0.0 0.0 

0.0 0.0 

TT= travel time 
54 

The very limited amount of truck transponder data that could be obtained for this site did not 

allow for a comprehensive comparison to either field-reported or traffic sensor data estimates of 

queues. Field personnel did report small queues developing each of the four nights (in both 

directions) for which transponder data were available, but the transponder data was such that 

slower speeds (15 mph) were detected on only two of the nights, and there was only a single 

speed observation during each night. These single observations occurred in the northbound 

direction, and were actually several miles away from the reported location of the work activity. 

Furthermore, it is not certain that the field personnel and truck transponder frames of reference 

used the same mile marker system. Ultimately, it would not have been possible to estimate 

traffic impacts of the work activity based on the limited transponder data.

Delay Measures 

The limitations of the effectiveness and accuracy of the traffic sensor data subsequently extended 

from queue duration and length estimates into delay computations. Table 24 provides a 

comparison of the delays computed using the field personnel estimates of queuing and the 

computational procedures for estimating delays based on queue lengths to delay estimates 

computed from the available traffic sensor data. In general, the total vehicle-hours of delay 

computed using the project field crew measurements are more than twice those estimated using 

the available traffic sensor data, even if it was assumed that the queues reported by the field 

personnel occurred within the work zone rather than upstream (i.e., a speed in queue of 30 mph 

was used rather than a much lower speed in queue that would have been computed using 

equation 1). Considering the queues as existing upstream of the lane closure points would have 

pushed the delay estimates to the upper values shown in the table. 

Since field personnel were not asked to note where relative to the temporary lane closures the 

queues occurred each night, a wide range of potential delay impacts could have occurred at this 

site during the evaluation period. In Table 24, average delays through a queue may have been as 

small as 6 minutes per vehicle or as high as 22 minutes per vehicle, depending on the speeds that 

existed within the reported queues. Table 25 summaries the travel time “ground truth” speed and 

delay study results from the September 2008 data collection trip, and compares them to the 

estimates of speed and delays computed for those same days via field personnel documentation 

and the traffic sensor data. 
55

Table 24. Comparison of Delay Measures, I-405 Seattle 

Delay Measure 

Southbound Nort

Based on Field 

Crew Dataa

Based on Traffic 

Sensor Data 

Based on Field 

Crew Dataa

Total delay during work activities Delay per night of work Delay per night of work when queues developed Average delay per entering vehicle during work\ activities Average delay per queued vehicle during work activities Maximum delay per queued vehicle during work activities 

2,905-11,368 veh-hr 

68-266 veh-hr 

83-325 veh-hrs 

0.3-1.3 min/veh 1.6-5.7 min/veh

5.9-21.0 min/veh 

450 veh-hr 

10 veh-hr 30 veh-hr 

0.1 min/veh 

1.0 min/veh 

2.1 min/veh 

3,653-14,258 veh-hr 

94-367 veh-hr 

101-394 veh-hr 

0.5-1.8 min/veh 1.7-6.3 min/veh 

5.9-21.9 min/veh 

a The lower end of the range shown was computed assuming the queue was located within the work zone and moving at capacity flow 

computed assuming the queue was located upstream from the lane closure taper and moving at the computed reduced speed in queue fr
56

Table 25. Comparison of Delays During Travel Time Studies, I-405 Seattle 

September 2, 2008 September 3, 2008 Sept

Based on 

Field 

Crew 

Dataa

Based on 

Traffic 

Sensor 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Dataa

Based on 

Traffic 

Sensor 

Data 

Measured 

via TT 

Studies 

Based on 

Field 

Crew 

Dataa

Southbound: Ave. Speed in Queue (Mph) Ave. Delay per Vehicle (Min) 

5.7-30 

0.3 

30 

0.5 

33 

1.0 

5.7-30 

0.5 

--- --- 

12 

3.6 

5.7-30 

0.3 

Northbound: Ave. Speed in Queue (Mph) Ave. Delay per Vehicle (Min) 

5.7-30 

0.4 

30 

0.4 

30 

0.5 

5.7-30 

0.6 

--- --- 

--- --- 

--- --- 

--- data not indicating presence of queue delay a The lower end of the range shown was computed assuming the queue was located within the work zone and moving at capacity flow 

computed assuming the queue was located upstream from the lane closure taper and moving at the computed reduced speed in queue fr
57 

It is interesting to note the range of speeds in queue that was measured by the travel time studies. 

On September 2nd, the traffic queue was located almost entirely within the limits of the lane 

closure, and speeds in queue were very close to those expected to exist when traffic is operating 

at near capacity flow conditions (i.e., 30-35 mph). However, on the other two nights, the queues 

tended to develop near the lane closure taper and extend upstream, which led to average speeds 

in queue of between 12 and 17 mph. Once again, knowing whether any queue that develops is 

located within or upstream of the work zone lane closure can significantly improve the ability of 

an analyst in estimating the average speed in queue, and in turn the actual delays being 

experienced by the traveling public.

KEY FINDINGS FROM I-405 SEATTLE PILOT TEST 

The feasibility of having field personnel document daily (nightly) work activity and queue 

durations and lengths that occur was again validated through this pilot test effort. It appears that 

the field personnel were quite accurate and meticulous in their documentation efforts; all 

instances of queues identified during the ground truth travel time studies were noted as queued 

conditions on the data collection forms. 

A key limitation in the proposed data collection and analysis procedures was again identified 

relative to the use of field personnel documentation of queues. Specifically, it will be necessary 

for field crews to not only document the lengths and durations of any queues that develop, but 

also to assess whether the queue is located upstream of the work zone lane closure, or beyond it 

in the work activity area. 

With regard to the use of electronic traffic sensor data for work zone mobility monitoring and 

performance measure development, a significant amount of effort was required in this pilot test 

to extract and use data files, only to find out that the files themselves were incomplete or 

provided only volume counts. The effectiveness of a spot sensor system to reasonably estimate 

both queues as well as delays due to work zone activity hinges on the availability of speed data at 

each sensor location. System configurations that provide only occasional sensors with speed 

data collection capabilities will provide less accurate estimates of work zone mobility impacts. 

The lack of speed data may be a design constraint, in which case the agency is somewhat limited 

in its options. However, the lack of data may also be a result of construction activities 

themselves, which the agency may be able to mitigate to some degree. There were several nights 

in the evaluation period where certain sensors appeared to “quit functioning” for a period of time 

in the vicinity where the field personnel documented some work activity occurring. It is likely 

that the work activities themselves may have disrupted power or communications to and from the 

sensor to render it unusable during the work activity. Developing and implementing methods of 

counteracting these type of temporary data losses due to work activities would significantly 

improve the potential for using traffic sensor data in computing work zone performance 

measures. 
58 

It was hoped that the review of truck transponder data would have provided additional insights 

into the potential use of this data for work zone traffic mobility monitoring efforts. 

Unfortunately, even though I-405 is a very high-volume facility, the amount of truck traffic that 

uses the facility (and is equipped with transponder technology) appears to be fairly small. This 

is most likely especially true at night once regular commuting traffic has decreased to the point 

where uncongested speeds (i.e., 55 mph or more) can be maintained on the main north-south 

freeway through Seattle (I-5). Once again, the usefulness of truck transponder data depends 

upon having an adequate sample size available, which was not the case here for the I-405 project. 
59 

8. PILOT TEST #3: I-95, PHILADELPHIA, PA 

DESCRIPTION 

A third pilot test location selected for study was an approximate 8-mile segment of I-95 in 

southern Philadelphia, PA, extending from Broad Street on the south to East Allegheny Avenue 

on the north. This section of I-95 consists of three to four lanes per direction, with occasional 

lane additions and drops around the major interchanges with I-676 and I-76. Traffic volumes on 

this section of roadway in 2008 ranged from 90,000 to 150,000 vpd. The posted speed limit is 

between 55 and 60 mph. 

Between August 18 and October 31, 2008, a highway contractor performed the following 

roadway repairs at various locations within this segment: 

• Concrete deck repairs on selected bridges 

• Asphalt milling 

• Asphalt overlay paving 

• Pavement patching and sealing 

• Construction of an expansion dam at one of the bridges 

Work activities were limited to between the hours of 9 pm and 5 am Monday through Friday. 

Over this time period, a total of 13 nights of work activity occurred in the northbound direction 

of travel, and 28 nights of activity in the southbound direction. On each night, the contractor 

usually closed two travel lanes at a location. There were instances, however, when three or more 

lanes were closed and traffic was narrowed down to a single lane of travel in that particular 

direction. On two nights, the contractor closed lanes in both directions of travel. The remaining 

nights involved lane closures in either one direction or the other. 

DATA SOURCES 

The Pennsylvania Department of Transportation (PennDOT) Philadelphia district office agreed 

to have its field personnel provide data collection support over the duration of the pilot test. The 

data collection form that requests work activity, lane closure information (location, duration, 

number of lanes closed and open), and approximate hourly queue length data were filled out by 

these personnel on most nights that work activity occurred. These lane closure and queue length 

data were then used to estimate work zone exposure measures, as well as average individual 

vehicle delay and total vehicle-hours of delay during congestion (using the previously described 

manual computation procedures and hourly traffic volumes). 

In addition to the field personnel manual data collection efforts, data from a series of roadsidemounted non-intrusive traffic sensors located throughout the corridor were accessed to provide 
60 

spot sensor data for analysis purposes. At some locations, sensors counted traffic in both travel 

directions; at other locations, only one direction of travel was being collected. In addition, there 

were some issues with some of the sensors on certain nights of work activity. As a result, data 

were not always available at all sensor locations on all nights of interest in the evaluation period. 

Table 26 and Table 27 provide a summary of the location of sensors in this roadway segment in 

each travel direction. The effective spacing between successive sensors during this pilot test 

ranged from about 0.3 miles to 2.8 miles. Unfortunately, several nights of work activity 

occurred where sensor spacing was fairly large, which limited the sensitivity of the sensor data to 

detect and assess work zone impacts upon travel mobility. Although each sensor, when 

operational, could provide lane-by-lane volumes and speeds, these lane data were summed and 

averaged to yield overall sensor station measures for use in this pilot test analysis. 

Floating vehicle travel time studies were conducted on three nights in early September 2008 to 

provide a ground-truth comparison of the field personnel and traffic sensor data sources. In 

addition, truck transponder data was obtained for those nights when work activity was occurring. 

Researchers were able to obtain truck data over 20 miles (10 miles per direction) on four 

consecutive nights in August 2008 when work activity and temporary lane closures were in 

place. Table 28 summarizes the amount of truck speed data that were obtained. Overall, the 

sample is very low, averaging 0.2 speed measurements per mile per hour. No observations were 

recorded in most mile segments during most hours of the work periods (82 percent of the milesegment-hours had no observations). The maximum amount of data that was obtained for any 

mile segment during any hour of analysis was 5 observations. 

Table 26. Spot Sensor Locations Northbound, I-95 Philadelphia 

Sensor Location Milepoint Distance from 

Upstream Sensor 

Island Ave 12.7 --- 

Enterprise Ave 14.5 1.8 

Broad St 16.6 2.1 

between S. Darren and S. 11th 17.9 1.3 

Walt Whitman Bridge 18.5 0.6 

S of I-676/I-95 20.8 2.3 

between Frankford and Girard 22.8 2.0 

S of Betsy Ross Bridge 25.4 2.6 

Lefevre St 26.3 0.9 
61 

Table 27. Spot Sensor Locations Southbound, I-95 Philadelphia 

Sensor Location Milepoint Distance from 

Upstream Sensor 

Lefevre St 26.3 --- 

S of Betsy Ross Bridge 25.4 0.9 

between Frankford and Girard 22.8 2.6 

N of I-676/I-95 21.3 1.5 

Walt Whitman Bridge 18.5 2.8 

Pattison Ave 17.9 0.6 

between S. Darren and S. 11th 17.6 0.3 

Broad St 16.7 0.9 

Enterprise Ave. 14.5 2.3 

Island Ave 13.3 1.2 

Table 28. Truck Transponder Sample Size, I-95, Philadelphia 

Date 

Hours of 

Closure 

Examined 

Length of 

Section 

Examineda

Truck 

Transponder 

Observations 

Observations per Mile 

per Hour 

Average Maximum 

Aug. 18, 2008 9 hr 20 mi 28 0.2 2 

Aug. 19, 2008 9 hr 20 mi 47 0.3 5 

Aug. 20, 2008 9 hr 20 mi 41 0.2 2 

Aug. 21, 2008 9 hr 20 mi 46 0.3 3 

4-Day Average 9 hr 20 mi 40.5 0.2 5 

a 10 miles in each direction 

RESULTS 

Exposure Measures 

As shown in Figure 12, the field crew documentation tables indicated that work actually 

occurred on slightly more than 40 percent of the nights during the evaluation period. Considering 

only Sunday through Thursday nights when the contractor was actually allowed to be on I-95 

and close lanes, work activity occurred on slightly more than 55 percent of the allowable nights. 

Inclement weather did likely cause the contractor not to work on some of the nights; these were 

not identified as part of pilot test (but would need to be if an agency were relying on such a 

measure to gauge the actual level of contractor effort on a project). In addition, the Philadelphia 

Phillies major league baseball team participated in post-season play in the fall of 2008. It is 

possible that the contractor was restricted from working on nights when the team was in town at 

the nearby ballpark. 
62 

Figure 12. Work Exposure Measures, I-95 Philadelphia. 

Table 29 summarizes the hours of work activity reported on the field crew data collection forms. 

Over a total of 30 nights of activity in the pilot test evaluation period, the contractor performed 

252 hours of work, averaging approximately 8.4 hours per work shift. 

 

Table 29. Hours of Work Activity, I-95 Philadelphia 

Work Exposure Measure Value 

 Total hours of work activity during project 

 Average # hours of work per day 

252 

8.4 

The next facet of exposure data gathered and computed for the Pennsylvania project were the 

roadway capacity losses. These are provided in Table 30. Overall, the majority of lane closure 

time involved the closure of two out of four available lanes. The maximum number of lanes 

present within the limits of the work zone each night was used as the baseline, rather than 

attempting to characterize each auxiliary lane or lane drop segment separately. Over the 30 

nights of work in the evaluation period, a total of 1,980 lane-mile-hours of roadway capacity loss 

were required for the project. This equates to an average of 2.9 miles of lane closure each night 

of work activity. 

41.1%

55.6%

0% 50% 100%

Percent of all nights worked

Percent of allowable nights

worked
63 

Table 30. Roadway Capacity Loss Measures, I-95 Philadelphia 

Capacity Loss Exposure Measure Value 

Percent of work hours involving: 

 1 of 4 lanes closed in a given direction 

 2 of 4 lanes closed in a given direction 

 3 of 4 lanes closed in a given direction 

 

Percent of inactive hours involving lane closures 

Lane-Mile-Hours of closures in evaluation period 

Average lane closure length per work activity period 

2.6% 

93.8% 

3.6% 

0% 

1,980 

2.9 miles 

The vehicular exposure to the work zone lane closures during the evaluation period are provided 

in Table 31. Slightly less than 500,000 vehicles passed through the nighttime temporary lane 

closures during the evaluation period. Overall, this corresponds to about 6 percent of the total 

amount of traffic that utilized this section of I-95 during the evaluation period. Meanwhile, 

nearly 1.5 million vehicle miles of travel occurred through the temporary lane closures over the 

duration of the evaluation period of this project. 

 

Table 31. Vehicle Exposure Measures, I-95 Philadelphia 

Capacity Loss Exposure Measure Value 

Number of vehicles passing through active lane 

closures in evaluation period 

Percent of total traffic in evaluation period 

encountering work activity and lane closures 

Total vehicle-miles-traveled past active work zone lane 

closures in evaluation period 

493,000 

6.2% 

1,430,000 veh-mi 

Queuing Measures 

Figure 13 and Table 32 both summarize queue measure characteristics at this site as documented 

by field personnel on the data collection form, and through analysis of available traffic sensor 

data from the regional TMC. Because work activity typically occurred simultaneously in both 

directions of travel each night, separate analyses were performed for each direction. The 

restriction of temporary lane closures to nighttime hours no doubt did reduce the frequency and 

severity of queues and congestion that might have otherwise developed. As noted in Table 32, 

field crews still reported the creation of queues on 77 percent of the nights when a lane closure 
64 

was implemented in the northbound direction, and on 100 percent of the nights with a lane 

closure in the southbound direction. For this pilot test location, the traffic sensor data at this site 

tracked fairly closely to the field crew data for these particular performance measures. 

Figure 13. Comparison of Queue Length Measures, I-95 Philadelphia. 

As Table 32 also illustrates, analyses of the traffic sensor data from this site found evidence of 

queuing on 78 percent of the nights of work activity and lane closures in the northbound 

direction, and 73 percent of the nights in the southbound direction. Even though the distance 

between sensors was quite large in some locations in the study section, the sensors happened to 

be in good locations on most nights to detect the development and duration of queues. The 

traffic sensor data did not align quite as well with the field crew data when it came to queue 

lengths (as shown in Figure 13), or the amount of time that the queues exceeded the 1-mile and 

2-mile thresholds. 

2.2

0.6

2.9

2.4 2.2

2.7 2.6

0.9

2.6

1.6

2.7

5.9

0

2

4

6

8

Average Queue Duration,

hrs

Average Queue Length,

mi

Max Queue Length, mi

SB Field Crew Data SB Traffic Sensor Data

NB Field Crew Data NB Traffic Sensor Data
65

Table 32. Comparison of Queue Measures, I-95 Philadelphia 

Queue Measure 

Southbound N

Based on Field 

Crew Data 

Based on 

Traffic Sensor 

Data 

Based on Fi

Crew Dat

Days of work activity when queuing occurred Amount of work activity time when queue > 1 mi Amount of work activity time when queue > 2 mi Amount of traffic volume through active work zone that encounters a queue 

100.0% 

5.5% 0.2% 

53.8% 

72.7% 14.5% 14.8% 35.2% 

76.9% 14.7% 

3.2% 

52.6% 
66 

Floating-vehicle travel time studies were performed during September 8-12, 2008 in order to 

provide a means of comparing the two data collection approaches to a “ground-truth” measure of 

queues and delays. No slowdowns or queues were detected on one night of data collection and 

the contractor was rained out on two other nights, so only two nights of work activity could be 

compared. For some reason, the lane closures that week were actually not documented in the 

field data collection forms, even though there were nights in which the lane closures apparently 

resulted in small queues at the sites. Therefore, Table 33 summarizes the comparison between 

traffic sensor and ground-truth queue measures from the travel time studies. The traffic sensor 

data was relatively accurate in identifying the duration of queuing that occurred on both nights, 

but tended to estimated longer maximum queues. 

 

Table 33. Comparison of Queue Conditions During Travel Time Studies, I-95 Philadelphia 

September 8, 2008 

Southbound 

September 10, 2008 

Southbound 

Based on 

Traffic 

Sensor Data 

Measured 

via TT 

Studies 

Based on 

Traffic 

Sensor Data 

Measured 

via TT 

Studies 

Queue Duration (Hrs) 

Maximum Queue Length (Mi) 

2.3 

2.1 

2.0 

0.4 

1.5 

2.1 

1.5 

0.9 

 TT= travel time 

Once again, the small sample size of truck transponder data did not allow for a comprehensive 

comparison to field personnel or traffic sensor data. A lack of speeds in successive mile 

segments during the same or successive hours of work activity limited the analysis to simply 

identifying individual speeds that were below what was considered to be uncongested. Those 

speeds were then assumed to be indicative of the presence of a traffic queue in the mile segment 

in which they occurred during that one-hour period. Unfortunately, even that simplistic 

assumption did not appear to correlate well with the other data sources. For example, Table 34 

identifies those mile point segments and hours each night where truck transponder speeds were 

below 35 mph, and compares those hours and mile points to queues reported by the field 

personnel on the data collection forms. On two nights, lower truck speeds did correspond to 

periods of work activity when field personnel reported queuing occurring in the vicinity of the 

lane closure. On the remaining nights, field personnel did not report any queues, even though a 

few very slow truck speeds were recorded. Therefore, it is difficult to know with any certainty 

whether the truck speeds were indicative of work zone impacts, or the result of other behaviors 

by the truck drivers such as pulling off the roadway to refuel or to get some sleep. 
67 

Table 34. Truck Transponder Queue Indicators, I-95, Philadelphia 

Date and Direction Time Location Speed, mph 

(# in sample) 

Queue 

Documented by 

Field Crew? 

August 19 

(Southbound) 3 am MP 13 22 (1) No 

August 20 

(Southbound) 11 pm MP 4 3 (1) Yes 

August 21 

(Northbound) 9 pm MP 9 12.7 (3) Yes 

August 21 

(Southbound) 9 pm MP 7 5 (1) Noa

August 22 

(Northbound) 

12 am 

1 am 

2 am 

MP 8 

MP 10 

MP 9 

2 (1) 

4 (1) 

8 (1) 

Nob

August 22 

(Southbound) 

1 am 

2 am 

MP 7,8 

MP 7,8 

3.5 (2) 

21.5 (2) Nob

a

 Queue was reported, but not beginning until 10:30 pm 

b

 Queue reportedly dissipated by midnight 

Delay Measures 

Table 35 presents a comparison of the delays computed using the field personnel estimates of 

queuing and the computational procedures for estimating delays based on queue lengths, to delay 

estimates computed from the available traffic sensor data. On one hand, the total vehicle-hours 

of delay computed using the project field crew measurements bracket the estimates derived from 

the traffic sensor data. On the other hand, this result is due mainly to the extremely large range 

of values documented in Table 35 for the delay computations based on the field crew estimates. 

As has been the case in each of the pilot tests discussed, a lack of information existed on whether 

the queue documented in the data collection form was located within the limits of the work zone 

(and thus operating at speeds closer to the speed of capacity flow on the facility) or was located 

at the lane closure taper and thus operating at a much lower stop-and-go speed over the length of 

the queue. The wide range in assumed operating speeds within the queue in turn led to highly 

different estimates of the individual motorist delay that is experienced, and thus much different 

computational results with respect to the total delays generated by the work activity during the 

evaluation period. Depending on which assumption is more correct, motorists who encounter a 

queue at the work zone may be delayed less than a minute, or as much as 30 minutes. 
68

Table 35. Comparison of Delay Measures, I-95 Philadelphia 

Delay Measure 

Southbound Nort

Based on Field 

Crew Dataa

Based on Traffic 

Sensor Data 

Based on Field 

Crew Dataa

Total delay during work activities Delay per night of work Delay per night of work when queues  developed Average delay per entering vehicle during work\ activities Average delay per queued vehicle during work activities Maximum delay per queued vehicle during work  activities 

2,020-23,313 veh-hr 

72-831 veh-hr 

72-831veh-hrs 

0.3-3.8 min/veh 0.6-7.1 min/veh

2.9-29.8 min/veh 

5,152 veh-hr 

184 veh-hr 253 veh-hr 

0.8 min/veh 

2.3 min/veh 

8.5 min/veh 

509-5,586 veh-hr 

94-367 veh-hr 

101-394 veh-hr 

0.2-2.6 min/veh 0.4-4.9 min/veh 

2.7-26.3 min/veh 

a The lower end of the range shown was computed assuming the queue was located within the work zone and moving at capacity flow 

computed assuming the queue was located upstream from the lane closure taper and moving at the computed reduced speed in queue fr
69 

If these values are compared to those obtained from the traffic sensor data, one would conclude 

that the queues tended to be located within the work zone and operating at speeds approaching 

capacity flow speeds, and not the extremely low stop-and-go speeds that characterize queues 

located at the beginning of lane closure tapers. However, examination of speeds determined 

through the ground truth travel time studies suggests that the traffic sensors yielded speeds that 

were higher than those actually occurring in the queues at this site, and so tended to 

underestimate the delays being created for motorists encountering a queue (Table 36). As noted 

previously, the limited amount of truck transponder data did not allow for an assessment of delay 

that could be compared to these other sources of data. 

Table 36. Comparison of Delay Conditions During Travel Time Studies, I-95 Philadelphia 

September 8, 2008 

Southbound 

September 10, 2008 

Southbound 

Based on 

Traffic 

Sensor Data 

Measured 

via TT 

Studies 

Based on 

Traffic 

Sensor Data 

Measured 

via TT 

Studies 

Average Speed in Queue (Mph) 

Average Delay per Vehicle (Min) 

20 

4.1 

7 

5.2 

27 

2.3 

12 

4.1 

KEY FINDINGS FROM I-95 PHILADELPHIAPILOT TEST 

Overall, the experiences from this pilot test mimic those of the other two pilot tests already 

described. Field personnel did appear to do a reasonably accurate job of documenting work 

activity and queue data, when such data were recorded. Unfortunately, it is less clear from this 

pilot test the degree to which queues were indeed recorded over the evaluation period. The fact 

that multiple nights of lane closure activity and queuing occurred when data collection personnel 

were at the site to collect ground truth queue and travel time data but were not recorded on the 

field crew data collection forms does raise questions about how many other nights were missed. 
70
71

9. PILOT TESTS #4 AND #5: I-15, LAS VEGAS, NV 

DESCRIPTION 

The fourth and fifth pilot test locations were located on Interstate 15 in Las Vegas, NV. The 

southern project (5.7 miles long) extended from I-215 on the south to Sahara Avenue on the 

north (Figure 14). The northern project extended from the I-15/US-93/US-95 interchange (the 

“Spaghetti Bowl” [SB] interchange) north to Craig Road (Figure 15). South of the SB 

interchange, the freeway was four lanes wide per direction, which dropped to three lanes per 

direction north of the interchange. Continued growth in the corridor and to the north created 

recurrent congestion in both project sections. South of the SB interchange, recurrent congestion 

northbound occurred in afternoon and early evening hours between Tropicana Avenue and 

Sahara Avenue (Figure 14), and occasionally in the SB interchange northward. Congestion also 

developed southbound in the morning approaching the SB interchange from the north, and 

occasionally approaching Flamingo Road. Traffic volumes throughout this section of I-15 were 

approximately 170,000 vpd. The posted speed limit in the work zones is 55 mph, reduced from 

the normal 65 mph speed limit on the facility. 

In the south project, the Nevada DOT (NDOT) is constructing an express lane in both directions 

within the existing highway median as a way to mitigate the recurrent congestion problem in this 

section. The project began on September 15, 2008 and was targeted for completion in 

September 2009. In the initial phases of the project, it was necessary to close the right lane of 

traffic in several sections to accommodate construction activities. Table 37 summarizes the dates 

and locations of these closures. Unlike the other pilot test locations, these were long-term lane 

closures that remained in place at all times until the work was completed. Acceleration and 

deceleration lanes for entrance and exit ramps within the section had to be closed as well (i.e., 

ramp traffic was funneled directly into mainline traffic). According to NDOT officials, this 

traffic control created difficulties for motorists at the merge points and was believed to have 

increased the duration and extent of congestion and queues that occurred. In early December 

2008, the contractor switched the long-term lane closures to the median of I-15. Although the 

total number of lanes remained the same through the section (3 per direction) as in the earlier 

phase, the elimination of merge point conflicts with entering traffic significantly reduced traffic 

crashes occurring regularly in that section (according to NDOT officials). It also appeared that 

this modification allowed traffic to flow more smoothly into and through the work zone, and may 

have reduced to some degree the length and duration of queues that developed. 
72

Figure 14. I-15 Express Lanes Widening (South) Project. 
73

Figure 15. I-15 Interchange and Freeway Widening (North) Project. 
74

Table 37. Location and Dates of Long-Term Lane Closures in South Project Limits 

Location of Long-Term Lane 

Closures Dates 

Northbound Right Lane: 

 Russell 

 Tropicana 

 Flamingo 

Northbound Median Lane: 

 Russell to Sahara 

September 15 – November 11, 2008 

September 22 – November 14, 2008 

September 23 – November 18, 2008 

December 1, 2008 – February 29, 2009a

Southbound Right Lane: 

 Flamingo 

 Tropicana 

 Russell 

Southbound Median Lane: 

 Russell to Sahara 

October 6, 2008 – January 22, 2009 

October 20, 2008 – December 4, 2009 

November 13, 2008 – January 23. 2009 

December 1, 2008 – February 29, 2009a

a

 End of pilot test evaluation period; lane closure extended beyond this date. 

The north project was a much larger design-build effort that will ultimately widen the freeway to 

five lanes per direction. This project began in September 2007 with a target completion date of 

Fall 2010. Because it was a design-build effort, much of the early effort on the project was 

design work, and so the actual work zone did not begin until January 2008. During the early 

portion of the project, no traffic lane restrictions (other than temporary nighttime lane closures) 

were used. However, in late spring 2008, the contractor requested that one lane in each direction 

be allowed to be closed long-term between the SB interchange and Lake Mead Boulevard in 

order to accelerate the rate of construction. The request was approved, and the lanes were closed 

on July 14, 2008. This meant that, for a distance north of the SB interchange, I-15 consisted of 

two lanes per direction. 

DATA SOURCES AND ANALYSES 

The fact that these pilot test sites involved long-term lane closures in a roadway section that was 

regularly experiencing recurrent congestion complicated the computation of work zone mobilityrelated performance measures. Specifically, the presence of delays and queues on a particular 

day during the evaluation period could not be assumed to be solely the result of the work zones 

(an assumption that could be made in the previous three pilot test locations already discussed). 

Instead, it was necessary to compare conditions prior to construction to those occurring during 

construction, with the differences then assumed to be due to the projects. Obviously, this 

requires that traffic data be available prior to the start of construction of a project. Fortunately, 

the Las Vegas Freeway and Arterial System of Transportation (FAST) regional TMC had traffic 

sensors installed since 2007 on a portion of I-15 between I-215 on the south and the SB 

interchange on the north. On average, traffic sensors are located at approximate 0.3 mile 

intervals in both directions of travel on I-15 in this area. Although data were not available north 
75

of the SB interchange within the actual limits of the north project, its effect upon northbound 

traffic approaching the interchange could be measured using the FAST data. 

Another factor complicating the analyses was the proximity of the two projects themselves. 

Approximately 2.3 miles separated the north end of the south project (Sahara Avenue) and the 

south end of the north project (the SB interchange). Prior to the implementation of the long-term 

lane closures, the pockets of congestion that developed on this facility were separate (between 

Flamingo Road and Sahara Avenue, and from the SB interchange north along I-15). However, 

once the closures were installed, queue spillback northbound from the north project occasionally 

extended upstream beyond Sahara Avenue. 

Speed and volume data from the FAST center were obtained for the summer months of 2007 

(May-August), and from September and October 2008. Meanwhile, travel times between I-215 

and the SB interchange were obtained for the same summer months in 2007 and from September 

2008 through February 2009. Although the volumes, speeds, and travel times all come from the 

same basic sensor data, FAST personnel indicate that the software and database structure makes 

extraction of the speed and volume data fairly labor-intensive, whereas the travel time data can 

be directly extracted from information posted on the dynamic message signs (DMS) mounted at 

strategic locations along the freeway. Consequently, it was much easier to access the travel time 

data, and so a larger time frame could be examined with those data. 

Unfortunately, although detailed traffic sensor data were available before and during the projects, 

field-collected data regarding work activity and queuing patterns were not. The research team 

became aware of the projects after they had been initiated, and discussions with NDOT and 

regional FHWA officials indicated that it would not be feasible to get field personnel to complete 

the daily data collection forms as had been used in the previous pilot tests, and obtaining access 

to project diaries would be problematic as well. Project personnel did indicate that the 

contractors were at the projects “most” days, and could work on both weekdays and weekends. 

Given that the lane closures remained in place throughout the duration of the evaluation period 

and were the primary reasons for the increased congestion that developed, the lack of detailed 

work activity data was less important than at other pilot test locations. Work crews operated 

behind temporary concrete barrier at both projects, and so influences of work activity upon 

traffic flows were generally minimal, according to project personnel. 

Truck transponder data were requested for the segment of I-15 between Blue Diamond Road to 

the south and East Craig Road to the north, a distance of approximately 15 miles, including five 

miles north of the US 93 interchange for which FAST traffic sensor data were not available. A 

five-day dataset were provided for this segment between January 12 and January 16, 2009. 

Speed data entries from the database were aggregated hour by hour across each one-mile 

segment in both directions of travel. Unlike the other pilot test locations, researchers requested 

data for all hours of the day, since these two pilot test locations both included long-term lane 
76

closures that could affect traffic operations throughout the day. As a result, a much larger 

sample was obtained (1600 + observations). Still, the data were random with pockets of 

multiple observations occurring within a given mile segment over several hours of the day, but 

with no observations in other mile segments. In fact, most mile segments did not have any 

observations in most hours of the day (92 percent of the mile-segment-hours had no observations 

available). Table 38 summarizes the characteristics of the available truck transponder dataset. 

Overall, the sporadic nature of these data did not allow for any type of comparison to the other 

data sources at these sites. 

Finally, to assess the accuracy of the FAST data and the truck transponder data, researchers 

traveled to the project sites to collect travel time data during the week of February 9-13, 2009. 

Data were collected continuously in both directions of travel daily between 6 am and 7 pm. 

RESULTS 

Exposure Measures 

As previously noted, specific data regarding actual days and hours of work activity were not 

available for these projects. However, some data were obtained from the south project regarding 

nights when temporary lane closures installed by the contractor to park a construction vehicle 

next to the work zone to load and unload materials. During those hours, there were then two 

lanes closed to traffic (the long-term lane closure plus the nighttime temporary lane closure). 

Functionally, these temporary lane closures were quite similar to the nighttime temporary lane 

closures in other pilot test locations previously discussed. Therefore, for comparison purposes, 

the percentage of calendar nights that involved these types of temporary lane closures was 

computed for this project, and is presented in Figure 16. One sees that an additional lane was 

closed on one-third of the nights in the evaluation period. Unfortunately, similar data were not 

available for the north project. 

Table 39 presents the exposure measures pertaining to the loss of roadway lane capacity during 

the evaluation period at the two projects. For the south project, these values include the single 

lane closed long term in each direction at each location (each assumed to be approximately 1.5 

miles for the various right lane closures and 4.0 miles when the median lane was closed), and an 

additional 1.5-mile (estimated) lane closure on those nights when a lane was temporarily closed. 

Assuming that the temporary nighttime lane closures typically lasted 9 hours per work shift as 

was the case in the previous pilot test locations, a second lane was temporarily closed 3.3 percent 

of the time during the evaluation period. For the north project, a single lane in each direction 

was closed for 1.8 of the 5.6 miles of the project during the entire evaluation period. One could 

argue that the nighttime lane closures at the south project could also be represented in terms of 1 

of 3 lanes closed, since there was already a long-term lane closure in place. However, it is 

shown as the second of four lanes closed in the table. 
77

Table 38. Truck Transponder Speed Sample Size, I-15, Las Vegas 

Date 

Northbound Southbound 

Truck 

Transponder 

Observations 

Observations per Mile per Hour Truck 

Transponder 

Observations 

Observations p

Average Maximum Average 

Jan. 12, 2009 175 0.5 26 123 0.3 Jan. 13, 2009 185 0.5 23 176 0.5 Jan. 14, 2009 244 0.7 28 168 0.5 Jan. 15, 2009 149 0.4 21 203 0.6 Jan. 16, 2009 129 0.3 17 134 0.4 
78 

Figure 16. Percentage of Calendar Nights Involving Temporary Lane Closures at the 

South Project, I-15 Las Vegas. 

Table 39. Roadway Capacity Loss Measures, I-15, Las Vegas 

Capacity Loss Exposure Measure South Project North Project 

Percent of evaluation period involving: 

 1 of 4 lanes closed in a given direction 

 2 of 4 lanes closed in a given direction 

 1 of 3 lanes closed in a given direction 

Percent of inactive hours involving lane closures 

Average lane-mile-hours of closures per month of 

evaluation 

Average lane closure length during evaluation period 

96.7% 

3.3% 

100% 

5,907 

2.2 mi 

100% 

100% 

1,296 

1.8 mi 

Since a lane was closed long term in each direction at both projects, all of the inactivity hours 

during the evaluation period involved a lane closure. In terms of amount of roadway space and 

time, the project resulted in slightly more than 5,900 lane-mile-hours of losses per month during 

the evaluation period at the south project, and 1,296 lane-mile-hours of losses per month at the 

north project. The multiple segments of long-term and nighttime temporary lane closures at the 

south project resulted in approximately 70 percent of the VMT through the project limits being 

exposed to lane closures (Table 40). In contrast, 32.1 percent of the VMT passing through the 

north project was adjacent to long-term lane closures. 

33.0%

18.7%

22.0%

0% 50% 100%

Both Directions Combined

Southbound

Northbound
79 

Table 40. Vehicle Exposure Measures, I-15, Las Vegas 

Vehicle Exposure Measure South Project North Project 

Number of vehicles encountering long-term lane 

closures per day during evaluation period 

 

Percent of vehicles in evaluation period encountering 

nighttime work activity and short-term lane closures 

Total vehicle-miles-traveled through project limits per 

day 

Percent of project VMT past long-term lane closures 

during evaluation period 

Percent of project VMT past short-term nighttime lane 

closures during evaluation period 

145,000 

4.5%. 

940,500 

70.3% 

0.6% 

185,000a

n/a 

924,000 

32.1% 

n/a 

a Traffic volumes just south of the SB interchange. Volumes north of the interchange are 

unknown, but likely are significantly lower than this value. 

n/a = data not available 

Queuing Measures 

As noted previously, computation of queuing performance measures was more difficult for these 

projects than for the previous pilot test locations. Specifically, existing congestion patterns and 

the proximity of the two projects hampered efforts to isolate the effects of each project upon 

traffic mobility. This can be demonstrated visually through a series of contour plots of speeds 

over time along the I-15 corridor, as shown in Figure 17 for the northbound direction of travel. 

Average conditions in the summer of 2007 (when no long term lane closures were present at 

either project) are provided in part (a) of Figure 17. In part (b), the speeds on I-15 northbound in 

early September are shown, when the north project had its permanent lane closures in place but 

before the south project had begun. Finally, in part (c) of the figure, average speeds during the 

last part of September and all of October are presented, when both the north and the south 

projects had long-term lane closures in place. In the before condition, a clear congestion pattern 

is evident from about Sahara Avenue extending upstream, initiating slightly after noon each day 

until approximately 5 pm or so. Around 3 pm or so each day, this region of slower speeds 

extended from just north of the I-215 interchange (approximately Russell Boulevard) to Sahara 

Avenue. Farther north of Sahara, speeds were slightly lower (40 to 60 mph) than free-flow, but 

not congested. 
80 

(a) Speed contour map summer 2007 (before north and south project lane closures) 

(b) Speed contour map in early September 2008 (north project lane closures only) 

I‐215

Trop

Sahara

SB

Mid 6 am Noon 6 pm

60‐80

40‐60

20‐40

0‐20

Speed

Range, mph

Direction of Travel →

I‐215

Trop

Sahara

SB

Mid 6 am Noon 6 pm

60‐80

40‐60

20‐40

0‐20

Speed

Range, mph

Direction of Travel
81 

(c) Speed contour map in late September and October 2008 (north and south project lane 

closures) 

Figure 17. Changes in Average Speeds Northbound Before and During Evaluation Period, 

I-15 Las Vegas. 

In contrast, the speed contour plot in early September demonstrates a clear development of 

congestion and queuing from the SB interchange. More importantly, this congestion extends 

beyond Sahara Avenue, which is the northern limit of the south project and is where congestion 

ended northbound back in 2007. Once the south project began in mid-September, one then sees 

a second region of slower speeds develop around Tropicana Avenue. The new region of queuing 

began in the AM peak and generally extended upstream into the I-215 interchange during the 

AM peak and again in the early afternoon hours. Unfortunately, sensors were not available 

farther upstream on I-15 to assess how much beyond the I-215 interchange congestion actually 

extended. 

The interdependence between projects is less obvious for the southbound direction, since FAST 

data were only available south of the north project. Still, one sees different congestion patterns 

in each of the three conditions (before, north project lane closures only, both south and north 

project lane closures). In the before condition (Figure 18 (a)), a few hours of congestion are 

evident just downstream of the SB interchange in the AM peak, and in the late evening/early 

morning hours from around Flamingo Road to near the I-215 interchange. However, in 

September 2008 when only the north project lane closures are in place, the late evening 

congestion at Flamingo Road is no longer evident. Whether this change is due to the north 

project lane closures acting as a mainlane “meter,” due to some diversion away from I-15 to 

local routes, or a general reduction in travel volumes as part of the economic recession that 

began in late 2007 cannot be determined strictly with these speed contour plots (although some 

I‐215

Trop

Sahara

SB

Mid 6 am Noon 6 pm

60‐80

40‐60

20‐40

0‐20

Speed Range,

mph

Direction of Travel →
82 

additional insights can be gained by examining the traffic volume data as is done later in this 

chapter). However, in October 2008, the introduction of long-term lane closures southbound in 

the south project at Flamingo Road (Figure 18(c)) appears to result in a significant slowdown 

most of the daytime hours (instead of occurring at night as in the before condition). Also, this 

daytime congestion appears to merge with a slowdown past the SB interchange around 5 pm 

each weekday. 

(a) Speed contour map before 

SB

Sahara

Trop

I‐215

Mid 6 am Noon 6 pm

60‐80

40‐60

20‐40

0‐20

Direction of Travel →

Speed

Range, mph
83 

(b) Speed countour map in early September 2008 (north project lane closures only) 

(c) Speed contour map in late September and October 2008 (north and south project lane 

closures) 

Figure 18. Changes in Average Speeds Southbound Before and During Evaluation Period, 

I-15 Las Vegas. 

SB

Sahara

Trop

I‐215

Mid 6 am Noon 6 pm

60‐80

40‐60

20‐40

0‐20

Direction of Travel →

Speed

Range, mph

SB

Sahara

Trop

I‐215

Mid 6 am Noon 6 pm

60‐80

40‐60

20‐40

0‐20

Direction of Travel →

Speed

Range, mph
84 

Overall, then, it is evident that the two projects did result in mobility impacts along I-15. 

However, exactly how these impacts should be calculated, and then assigned to each project, is 

not clear. One could decide that the two distinct congestion regions shown for the northbound 

direction in Figure 17 (c) could be allocated separately to the two projects (i.e., the congestion 

from before Sahara Avenue to the SB interchange allocated to the north project; the congestion 

from I-215 to about Tropicana Avenue allocated to the south project). However, this would 

mean that a portion of the congestion that existed within the limits of the south project in the 

before condition would be compared against the north project impacts, rather than those from the 

south project. In addition, this would entail the division of queues on those days when the queue 

lengths were longer than average and extended farther down in the south section (for northbound 

traffic). One could also consider all of the congestion that exists south of Sahara Avenue as part 

of the south project in both the before and during conditions, and just associate the congestion 

that developed north of Sahara Avenue as part of the north project impacts. In this approach, the 

length of queuing and delays for the north project northbound would again be truncated, this time 

to the Sahara Avenue/SB interchange segment. These two approaches would also require the 

analyst to decide whether to use the summer 2007 congestion patterns, or those in early 

September 2008 for comparison against the conditions during the south project. As a final 

option, the entire length of I-15 between I-215 and the SB interchange could be considered as a 

single segment, and the impacts discussed as the combination of effects of the two projects. 

For illustrative purposes, queuing measures for a separate analysis of each project (with the 

limits of impacts for each project separated at Sahara Avenue) and for a combined analysis of the 

entire I-15 segment were computed and are compared in Figure 19 through Figure 21. Queues 

were defined as when and where speeds dropped to below 35 mph. In Figure 19, the average 

number of hours of congestion per day is shown for summer 2007 before either project began, in 

early September 2008 when only the north project lane closures were present, and in late 

September-October 2008 when both the north and the south projects had long-term lane closures 

in place. Considered in isolation, one sees that the north project lane closures present in early 

September 2008 had little effect on northbound congestion in the I-215 to Sahara Avenue 

subsection, but did in the Sahara Avenue to SB interchange subsection. Once the south project 

lane closures were in place northbound in late September and October 2008, the duration of 

congestion increased significantly in the south project subsection, but the subsection between 

Sahara Avenue and the SB interchange remained fairly consistent in terms of the hours of 

congestion experienced each day. 
85 

(a) Northbound 

(b) Southbound 

Figure 19. Effects of the Pilot Test Projects on Hours of Congestion, I-15 Las Vegas. 

In the southbound direction, the introduction of a lane closure within the north project would not 

be expected to have a dramatic affect on congestion downstream of the project (i.e., south of the 

SB interchange). However, one does see a two-hour increase (from 3.0 hours to 5.2 hours) in 

congestion duration in the section from Sahara Avenue to I-215 in September 2008 when the 

north project lane closures were in place but not the south project lane closures. The reasons for 

4.0

0.0

4.0

4.6 4.5

5.2

11.7

4.9

11.9

0

2

4

6

8

10

12

14

I‐215 to Sahara Sahara to SB I‐215 to SB

Combined

Average Hours of Congestion per Day

Summer 2007

Early Sep 2008

Late Sep‐Oct 2008

3.0 3.0

6.0

2.6

5.2

6.4

4.0

10.6 10.8

0

2

4

6

8

10

12

14

SB to Sahara Sahara to I‐215 SB to I‐215

Combined

Average Hours of Congestion per Day

Summer 2007

Sep 2008

Oct 2008
86 

the longer duration of congestion in that subsection could not be determined from the available 

data and documentation. Interestingly, though, the entire I-15 section from the SB interchange to 

I-215 did not experience much of a difference initially in the duration of congestion in the 

southbound direction when the north project lane closure is in place. Rather, the summer 2007 

and September 2008 average hours of congestion are almost identical (6 versus 6.4 hours per 

day). It is only when the south project lane closures were installed southbound in October 2008 

that the duration of congestion increased to more than 10 hours per day. 

The interaction between projects is even more evident when one considers their impacts upon 

queue lengths. Figure 20 presents the average queue lengths each day (when queues were 

present) for the three time periods of interest in each direction. Northbound, one sees that 

average queue length in the I-215 to Sahara Avenue section actually declined slightly in both the 

early September 2008 and the late September-October 2008 periods, but increased in each of the 

periods in the Sahara Avenue to SB interchange section (by 0.5 miles in the early September 

2008 period, by an additional 0.7 miles in the late September-October 2008 period). Looking at 

the section in its entirety, average queue lengths increased by 0.4 miles in early September 2008, 

and essentially remained that way into late September-October 2008. 

Southbound, the effects of the projects on average queue lengths is more consistent with 

expectations in each subsection, and overall. Relative to summer 2007, average queue lengths 

southbound were 0.3 miles longer in the Sahara Avenue to I-215 subsection, and only 0.1 miles 

longer in the SB interchange to Sahara Avenue subsection. The slightly longer queues in the 

south subsection could again be due to possible rubbernecking of northbound lane closure 

conditions by southbound traffic. It is also important to note that while both subsections 

experienced small queue length increases in this particular period, the effect upon the total SB 

interchange to I-215 section was only 0.2 miles (from 0.5 to 0.7 miles), less than the sum of the 

increases of the two subsections. Once the southbound lane closures in the south project were in 

place (October 2008), average queue lengths grew by an additional 0.2 miles in the southern 

subsection, but by 0.6 miles (from 0.5 to 1.1 miles) in the SB interchange to Sahara Avenue 

section. Overall, traffic traveling the entire SB interchange to I-215 section experienced an 

average 0.9 mile increase in queue lengths (from 0.7 to 1.6 miles) when the south project lane 

closures were put in place, an amount that is actually greater than the sum of the increases in the 

two subsections. 
87 

(a) Northbound 

(b) Southbound 

Figure 20. Effect of the Pilot Test Projects on Average Queue Length, I-15 Las Vegas. 

Figure 21 presents the maximum daily queue lengths estimated during each analysis period. The 

summer 2007 values are actually a maximum length based on speeds averaged over one summer 

month (daily values were not available from that time period), whereas those for September and 

October 2008 are the actual daily maximum lengths. Consequently, the summer 2007 values 

would be expected to be substantially lower than the September and October 2008 values (which 

they are). Although direct comparisons to the summer 2007 values are not appropriate, the 

2.0

0.0

2.0

1.5

0.5

2.4

1.8

1.2

2.3

0.0

0.5

1.0

1.5

2.0

2.5

3.0

I‐215 to Sahara Sahara to SB I‐215 to SB

Combined

Average Queue Length, mi

Summer 2007

Early Sep 2008

Late Sep‐Oct 2008

0.3

0.6 0.5 0.6 0.7 0.7 0.8

1.3

1.6

0.0

0.5

1.0

1.5

2.0

2.5

3.0

SB to Sahara Sahara to I‐215 SB to I‐215

Combined

Average Queue Length, mi

Summer 2007

Sep 2008

Oct 2008
88 

relative trends are clear and consistent. Northbound, one sees higher maximum queue lengths in 

both the early September 2008 and the late September-October 2008 periods, with those in the 

September-October 2008 the highest for both subsections and for the entire I-215 to SB 

interchange section. 

(a) Northbound 

(b) Southbound 

Figure 21. Effects of Pilot Test Projects on Maximum Queue Lengths, I-15 Las Vegas. 

3.3

0.0

3.3

4.5

1.6

5.4 5.4

2.0

6.4

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

I‐215 to Sahara Sahara to SB I‐215 to SB

Combined

Maximum Queue Length, mi

Summer 2007

Early Sep 2008

Late Sep‐Oct 2008

0.6

1.1 1.1

1.4

4.2

5.3

1.5

3.3

4.4

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

SB to Sahara Sahara to I‐215 SB to I‐215

Combined

Maximum Queue Length, mi

Summer 2007

Sep 2008

Oct 2008
89 

Southbound, the effects of the projects on maximum queue lengths are less clear. In fact, the 

value in the September 2008 period was higher than that observed in the October 2008 period for 

the SB interchange to Sahara Avenue subsection, and for the entire SB interchange to I-215 

section. 

Table 41 provides a summary of the changes in each of the queue performance measures in each 

of the subsections and the entire freeway segment when the north project lane closures were 

installed, and then when the south project lane closures were added. 

Table 41. Summary of Queue Measures, I-15 Las Vegas 

Queue Measure of Interest 

Northbound 

I-215 to Sahara Sahara to SB 

Interchange 

I-215 to SB 

Interchange 

Effect of North Project on: 

 Duration of Queues per Day 

 Average Queue Length per Day 

 Maximum Queue Length per Day 

0.6 hr ↑

0.5 mi ↓

1.2 mi ↑

4.5 hr ↑

0.5 mi ↑

1.6 mi ↑

1.2 hr ↑

0.4 mi ↑

2.1 mi ↑

Effect of South Project on: 

 Duration of Queues per Day 

 Average Queue Length per Day 

 Maximum Queue Length per Day 

7.1 hr ↑

0.3 mi ↑

0.9 mi ↑

0.4 hr ↑

0.7 mi ↑

0.4 mi ↑

6.7 hr ↑

0.1 mi ↓

1.0 mi ↑

Southbound 

SB Interchange 

to Sahara Sahara to I-215 SB Interchange to I-215 

Effect of North Project on: 

 Duration of Queues per Day 

 Average Queue Length per Day 

 Maximum Queue Length per Day 

0.3 hr ↓

0.3 mi ↑

0.8 mi ↑

2.2 hr ↑

0.1 mi ↑

3.1 mi ↑

0.4 hr ↑ 

0.2 mi ↑

4.2 mi ↑

Effect of South Project on: 

 Duration of Queues per Day 

 Average Queue Length per Day 

 Maximum Queue Length per Day 

1.4 hr ↑

0.2 mi ↑

0.1 mi ↑

5.4 hr ↑

0.6 mi ↑

0.9 mi ↓

4.4 hr ↑

0.9 mi ↑

0.9 mi ↓

Next, in Table 42, the amount of time that queues exceeded specified threshold lengths (1, 2, and 

3 miles) are presented for each of the three time periods. These measures again show the 
90 

difficulties of attempting to examine the impacts of the two projects separately, given that the 

queues extend across the two subsections. 

Table 42. Comparison of Queue Threshold Measures, I-15 Las Vegas 

Queue Measure 

Amount of Time Each Day when Queues 

Exceeded: 

1 mile 2 miles 3 miles 

Northbound: 

 I-215 to Sahara Avenue 

 Summer 2007 

 Early Sep 2008 

 Late Sep-Oct 2008 

 Sahara Avenue to SB Interchangea

 Summer 2007 

 Early Sep 2008 

 Late Sep-Oct 2008 

 I-215 to SB Interchange 

 Summer 2007 

 Early Sep 2008 

 Late Sep-Oct 2008 

14.6% 

14.6% 

37.2% 

0.0% 

13.8% 

13.6% 

14.6% 

17.1% 

38.6% 

10.4% 

5.0% 

22.5% 

0.0% 

0.0% 

0.2% 

10.4% 

14.2% 

26.4% 

6.3% 

0.8% 

7.6% 

0.0% 

0.0% 

0.0% 

6.3% 

11.3% 

16.3% 

Southbound: 

 SB Interchange to Sahara Avenuea

 Summer 2007 

 Sep 2008 

 Oct 2008 

 Sahara Avenue to I-215 

 Summer 2007 

 Sep 2008 

 Oct 2008 

 

 SB Interchange to I-215 

 Summer 2007 

 Sep 2008 

 Oct 2008 

0.0% 

1.6% 

6.0% 

2.1% 

2.5% 

23.9% 

2.1% 

4.4% 

25.9% 

0.0% 

0.0% 

0.0% 

0.0% 

0.9% 

12.3% 

0.0% 

1.1% 

13.8% 

0.0% 

0.0% 

0.0% 

0.0% 

0.4% 

5.3% 

0.0% 

0.4% 

10.5% a

 This subsection is less than 3 miles long 

Northbound, the I-215 to Sahara Avenue subsection appears to have seen a drop between 

summer 2007 and early September 2008 in the percentage of time when long (greater than 2 

miles) queues were present. This is then followed by a large increase once the lane closures in 
91 

this subsection were installed in late September-October 2008. For the Sahara Avenue to SB 

interchange subsection, the amount of time that queues were between 1 and 2 miles increased 

both in the early September 2008 and the late September-October time periods, relative to the 

summer 2007 period. Overall, the north project lane closures appears to have increased the 

percentage of time of 2 and 3 mile queues in early September, and these percentages again 

increased with the installation of additional south project lane closures in late September-October 

2008. 

Southbound, the effects are more consistent with expectations, since there is less interaction of 

impacts between the north and the south project within the limits of available traffic surveillance 

data from the SB interchange to I-215. A clear trend towards higher percentage of times each 

day with longer queues are evident in both subsections, and in the entire SB Interchange to I-215 

segment. 

Finally, the extent to which daily traffic is impacted by the presence of queues is presented in 

Figure 22. Northbound, the interaction of the two projects upon mobility impacts is again 

evident. The I-215 to Sahara subsection showed just a small (4 percent) increase in the amount 

of daily traffic that experienced congestion between summer 2007 and early September 2008, 

indicating that the north project lane closures had little effect upon this measure. Then, once the 

south project lane closures were put in place, the amount of traffic experiencing congestion more 

than doubled (from 24 percent of daily traffic in early September to nearly 60 percent in late 

September-October 2008). For the northern subsection (Sahara Avenue to the SB interchange), 

the north project lane closure effects are very evident. Between summer 2007 and early 

September 2008, this subsection of I-15 changed from having no congestion effects (zero percent 

of daily traffic experiencing congestion) to more than 24 percent of traffic experiencing queuing 

in the early September 2008 period. This percentage continued even after the south project lane 

closures were installed in late September-October 2008, which should have had some amount of 

metering effect upon northbound traffic approaching from I-215. 

Southbound, the measures in Figure 22 (b) again mostly reflect the effects of the south project 

lane closures. The north subsection (SB interchange to Sahara Avenue) actually saw the percent 

of daily traffic encountering congestion decrease slightly from 17.6 percent to 9.8 percent 

between summer 2007 and September 2008, and return to slightly higher levels (20.9 percent) 

once the southbound lane closures in the south project were installed. In the southern subsection, 

the percent of daily traffic experiencing congestion increased both in the September 2008 and 

again in the October 2008 time period. As previously discussed, it is hypothesized that 

rubbernecking of northbound lane closures and congestion in that subsection is responsible for 

the increase in September 2008, and the southbound lane closures in October 2008 then resulting 

in the additional amount of traffic exposure to queues. Looking at the entire I-215 to SB 

interchange section as a whole, one sees the effects of both the north and south projects in the 
92 

northbound direction, and two levels of effects of the south project (one due to driver 

rubbernecking and the other due to actual lane closures). 

(a) Northbound 

(b) Southbound 

Figure 22. Percent of Daily Traffic That Encounters Queuing, I-15 Las Vegas. 

20%

0%

20%

24% 24%

27%

60%

25%

61%

0%

20%

40%

60%

80%

I‐215 to Sahara Sahara to SB I‐215 to SB

Combined

Percent of Daily Traffic Queued

Summer 2007

Early Sep 2008

Late Sep‐Oct 2008

18%

8%

24%

10%

23%

30%

21%

55% 56%

0%

20%

40%

60%

80%

SB to Sahara Sahara to I‐215 SB to I‐215

Combined

Percent of Daily Traffic Queued

Summer 2007

Sep 2008

Oct 2008
93 

Delay Measures 

Separate analyses of the two subsections examined with the queuing measures could not 

performed for delays because the DMS sign travel times from the FAST system used for the 

analysis were available only for the full section length. These data were available through 

February 2009, whereas the individual sensor data (speeds, volumes) were only available 

through October 2008. Use of the DMS travel time data allowed for the analysis of the 

differences between the right lane and median lane closures (the median lane closures were 

installed in December 2008). 

Delay measures for both northbound and southbound are provided in Table 43. For purposes of 

this analysis, average speeds less than 55 mph were taken to represent delay. Northbound, 

recurrent congestion in summer 2007 (before either project began) totaled 1,030 vehicle-hours of 

delay per day. This equated to an average of less than 1 minute per vehicle using the facility 

each day, and only an average of 1.2 minutes per vehicle that encountered congestion each day. 

These values were somewhat higher in early September 2008 when the north project lane closure 

was in place, but the south project lane closures had not yet began. In this time period, a total of 

2,411 vehicle-hours of delay were incurred each day in this section of freeway. On a per vehicle 

basis, these equated to 1.6 minutes per entering vehicle and 5.9 minutes per vehicle encountering 

congestion, on average. Depending on the day, however, brief occurrences of delays reached as 

much as 16.2 minutes during this period. 

Table 43. Comparison of Delay Measures, I-15 Las Vegas 

Time Period 

Minutes of Delay 

Veh-Hrs per 

Day 

Average per 

Entering 

Vehicle 

Average per 

Queued 

Vehicle 

Maximum 

per Queued 

Vehicle 

Northbound 

 Summer 2007 

 Early Sep 2008 

 Late Sep -Nov 2008 

 Dec 2008-Feb 2009 

Southbound 

 Summer 2007 

 Sep 2008 

 Oct-Nov 2008 

 Dec 2008-Feb 2009 

0.8 

1.6 

2.3 

3.4 

0.4 

0.2 

5.4 

5.1 

1.2 

5.9 

5.0 

6.7 

0.4 

0.5 

8.1 

7.4 

n/a 

16.2 

21.2 

30.9 

n/a 

11.8 

24.2 

29.1 

1,030 

2,411 

3,417 

5,019 

536 

313 

8,621 

7,951 

The late September through November 2008 time period involved both the north project lane 

closure and the south project lane closures in the right lane and shoulder. The addition of the 

south project lane closures further increased congestion, such that 3,417 vehicle-hours of delay 
94 

per day were experienced by motorists during this period. On a per-vehicle basis, this equated to 

2.3 minutes per entering vehicle and 5.0 minutes per vehicle encountering congestion. The 

restriction of roadway capacity at various points in the south project also caused higher spikes in 

delay during the period, as the maximum delay per vehicle increased to 21.2 minutes. 

Delays northbound increased still more once the right lane and shoulder closures in the south 

project were replaced with a median lane closure in December 2008. For the December 2008 to 

February 2009 period, 5,019 vehicle-hours of delay were experienced per day by motorists, 

nearly five times that of the summer 2007 period before the work zones were in place. This 

delay equated to 3.4 minutes of delay per entering vehicle, and 6.7 minutes per vehicle 

encountering congestion on this freeway section. During this period, delays reached as much as 

30.9 minutes per vehicle on certain days and times. This was somewhat surprising, given that 

NDOT officials had commented that they believed that traffic flow had improved when the right 

lane and shoulder closures were replaced with median lane closures in the project. It is possible 

that the somewhat contrary results northbound were due to spillback effects from the north 

project that masked any benefits that the lane closure switch in the south project had upon traffic 

flow. 

As shown in Table 43, delays in the southbound direction also increased significantly as a result 

of construction activities and lane closures in the south project. Whereas delay in summer 2007 

and September 2008 averaged between 313 and 536 vehicle-hours per day, it grew to 8,621 

vehicle-hours per day in October-November 2008, more than a 15-fold increase. Average delays 

per entering vehicle also increased significantly from less than a minute to more than 5 minutes 

per vehicle, and the average delay per queued vehicle grew to 8 minutes per vehicle. Maximum 

delays also doubled once the lane closures were installed, from 11.8 minutes per queued vehicle 

in September 2008 to 29.1 minutes in the December 2008 to February 2009 time period. 

It is important to note that delays during the south project lane closures actually did decrease 

slightly (by about 8 percent) in the December 2008-February 2009 time frame when the median 

lane was closed as compared to the October-November 2008 time frame when the right lane and 

shoulder was closed. Although such an improvement was not evident in the northbound delay 

data, delays southbound did suggest that conditions were improved slightly by the lane closure 

switch to the median. These results further suggest that the effects of the two projects on 

northbound traffic are intertwined, making it difficult to isolate the effects of one project or the 

other upon overall traffic conditions. 

Finally, as an accuracy check, the travel times extracted from the DMS FAST database were 

compared to field-collected travel times gathered during the second week of February 2009. 

Table 44 summarizes the results of that comparison. Overall, the travel times estimated from the 

FAST system replicated those obtained in the field fairly well. On average, the FAST travel 

times averaged about 1 to 2 minutes (10 to 13 percent) longer over the 7-mile section than the 
95 

field measured values. Meanwhile, Pearson Correlation Coefficients were very high, ranging 

between 0.790 and 0.913. 

Table 44. Travel Time Data Comparison, I-15, Las Vegas 

Northbound Southbound 

Average Travel Time from FAST 

Average Travel Time from Field Studies 

Difference 

Z-Statistic 

Pearson Correlation Coefficient 

14.2 min 

12.3 min 

1.9 min 

6.49 

0.913 

12.1 min 

11.0 min 

1.1 min 

4.75 

0.790 

Travel Time Reliability Measure 

The final performance measure examined for these two pilot test projects was the effect of the 

work zones upon travel time reliability along this section of I-15. Reliability was not a measure 

examined at the previous pilot test project locations, as travel times in the absence of the work 

zone lane closures were very steady (there was enough excess roadway capacity available to 

maintain very stable traffic speeds on the facility). Hence, the total effect of the work zones 

upon reliability is represented by the additional delays created on those days and nights when 

lanes were closed and congestion developed due to the capacity restrictions. For the two I-15 

projects, however, some degree of travel time unreliability already existed during a portion of the 

day prior to the start of the projects. Consequently, it was necessary to measure how reliability 

was changed relative to the before condition, similar to the need to compare queues and delays 

during the projects to those that existed before the project began. 

The term “buffer index” was selected as the main reliability measure evaluated. This index 

represents the percentage increase in average travel times that a person must allocate to their trip 

in order to be sure they will arrive on time most of the time. Traditionally, “most” has been 

defined as arriving early or on time 19 out of 20 trips (i.e., 95 percent of the time). Other 

definitions are possible, however. For example, a buffer index based on the 80th-percentile trip 

time would mean that drivers arrive early or on time four out of every five trips (i.e., they would 

typically be late once every five days). 

Table 45 and Table 46 present buffer indices computed for both the 95th and 80th percentile travel 

time thresholds. Individual travel times by time period each day were not available for the 

summer 2007 time period; however, overall travel times each hour were obtained for July 2008. 

Given that the lane closures in the north project were initiated in mid-July, it was possible to 

estimate the buffer index in early July 2008 as the “before” condition. The other periods reflect 

the north project effects, the combined effects of the north and south projects with right lane and 
96 

shoulder closures, or the combined effects of the north and south projects with a median lane 

closure in place, respectively. 

From Table 45, the north project lane closures are seen as increasing the buffer index in both the 

northbound and southbound directions of travel between I-215 and the SB interchange. The 

buffer index was already rather large during midday hours northbound, and this was further 

exacerbated by the introduction of the north project lane closures. Interestingly, the additional 

lane closures installed in the south project in late September only resulted in a slight incremental 

increase in the buffer index during this period (from 70.5 to 77.3 percent, and then to 80.3 

percent when the median lane closures were installed in December 2008). Meanwhile, the 

effects of the south project lane closures was more substantial in both the peak periods, rising 

from a buffer index of 9 percent to 37.3 percent and then to 61.9 percent in the morning peak, 

and from 46.6 percent to 49.2 percent and then 63.9 percent in the evening peak. In the 

southbound direction, the north project lane closures have very little influence on travel time 

reliability. In fact, the buffer index from late July through September 2008 is actually less than 

in early July 2008 during the midday hours. Once lane closures are installed in the south project, 

significant increases in the buffer index are seen in all time periods. Interestingly, the buffer 

index is slightly higher in the midday and evening peak hours when the median lane was closed 

in the south project as compared to the right lane and shoulder closures, even though NDOT and 

FHWA reported that traffic flow was smoother in the median lane closure condition (the index 

did decrease in the morning peak period, however). 

Table 45. 95th Percentile Buffer Indices, I-15, Las Vegas 

Time Period AM Peak 

(6-9 am) 

Midday 

(9 am – 3 pm) 

PM Peak 

(3-6 pm) 

Northbound 

 Early Jul 2008 

 Late Jul-Early Sep 2008 

 Late Sep -Nov 2008 

 Dec 2008-Feb 2009 

Southbound 

 Early Jul 2008 

 Late Jul-Sep 2008 

 Oct-Nov 2008 

 Dec 2008-Feb 2009 

5.5% 

9.0% 

37.3% 

61.9% 

5.6% 

17.0% 

56.3% 

35.4% 

55.0% 

70.5% 

77.3% 

80.5% 

24.7% 

17.1% 

62.5% 

77.2% 

22.2% 

46.6% 

49.2% 

63.9% 

22.3% 

34.9% 

60.6% 

70.1% 

The trends are slightly different if the 80th percentile travel time is used as the basis for 

computing the buffer index. As depicted in Table 46, the lane closures installed on the north 

project had very little effect on the buffer indices northbound. The only significant change was 
97 

during the evening peak when the index increased from 11.5 to 22.6 percent. Even more 

surprising is the fact that the introduction of lane closures in the south project significantly 

increased the northbound buffer index in the morning peak (from 1.4 percent to 20.5 percent), 

but had little effect on the midday or evening peak indices. In fact, the midday buffer index 

actually decreased in the late September to November period, and was only slightly higher 

(relative to the late July-early September period) once the lane closures were moved to the 

median lane in December 2008. Southbound, the north project lane closures had no effect 

whatsoever on the buffer index. Only when the south project lane closures were installed in 

October 2008 does the buffer index increase substantially in all time periods. 

Table 46. 80th Percentile Buffer Indices, I-15, Las Vegas 

Time Period AM Peak 

(6-9 am) 

Midday 

(9 am – 3 pm) 

PM Peak 

(3-6 pm) 

Northbound 

 Early Jul 2008 

 Late Jul-Early Sep 2008 

 Late Sep -Nov 2008 

 Dec 2008-Feb 2009 

Southbound 

 Early Jul 2008 

 Late Jul-Sep 2008 

 Oct-Nov 2008 

 Dec 2008-Feb 2009 

1.7% 

1.4% 

20.5% 

31.9% 

2.9% 

3.2% 

24.2% 

15.0% 

28.9% 

34.4% 

32.7% 

39.0% 

5.3% 

2.0% 

31.2% 

26.9% 

11.5% 

22.6% 

25.1% 

33.1% 

5.5% 

4.3% 

37.2% 

30.0% 

KEY FINDINGS FROM THE I-15 PILOT TESTS 

The analysis of impacts from the two pilot test locations on I-15 in Las Vegas, NV proved to be a 

much more involved process, due to the fact that lane closures which significantly impact 

capacity were installed on a long-term basis in both projects. Projects which limit lane closure 

activity to off-peak hours when traffic congestion is not normally present on the roadway allow a 

reasonable assumption to be made that any impacts are due solely to the work zone lane closure. 

When long-term lane closures occur in locations where recurrent congestion is already present, 

such a simplifying assumption cannot be made. Rather, the effects of the lane closures at these I15 projects were combined with the recurrent congestion already being experienced on this 

facility before the projects began. Therefore, information about travel conditions before the 

projects began was required so that incremental changes in performance measures could be 

estimated during each project. The analysis was also complicated by the fact that both projects 

were located close enough to each other that their impacts on travel were intertwined. Overall, 
98 

however, it is apparent that the long-term lane closures at these two projects did have a 

significant effect on travel conditions within the corridor. 

Several useful insights regarding work zone performance measurement were obtained from this 

analysis. Many of them were similar to those reported for the other pilot test locations as well. 

However, one unique finding from these two projects is that a travel time reliability measure 

(buffer index) can be another way of assessing the mobility impacts of work zones in regions 

already experiencing some degree of congestion on the facility prior to initiating work activity on 

the project. The roadway capacity reductions caused by long-term work zone lane closures make 

travel times more susceptible to fluctuations in daily demand, as well as to incidents. Since the 

available capacity through the roadway segment is constrained, incidents that occur will have a 

greater affect on travel because they create an incrementally larger reduction in the available 

capacity than when all lanes were open. The end result is that the predictability of travel times 

through the work zone may become more unreliable. A travel time reliability measure can be 

used to track this variability and enable better work zone management and public information. 

The results from the I-15 sites also illustrate the value of having a functional TMC available for 

evaluating work zone mobility impacts in a more complex urban environment. The additional 

analysis that can be performed with the larger data set is evident by the range of tables and charts 

in this chapter that were not prepared for the other sites. The availability of TMC data also 

allows the work zone analyst to compute vehicle exposure much more accurately than would 

otherwise be possible. Furthermore, when recurrent congestion already occurs on a roadway 

segment prior to the initiation of the work zone, detailed traffic data that can be obtained from a 

TMC allow analysts to evaluate both spatial and temporal changes in those congestion patterns. 

In addition, the effect of work zone capacity reductions upon non-recurrent congestion effects, 

measured in terms of travel time reliability, can also be evaluated when TMC data are available. 

The results of the I-15 pilot tests illustrate the importance of selecting performance measures that 

are appropriate to the various types of projects and project conditions. For example, for projects 

like the I-15 ones where there are long-term lane closures, the value of certain exposure 

measures such as the percent of time when work activities involve lane closures has little 

meaning. Agencies should consider stratifying projects on the basis of lane closure strategy 

employed when examining overall trends across projects in a district or region over time. 
99 

10. FINDINGS AND LESSONS LEARNED 

This report has presented the results of an effort to pilot test the feasibility of several mobilitybased work zone performance measures. The measures encompassed various dimensions of: 

• Work zone exposure 

• Traffic queuing 

• Motorist delays 

• Travel time reliability 

A discussion of useful safety-related measures was also presented. However, because of 

anticipated lag times in obtaining crash data from the various pilot test locations (and the 

straightforward nature with which safety-related performance measures are typically developed), 

exposure and mobility-related performance measures only were targeted at each of the pilot test 

locations. 

The pilot test also incorporated different methods of data collection to support performance 

measurement computations. The first method was to rely on field personnel at the project to 

manually document the occurrence of traffic queues that develop because of temporary lane 

closures or other work activities that constrain roadway capacity. Use of this method assumes 

that traffic conditions in the absence of the work zone would have been uncongested, such that 

any queuing or congestion that observed could be attributed solely to the work zone. The second 

method was to use electronic traffic surveillance data to monitor traffic conditions and compute 

desired performance measures. This data may come from a regional TMC already in place, or 

from sensors specifically deployed as part of a portable work zone ITS. The third method relied 

on truck speed data collected by third-party vendors willing to provide limited access for 

transportation monitoring purposes. A screening process was undertaken to access available 

speed observations at each project on specific times and dates when traffic impacts were 

expected to exist. Organizing these data by location and time of day was expected to provide 

another method of tracking the effect of work zones on traffic mobility on a facility. 

In the following section, a synopsis of the key lessons learned with respect to data collection and 

analyses of work zone exposure and mobility data are presented. Next, the various performance 

measures are compared across projects to discuss how they might be used to assess for critiquing 

project-level decisions and actions, as well as agency policies and processes. 

DATA COLLECTION AND ANALYSIS FINDINGS AND LESSONS LEARNED 

Manual Documentation of Queues by Field Personnel 

Overall, the results of the pilot test suggest that it is possible to obtain fairly good quality queue 

data from field personnel upon which to base work zone mobility performance measure 
100 

computations. The impact of data collection demands on field personnel workload was a 

potential concern initially in this pilot test, but workload-related problems did not appear to 

materialize. Comparisons of the queue length and duration data to data from traffic surveillance 

sensors and ground-truth measurements (when available) suggest that reasonable estimates can 

be obtained. Very few instances were identified where field personnel failed to document either 

the work and lane closure activity (this type of information is normally needed for project diary 

entries anyway) or the development of queues. The fact that this was a special request as part of 

a research project may be part of the reason the effort was so successful. It is likely that regular 

reminders (possibly as a special note in the project diaries) would be beneficial to agencies 

striving to adopt this process as part of their monitoring efforts. It is also likely that an 

indication of the importance of this documentation effort by upper management in an agency 

would further ensure personnel consistency in reporting. 

These successes notwithstanding, a few key lessons were also learned through this pilot test 

effort. 

• It will be important for agencies to establish simple operational definitions for field 

personnel as to what exactly constitutes a traffic queue – In most cases, agencies will 

want to use a reasonable and consistent indicator, such as when vehicle speeds drop 

below 35 mph. Other agencies may choose to use a much lower value to define what 

constitutes a queue (some agencies use values as low as 10 mph to define queuing). 

Unfortunately, it may not be possible for field personnel to accurately gauge vehicle 

speeds and make distinctions as to whether a backup truly meets the agency definition of 

a queue. All that an agency may be able to expect is for field crews to be able to define 

when and how far upstream traffic has “backed up.” Estimates of what the speed in 

queue likely was at that work zone may then need to be computed after the fact using 

traffic flow relationships. 

• Estimates of queue lengths need to include a description of the location of the queue 

relative to the lane closure (upstream, beyond taper and into work zone, partially 

upstream and partially within the work zone, etc.) – Related to the previous bullet, the 

comparison of queues documented by field personnel and the electronic traffic 

surveillance sensor data at several sites indicated that some queues occurred at the point 

of the lane closure transition, whereas others occurred within the work zone next to the 

location of work activity or other disturbance. When queues occur within the work zone 

where the number of lanes available remains constant, traffic flow relationships suggest 

that speeds in those queues will be much higher than in queues at and upstream of a lane 

closure transition. Equation 1 identified the queue speed estimated for the lane closure 

transition queues. For queues within the work zone, the speed at capacity flow 

(approximately one-half of the free-flow speed) is a more realistic value to use. 
101 

• Manual methods will be less effective in capturing short (less than one hour) 

disturbances that result in queues – Realistically, neither manual methods or electronic 

surveillance data are likely to be all that effective in capturing short-duration queues that 

may form because of small fluctuations in traffic flow behavior, a temporary disruption in 

flow by work vehicles entering or exiting the work area, or other disturbance. Given the 

workload that field personnel typically carry during work zone activities, only the more 

significant long-lasting queues that develop are likely to be documented. 

Use of Electronic Traffic Surveillance Data 

The results of this pilot test also indicate that electronic traffic surveillance data, when available, 

can be utilized to measure work zone exposure and to compute the effects of work zones on 

traffic mobility. However, some challenges do exist in obtaining and applying these types of 

data to work zone exposure and mobility-related performance measurement. The major lessons 

learned relative to the use of electronic surveillance data are as follows: 

• Field personnel documentation of when and where lane closures were placed and 

hours of work activity will still be needed to compute mobility performance measures 

using electronic traffic surveillance data – Although it may be desirable to be able to 

rely on electronic traffic surveillance data alone to determine when lanes were closed, 

which lanes were closed, when the lane closures were removed, etc. (and thereby 

eliminate the need to extract these data from field personnel), the realities of electronic 

surveillance data is that it is often not easy to distinguish between changes in conditions 

that are due to the work zone actions listed, those which are due to incidents, and those 

due to normal “noise” in the traffic stream. 

• It is important to make sure that the traffic sensors themselves will remain operational 

when work begins – At some of the pilot test sites, it appears that power was disrupted 

temporarily to the traffic sensors when work was occurring. As a result, data were not 

available during the exact times they would be needed for monitoring and performance 

measurement computation purposes. If temporary power disruptions in the vicinity are 

anticipated, it will be necessary to take steps to fill in gaps in coverage (by manual 

documentation of conditions by field personnel or by deploying portable traffic sensors 

that can provide the data during the power outage). 

• The level of accuracy in work zone mobility performance measurement achievable with 

electronic traffic surveillance data depends heavily on the design of the system 

(particularly traffic sensor spacing) – Some of the key factors that will affect the extent 

to which accurate performance measures can be recorded and computed include 1) the 

availability of speed data from each sensor (critical to the estimation of delays and queue 

lengths), 2) the spacing between sensors (closer sensor spacing allow for finer resolution 

of queue lengths and queue length growth/dissipation over time), and 3) the limits of 
102 

surveillance relative to the length of impacts that develop (if queues grow beyond the 

limits of surveillance, the measures computed from that data may be counterintuitive to 

what is actually occurring at the site). 

• Aggregation of traffic sensor data to hourly averages is a reasonable compromise 

between accuracy and practicality for monitoring work zone mobility impacts – Data at 

this level of detail appears to be still sensitive enough to detect the onset and dissipation 

of congestion, but not so detailed so as to overburden the analyst. 

Truck Probe Spot Speed Data 

At each of the pilot test locations, analysis and comparison of truck probe speed data for work 

zone mobility performance monitoring was fairly limited because of a lack of adequate sample 

size across time and space. In some cases, work activity occurred during nighttime hours when 

traffic volumes are lower, and this contributed to the low sample size. However, even at the Las 

Vegas pilot test locations, the amount of truck transponder data obtained was not enough to 

allow a full comparison to other data sources. There were instances at the three nighttime project 

pilot test sites in which the knowledge that a work zone was present and that congestion had 

developed (via other data sources) could be correlated with a reduced speed measurement 

obtained from the truck transponder data. Unfortunately, there were more instances in which 

truck speed data were not available at the location and time that work zone queuing was known 

to have developed. Consequently, its value as a primary data source for mobility monitoring and 

performance measurement is currently rather limited. 

The limitations of this current pilot test notwithstanding, the potential use of vehicle probe speed 

data for work zone performance measurement is still attractive to highway agencies. As private 

sector vendors of speed and travel time data continue to evolve, an adequate supply of this type 

of data may someday become available. Although there were few actual lessons learned through 

the review of truck transponder speed data in this pilot test effort, the following are three issues 

to be considered in future efforts to use this type of data: 

• The choice of segment length must balance the trade-offs between amount of data that 

is available and level of accuracy of queue and delay estimates – For this pilot test, the 

decision was made to use one-mile segments and one-hour time periods in the analysis. 

It was found that the amount of data available was insufficient to support the analysis at 

this level of detail. Unfortunately, even a longer segment length would not have proven 

beneficial for the pilot test (researchers did examine the possibility of combining mile 

segments each hour, without much improvement in coverage). For other vehicle probe 

data sources, the goal should be to dissect the affected roadway segment into intervals 

that allow for multiple readings (at least three would be preferable in order to establish 

the degree of variability of each segment estimate) in each segment in each hour. If a 

reasonable speed estimate can be established in each segment each hour, the 
103 

computational procedures for determining queue lengths and delays from spot sensor data 

could be applied. 

• A common reference system must be used to define the segment endpoints and work 

zone location – In many work zones, the location of work activity and temporary lane 

closures will change from work period to work period. Documentation of where the 

work zone is located each period is essential to matching the vehicle probe speed data in 

each segment to that location so that queue lengths can be approximated. 

• The potential reaction of the vehicle fleet to the presence of a work zone and resulting 

congestion should be considered when analyzing the data – In the case of truck 

transponder data, communication between drivers of the presence of severe congestion at 

a work zone could lead to many drivers leaving the roadway to take a break and wait for 

congestion to dissipate. Although it was not possible in this pilot test to verify or refute 

whether this occurred at any of the sites, the potential does exist for such behavior to 

occur. 

• It is important to remember that speeds within each road segment are not true “spot” 

speeds – The aggregation of speeds obtained from vehicle probes will either be a 

compilation of speeds all along the length of the roadway segment, or an estimate of 

speeds using elapsed travel time on that segment or portion thereof. Significant changes 

in roadway characteristics within the segment length can cause much different speeds at 

different locations within the segment. This increased variability makes it more difficult 

to assess the impacts of a work zone, and implies that a greater sample size will be 

needed. 

Comparison across Possible Data Collection Methods 

Table 47 compares and contrasts the various data collection methods currently available for 

monitoring work zone impacts and assessing performance. The table includes point-to-point 

travel time measurement systems mentioned previously, but which were not available for 

inclusion in the pilot test effort. 
104

Table 47. Comparison of Data Collection Methods for Work Zone Monitoring and Performance M

Data Source Advantages Disadvantages Other 

Manual Measurement of Queue by Field Personnel 

- Direct control of data by agency 

- Easy to implement 

- Minimal additional cost to agency 

- Increases work load of field 

personnel (inspector, TTC supervisor, etc.) 

- Limited to locations where 

recurrent congestion not present 

- Important t

and end of zone lane c

Electronic Spot Speed Data: Existing TMC already in place 

- Minimal additional cost to agency 

- Availability of “before” data 

(allows assessment of incremental effects of work zone) 

- Location of devices may not be 

optimum for work zone assessment purposes 

- Can require significant effort to 

extract and process desired data from entire system 

- Important t

will remain work activi

Portable ITS devices (sensors on trailers, portable sensors in channelizing devices, etc.) 

- Allows for optimum placement of 

sensors for work zone monitoring purposes 

- Work zone ITS can be costly to the 

project 

- Portable de

consistently documente

- Sensors mu

limits of an

Vehicle probe data (i.e. truck transponders) 

- Does not require agency to 

purchase technology to deploy 

- Does not require technology to be 

moved or maintained 

- Sample size is an issue for truck 

transponder data 

- May require purchasing from thirdparty vendors 

- Important t

are not true distributed which they 

Electronic Point-toPoint Travel Time Data: AVL systems - Very accurate tracking of speed 

profiles possible 

- Vehicle fleets to draw data from 

are usually very limited 

- Will require agreements with 

agencies or vendors who collect these data 

-The potentiadata at a finer needed, which management 
105

Data Source Advantages Disadvantages Other 

AVI toll tags - Available sample size can be fairly 

high 

- Queues difficult to measure 

without multiple, very closely spaced sensors 

- May require agreement with toll 

agency to gather data 

- Deployment of additional 

transponder readers can be costly 

- Generally l

a significandriving poptheir vehicl

License plate recognition systems 

- Available sample size can be fairly 

high 

- Costly to implement - Sample size availability depends on 

ability of system to match license plates 

- Queues difficult to measure 

without multiple, very closely spaced sensors 

- May create 

with local c

From bluetooth readers - Data can be obtained unobtrusively 

from roadside devices 

- Requires purchase and deployment 

of Bluetooth readers 

- Queues difficult to measure 

without multiple, very closely spaced sensors 

- Dependent 

present 

 Cell phone tracking - Large potential sample size within 

traffic stream 

- Requires agreements with 3rd party 

vendors to obtain data 

- Dependent 

present 
106 

FINDINGS AND LESSONS LEARNED REGARDING PERFORMANCE MEASURE 

COMPUTATION AND INTERPRETATION 

The proposed measures themselves offer insights into their potential usefulness to highway 

agencies for both current and future project-level decision-making, and for process-level 

reviews. 

Exposure Measures 

The importance of vehicle-based measures of exposure (volumes through the work zone, 

vehicle-miles-traveled through the work zone) for computing and normalizing queue, delay, and 

crash-based performance measures was discussed earlier in this report. The importance and 

lessons learned regarding some of the additional exposure measures proposed are discussed 

below. 

• Work activity measures (percent of days worked, average hours per day of work) will be 

useful in tracking and comparing contractor level of effort – However, many factors 

affect contractor ability to work on a given project. Consequently, these data are likely to 

be most useful when averaged across multiple projects, or in some form of compliance 

assessment to a threshold target (e.g., percent of projects with less than 40 percent of 

work days with activity). These values would also be useful for monitoring efforts of 

individual contractors (again, either in terms of averages or as a percentage of projects 

with work activity percentages below a preset threshold). Finally, these measures could 

also be extrapolated across all projects in a region as a way to quantify total exposure for 

use by public information offices or other needs. 

• Capturing lane closure hours (percent of hours involving 1, 2, etc. lanes closed) is 

relevant only if total lanes remaining open is also captured – Agencies will need to 

capture and categorize these data based on lanes open/lanes closed configurations. One 

lane closed on a five-lane section is much different than on a two-lane section. Given 

how the number of lanes available for traffic can vary from location to location along a 

section of multi-lane roadway, establishing field documentation procedures for lane 

closures that require the number of lanes closed, number of lanes open, and location of 

the lane closure(s) will be important. Proper documentation of lane closure parameters 

will also facilitate collection and analysis of delay and queue measures (especially when 

relying on electronic traffic surveillance sensor data).

• It will be important to stratify projects based on the type of lane closure being utilized 

when examining trends or evaluating compliance to thresholds – For example, projects 

involving short-term lane closures each day or night should be examined differently than 

those involving long-term lane closures. Information regarding percent of lane closure 

hours with inactivity will be much more valuable for the short-term lane closure projects 

than for long-term lane closure projects, since the traffic control designer should already 
107 

have acknowledged that there will be many inactive hours with lane closures for a longterm lane closure traffic control option. While it still may be worthwhile to quantify this 

information, mixing the results with short-term lane closure projects will tend to skew the 

values and make it difficult to assess agency decisions and policies for either type of lane 

closure being used.

• Computation of the total lane-mile-hours of lane closures during the project can 

provide another way to normalize delay measures – Depending on the number of lanes 

available in a given direction of travel, contractors have options in term of how many 

lanes are closed for a given work shift. However, the number of lanes closed at a time 

also affects how many work shifts are needed to complete a task. Normalizing vehiclehours of delay on a per-lane-mile-hour of closure basis can help connect traffic impacts to 

both lane closure and work productivity measures, if measured across the total duration 

of the work task being completed. 

Queue Measures 

The argument was made earlier in this report that queuing measures themselves were valuable 

for work zone mobility monitoring and impact assessment. Furthermore, an approach was 

proposed for estimating delay impacts from queue length and duration documentation by field 

personnel during work zone activities. The pilot test results did indicate the need to include 

additional details during queue length documentation to improve delay estimates. Additional 

insights and lessons learned regarding queue measures are provided below. 

• The percent of time when queues exceed selected threshold values should be useful as 

a potential performance specification for work zone traffic control and impact 

mitigation – The results of the pilot tests illustrate that unexpected queues can occur from 

time to time due to fluctuations in traffic flows and small, temporary disturbances to that 

flow near areas of work activity. These can occur even if analysis during traffic control 

plan design indicates that traffic demands do not exceed the expected traffic capacity 

through the work zone (although the likelihood increases the closer demand is to 

expected capacity value). It appears that many of these queues are fairly short in duration 

and length. This performance measure provides a simple way to distinguish between 

those occasional short-duration queues that are out of the control of the 

contractor/highway agency, and those that are systemic in exceeding agency threshold 

targets. 

• The measure “percent of traffic encountering a traffic queue” provides a direct 

indication of the breadth of impacts of the work zone to the motoring public – 

However, it may be challenging for agencies to use this measure where recurrent 

congestion is present prior to the start of the project, and where long-term lane closures 

have been deployed. For these situations, it will be necessary to first compute this 
108 

measure for the before-project condition, and then measure the change in the measure 

once the work zone has been installed. 

• The average duration of queue per work shift or day provides valuable information at 

projects where recurrent congestion was already present prior to the start of the work 

zone, or where electronic surveillance may not cover the entire length of work zone 

impacts on a roadway – The measure also provides useful insights about scheduling 

decisions for short-term lane closures each work period. 

• The average queue length and maximum queue length measures have value for 

assessing both project-level and process-level traffic management decisions – As stated 

earlier in this report, the maximum queue length is a useful measure for evaluating how 

well planning and impact analysis tools and procedures worked for the project, and to 

assess whether advance warning signs and other traffic control provided adequate 

warning for the project. In addition, it is expected that tracking these measures across 

multiple projects in a region (appropriately categorized by roadway type) to determine 

whether policies and procedures should be modified in terms of capacity values assumed, 

analysis tools and procedures allowed, impact mitigation strategy emphases, etc. 

Delay Measures 

Vehicle-hours of delay are generally the most significant contributor to the additional road user 

costs created by work zone activities, and so can have significant implications in terms of 

contracting strategies used and penalties and incentives incorporated into contract language. 

Relating these total costs in terms of impacts to individual motorists is also important from a 

customer service perspective that many agencies emphasize. Since queues and delays are 

correlated, similar trends and insights can be gained through examination of both types of 

measures. The delay measures pilot tested in this effort do appear to provide information that 

could be valuable to a highway agency or a contractor, except for the average delay per entering 

vehicle measure. Initially, this measure was proposed as a way to account for the fact that a 

work zone can affect some motorists very significantly during those times when congestion and 

queues are present (and be perceived somewhat negatively by those motorists), but have no 

effect and so be perceived much more positively by motorists during other times of the work 

activity when queues have not developed. In reality, attempting to average the impacts across all 

vehicles only appears to mute the overall effect of the project and give a somewhat unrealistic 

perception that the impacts to motorists were not all that significant. The average delay per 

queued vehicle, along with the percent of traffic encountering a queue, appears to be a more 

straightforward way to account for both the intensity and breadth of work zone impacts on 

motorists. 
109 

Travel Time Reliability Measure 

The use of the buffer index as a travel time reliability measure for work zone performance 

monitoring was demonstrated for one of the pilot test project locations. For projects where 

recurrent congestion already exists and where reliability is already affected, computation of 

buffer indices and comparison to the pre-work zone condition appears to offer useful insights 

into another dimension of user impacts due to the work zone. Two different reliability thresholds 

were computed (95th percentile and 80th percentile travel times) for the pilot test, and both 

indicated impacts due to the work zone. However, the magnitude of the impacts differed. Given 

that travel time reliability measurement research in general has not provided recommendations 

about appropriate thresholds to use, it would seem appropriate to compute and monitor both as 

part of work zone mobility performance measurement at the present time. 

Safety Measures 

Although data were not available to allow computation of safety performance measures for the 

pilot test locations, the process by which such measures are computed is fairly straightforward. 

Traditionally, agencies do (eventually) have access to crash data through their statewide crash 

records system. What is usually missing is the exposure data that is needed to convert the crash 

data to meaningful rates that can be compared to pre-work zone conditions or across projects, 

regions, or the state. In addition, the crash data can also be combined with work activity 

exposure data to assess the impacts of the project or project task upon overall crash numbers or 

crash costs. 

It is also important to utilize and interpret the safety measures (as well as the exposure and 

mobility-related measures) relative to the goals of a particular project when making decisions 

regarding the effectiveness of a particular approach used to complete the work. For example, 

performing a particular task using only nighttime lane closures may result in a higher crash rate 

(or increase in crash rate) on per vehicle-mile-traveled basis than doing the work doing the day. 

However, because of the much lower traffic volumes present at night, the total impact upon 

traffic crashes over the duration of completing that work task might still have been much lower 

than if it had occurred during the day. Ensuring that the appropriate measure was computed 

(total crash costs for the project, in this example) will allow agencies to continue to improve their 

overall processes and procedures for delivering a functional, safe, and high-quality transportation 

product to the public. 

NEXT STEPS 

To assist practitioners in applying the findings and lessons learned from this pilot test effort, a 

primer on selecting and computing work zone performance measures is being developed to 

accompany this report.
110 
111 

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U.S. Department of Transportation 

Federal Highway Administration 

Office of Operations 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

http://www.fhwa.dot.gov/workzones

April 2011 

FHWA-HOP-11-022