Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-20-008 MAY 2020 

Alternative Designs to Alleviate

Freeway Bottlenecks at Merging,

Diverging, and Weaving Areas
FOREWORD

Merging, diverging, and weaving areas are major bottleneck locations on uninterrupted flow 

facilities and are a significant source of recurring congestion. This report summarizes the 

development of five alternative designs for application at freeway merging, diverging, and 

weaving areas: split merge points, managed lanes on the right-hand side, mainline metering, 

coordinated ramp metering, and speed optimization via traffic calming devices. This report also 

includes simulations of the innovative treatments to evaluate the benefits of the designs. This 

report will be of interest to State and local departments of transportation who are interested in 

exploring innovative methods to more effectively manage their facilities. 

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations 

Research and Development

Notice

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

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. 

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

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

objective of the document. 

Quality Assurance Statement

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

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

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-20-008

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

4. Title and Subtitle

Alternative Designs to Alleviate Freeway Bottlenecks at Merging, 

Diverging, and Weaving Areas

5. Report Date

May 2020

6. Performing Organization Code:

7. Author(s)

David K. Hale (ORCID: 0000-0001-5486-9367), Jiaqi Ma (ORCID:

0000-0002-8184-5157), Amir Ghiasi (ORCID: 0000-0002-0986-

9840), Pascal Volet (ORCID: 0000-0002-1753-0935), Burak Cesme 

(ORCID: 0000-0002-1265-981X), Alexandra Kondyli (ORCID:

0000-0002-3462-0000), Bastian Schroeder (ORCID: 0000-0001-

8916-421X), Sonika Sethi (ORCID: 0000-0002-5236-0003), 

Laura Torres (ORCID: 0000-0001-8250-0436), and Francois Belisle 

(ORCID: 0000-0003-1592-3986)

8. Performing Organization Report No.

9. Performing Organization Name and Address

Leidos, Inc.

11251 Roger Bacon Drive

Reston, VA 20190-5201

10. Work Unit No.

11. Contract or Grant No.

DFTH6116D00030

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report; July 2017–September 2018

14. Sponsoring Agency Code

HRDO-20

15. Supplementary Notes

The Contracting Officer’s Representative was Joe Bared (HRDO-20). 

16. Abstract

Alternative intersections and interchanges brought major benefits to the United States during the 2008–2018 time 

period. Given the positive impacts, it stands to reason that similar breakthroughs might be possible at freeway 

merge and diverge locations. This report describes the outcomes of a project that examined this possibility. The 

primary tool of evaluation was sensitivity analysis via microscopic traffic simulation, also known as 

microsimulation. The split merge design, and the positioning of managed lanes on the right side of the freeway, 

are two unrelated designs that showed excellent promise (in terms of benefit–cost ratio). Coordinated ramp 

metering did not show significant additional benefits beyond conventional asservissement lin ́eaire d’entr ́ee 

autorouti`ere, also known as ALINEA, metering. Speed optimization showed excellent benefits but only during 

narrow ranges of traffic congestion. Mainline metering also showed reasonably good benefits but could be 

challenging to implement. It is conceivable that speed optimization and mainline metering could produce more 

favorable benefit–cost ratios if they could be implemented via connected and automated vehicle (CAV)

technologies. However, the split merge and right-side managed lane designs produced excellent benefits without 

the need for CAV technologies.

17. Key Words

Split merge, managed lanes, mainline metering, 

coordinated ramp metering, speed optimization, 

freeway merge

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

182

22. Price

N/A

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

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER 1. INTRODUCTION ................................................................................................ 5

CHAPTER 2. SYNTHESIS OF RESEARCH AND FINDINGS.............................................. 7

Innovative Strategies .............................................................................................................. 7

Alternative Geometric Designs ........................................................................................... 7

Speed Optimization ........................................................................................................... 13

Enforcing Late Merge ....................................................................................................... 14

Coordinated Ramp Metering (CRM) ................................................................................ 15

Dynamic Hard Shoulder Running (DHSR) ...................................................................... 15

ML Access Point Optimization ......................................................................................... 15

Other Strategies ................................................................................................................. 15

Relevant Literature ............................................................................................................... 16

Recurring Traffic Bottlenecks: a Primer ........................................................................... 16

Part-Time Shoulder Use (PTSU) ...................................................................................... 16

ICM ................................................................................................................................... 18

CRM .................................................................................................................................. 18

DMC ................................................................................................................................. 21

VSLs ................................................................................................................................. 21

Cycle-based Variable Speed Limits (CVSLs) .................................................................. 22

Design of MLs .................................................................................................................. 22

Summary ........................................................................................................................... 25

CHAPTER 3. RESEARCH WORK PLAN .............................................................................. 27

Acceleration and Deceleration Lane Designs ..................................................................... 27

Speed Optimization (e.g., via dynamic traffic calming devices) ....................................... 27

Mainline Metering (i.e., dynamic signal control) ............................................................... 27

Coordinated Adaptive Ramp Metering System (e.g., HERO) .......................................... 28

Open-Access MLs on the Right-hand side .......................................................................... 28

Site Selection .......................................................................................................................... 28

I–270 MD .......................................................................................................................... 30

I–95 PA ............................................................................................................................. 30

I–35 KS ............................................................................................................................. 36

I–66 VA ............................................................................................................................ 38

I–15 CA ............................................................................................................................. 39

I–35W MN ........................................................................................................................ 40

C–470 CO ......................................................................................................................... 40

I–5 WA.............................................................................................................................. 42

Experimental Approach ....................................................................................................... 42

Input and Output Parameters ............................................................................................ 43

Prioritization of Strategies ................................................................................................ 43
iv

CHAPTER 4. SENSITIVITY ANALYSIS EXPERIMENTS ................................................. 45

Acceleration and Deceleration Lane Designs ..................................................................... 45

Experimental Design ......................................................................................................... 45

Synthetic Network Results ................................................................................................ 48

Simulation Results in a Real-World Network .................................................................. 56

Summary ........................................................................................................................... 58

Speed Optimization (e.g., via dynamic traffic calming devices) ....................................... 58

Experimental Design ......................................................................................................... 59

Results and Discussion ..................................................................................................... 59

Mainline Metering (I.E., dynamic signal control) .............................................................. 61

Dynamic Signal Control Algorithm .................................................................................. 61

Test Environment .............................................................................................................. 61

Simulation Setup and Application .................................................................................... 62

Summary and Conclusions ............................................................................................... 67

Coordinated Adaptive Ramp Metering System (e.g., HERO) .......................................... 69

Model Setup ...................................................................................................................... 69

ALINEA Algorithm .......................................................................................................... 70

HERO Algorithm .............................................................................................................. 71

Results and Discussion ..................................................................................................... 77

Open-Access MLs on the Right-hand side .......................................................................... 82

Methods............................................................................................................................. 83

Assumptions ...................................................................................................................... 83

Experimental Design ......................................................................................................... 85

Simulation Approach ........................................................................................................ 86

Results and Analysis—Simulation Runs .......................................................................... 87

Analysis of Merge Area .................................................................................................... 87

Analysis of Diverge Area .................................................................................................. 91

Analysis of Weaving Area ................................................................................................ 94

I–66 Calibrated Network................................................................................................... 98

Conclusions ....................................................................................................................... 98

CHAPTER 5. CONCLUSIONS ............................................................................................... 101

Acceleration and Deceleration Lane Designs ................................................................... 101

Speed Optimization (e.g., via dynamic traffic calming devices) ..................................... 101

Mainline Metering (i.e., dynamic signal control) ............................................................. 102

Coordinated Adaptive Ramp Metering System (e.g., HERO) ........................................ 103

Open-Access MLs on the Right-hand side ........................................................................ 103

APPENDIX. DATA COLLECTION AND CALIBRATION ............................................... 105

I–66 VA Vissim .................................................................................................................... 106

Background Information ................................................................................................. 106

Overview ......................................................................................................................... 106

O-D Estimation ............................................................................................................... 110

Throughput Calibration ................................................................................................... 112

Calibration of Speed Flow Curves .................................................................................. 112

Calibration of Travel Time ............................................................................................. 112

Reasonableness of Animation ......................................................................................... 112
v 

I–270 MD Vissim ................................................................................................................. 113

Background Information ................................................................................................. 113

Overview ......................................................................................................................... 114

Throughput Calibration ................................................................................................... 115

Calibration of Additional Measures ................................................................................ 116

Reasonableness of Animation ......................................................................................... 119

I–95 PA Vissim .................................................................................................................... 126

Background Information ................................................................................................. 126

Overview ......................................................................................................................... 126

Throughput Calibration ................................................................................................... 130

Calibration of Additional Measures ................................................................................ 132

Reasonableness of Animation ......................................................................................... 135

I–35 KS Vissim .................................................................................................................... 138

Background Information ................................................................................................. 138

Overview ......................................................................................................................... 138

Modeling the Facility ...................................................................................................... 138

Data Inputs ...................................................................................................................... 139

Model Calibration ........................................................................................................... 140

I–15 CA AIMSUN ............................................................................................................... 146

Background Information ................................................................................................. 146

Overview ......................................................................................................................... 147

Driver Behavior Calibration ........................................................................................... 148

Throughput Calibration ................................................................................................... 149

Calibration of Additional Measures ................................................................................ 152

Reasonableness of Animation ......................................................................................... 154

I–394 MN TSIS-CORSIM .................................................................................................. 156

Background Information ................................................................................................. 156

Overview ......................................................................................................................... 157

Throughput Calibration ................................................................................................... 158

Calibration of Additional Measures ................................................................................ 158

Reasonableness of Animation ......................................................................................... 159

ACKNOWLEDGEMENTS ..................................................................................................... 161

REFERENCES .......................................................................................................................... 165
vi 

LIST OF FIGURES

Figure 1. Illustration. Split merge design on an acceleration lane. ................................................. 2

Figure 2. Screenshot. Simulation of congestion formation at a freeway weaving area. ................. 5

Figure 3. Illustration. Freeway acceleration lane with split merge points. ..................................... 7

Figure 4. Illustration. Freeway deceleration lane with split diverge points. ................................... 8

Figure 5. Illustration. Typical T-ramp for reversible-flow MLs facility (Kuhn et al. 2005). ....... 10

Figure 6. Illustration. Typical drop ramp for reversible-flow MLs facility (Kuhn et al. 

2005). ................................................................................................................................... 11

Figure 7. Illustration. Typical Y-ramp for two-lane reversible-flow MLs facility (Kuhn et 

al. 2005). .............................................................................................................................. 12

Figure 8. Illustration. Dynamic traffic calming. ........................................................................... 13

Figure 9. Illustration. Dynamic signal control for both mainline and ramp. ................................ 14

Figure 10. Illustrations. Improved capacity utilization from late merges. .................................... 15

Figure 11. Illustrations. Parallel-style and taper-style off-ramps with PTSU 

(Jenior et al. 2016). .............................................................................................................. 17

Figure 12. Diagram. HERO coordination software (Papamichail et al. 2010). ............................ 19

Figure 13. Illustration. The CVSL concept. .................................................................................. 22

Figure 14. Illustration. ML segment types in the HCM (Wang et al. 2012). ................................ 23

Figure 15. Illustration. ML separation types in the HCM 

(Transportation Research Board 2010). .............................................................................. 24

Figure 16. Illustration. ML cross-weave effect (Wang et al. 2012). ............................................. 24

Figure 17. Map. Southern part of the I–95 Vissim study corridor in PennDOT District 6. ......... 31

Figure 18. Map. Middle part of the I–95 Vissim study corridor in PennDOT District 6. ............ 32

Figure 19. Map. Northern part of the I–95 Vissim study corridor in PennDOT District 6. ......... 32

Figure 20. Map. Northbound US 322 left-side merge in Vissim. ................................................. 33

Figure 21. Map. Estimated travel times in the northbound direction during the a.m. peak: 

estimated travel time for a typical Wednesday around 8 a.m. ............................................ 33

Figure 22. Map. Southbound I–476 right-side merge in Vissim. ................................................. 34

Figure 23. Map. Estimated travel times in the southbound direction during the a.m. peak: 

estimated travel time for a typical Wednesday around 8 a.m. ............................................ 34

Figure 24. Map. Estimated travel times in the northbound direction during the p.m. peak: 

estimated travel time for a typical Wednesday around 5 p.m. ............................................ 35

Figure 25. Map. Estimated travel times in the southbound direction during the p.m. peak: 

estimated travel time for a typical Wednesday around 5 p.m. ............................................ 35

Figure 26. Map. I–35 Kansas City, KS, facility............................................................................ 37

Figure 27. Map. Illustration of the I–66 freeway testbed. ............................................................ 38

Figure 28. Map. Locations of RTMS trailers and portable action cameras. ................................. 39

Figure 29. Chart. Comparison of estimated flow and observed flow from 3:00–3:15 p.m. ......... 39

Figure 30. Map. San Diego, CA, I–15 network. ........................................................................... 40

Figure 31. Photo. Aerial view of C–470 at US 285. ..................................................................... 41

Figure 32. Photo. Buttonhook ramp near the junction of C–470 and US 285. ............................. 41

Figure 33. Map. Aerial view of I–5 at US 101. ............................................................................ 42

Figure 34. Illustrations. Split merge and diverge areas on a synthetic simulation network. ........ 46

Figure 35. Illustrations. Two designs for the synthetic simulation network geometry 

(weaving). ............................................................................................................................ 47
vii 

Figure 36. Graphs. Bottleneck performance at 0.25-mi connection spacing. ............................... 55

Figure 37. Photos. Interchange 3 before and after design............................................................. 57

Figure 38. Charts. Throughput and average delay. ....................................................................... 58

Figure 39. Illustration. Simple weaving section. .......................................................................... 59

Figure 40. Illustration. Location of maximum benefits for speed optimization. .......................... 59

Figure 41. Map. Case study map for the dynamic signal control on I–35 Kansas City, KS. ....... 62

Figure 42. Charts. Simulation results for the default setting. ....................................................... 63

Figure 43. Illustration. Example signal timing plan. .................................................................... 64

Figure 44. Graphs. Sensitivity analysis on Dmain and Dramp. ......................................................... 66

Figure 45. Box-and-whisker plots. Sensitivity analysis on Rramp. ................................................ 67

Figure 46. Illustration. Selected area of the I–15 corridor and model setup for ALINEA and 

SDRMS ramp metering. ...................................................................................................... 70

Figure 47. Graphs. Mainline speed/time just upstream of on-ramp 9 and on-ramp 8. ................. 78

Figure 48. Illustrations. Synthetic simulation network (merge and diverge). .............................. 84

Figure 49. Illustrations. Synthetic simulation network geometry (weaving)................................ 85

Figure 50. Maps. TMCs used in the calibration.......................................................................... 110

Figure 51. Map. Zone locations. ................................................................................................. 111

Figure 52. Screenshot. Merging area simulation example one (bird’s eye view)....................... 112

Figure 53. Screenshot. Merging area simulation example two (zoomed-in view). .................... 113

Figure 54. Screenshot. Lane drop simulation (zoomed-in view). ............................................... 113

Figure 55. Map. I–270 southbound starting at Shady Grove (a.m. peak). .................................. 114

Figure 56. Illustration. Field-measured speed heat map from 7:00–8:00 a.m. ........................... 120

Figure 57. Illustration. Field-measured speed heat map from 5:00–6:00 p.m. ........................... 120

Figure 58. Illustration. Field-measured speed heat map for p.m. peak hour. ............................. 121

Figure 59. Illustration. Field-measured speed heat map for a.m. peak hour. .............................. 121

Figure 60. Diagram. Animation check at 7:10 a.m. .................................................................... 122

Figure 61. Diagram. Animation check at 7:20 a.m. .................................................................... 122

Figure 62. Diagram. Animation check at 7:30 a.m. .................................................................... 123

Figure 63. Diagram. Animation check at 7:40 a.m. .................................................................... 123

Figure 64. Diagram. Animation check at 5:10 p.m..................................................................... 124

Figure 65. Diagram. Animation check at 5:20 p.m..................................................................... 124

Figure 66. Diagram. Animation check at 5:40 p.m..................................................................... 125

Figure 67. Illustration. A stuck vehicle on an on-ramp. ............................................................. 125

Figure 68. Map. I–95 corridor in Pennsylvania. ......................................................................... 127

Figure 69. Illustrations. How traffic flows on the I–95 corridor in Pennsylvania. ..................... 128

Figure 70. Illustrations. I–95 weekday congested locations. ...................................................... 129

Figure 71. Screenshots. Vissim screenshots of I–95 PA. ........................................................... 131

Figure 72. Map. Typical Tuesday traffic at 5:00 p.m. for I–95 southbound. ............................. 136

Figure 73. Screenshot. Vissim screenshot during the p.m. peak hour for I–95 southbound. ..... 136

Figure 74. Map. Typical Tuesday traffic at 5:00 p.m. for I–95 northbound. .............................. 137

Figure 75. Screenshot. Vissim screenshot during the p.m. peak hour for I–95 northbound. ...... 137

Figure 76. Screenshot. Vissim model of the I–35 KS corridor. .................................................. 139

Figure 77. Map. Kansas City Scout speed profile. ..................................................................... 143

Figure 78. Map. Calibrated Vissim model speed profile. ........................................................... 144

Figure 79. Charts. Speed-flow graphs along the I–35 southbound corridor. .............................. 146
viii

Figure 80. Diagram. Flowchart describing the calibration approach (Dowling, Skabardonis, 

and Alexiadis 2004). ......................................................................................................... 147

Figure 81. Illustration. Updated PeMS I–15 a.m. southbound speed contour. ........................... 153

Figure 82. Illustration. Updated Aimsun modeled I–15 a.m. southbound speed contour. ......... 153

Figure 83. Map. Location and geographic boundaries of the I–15 corridor 

(Dion and Skabardonis 2015). ........................................................................................... 156

Figure 84. Map. Calibrated 8-mi stretch of I–394 eastbound between I–494 and I–94. ............ 157

Figure 85. Map. Speed limits near Minneapolis. ........................................................................ 157

Figure 86. Screenshot. Calibration of I–394 MN volumes. ........................................................ 158

Figure 87. Screenshot. Calibration of I–394 MN speeds. ........................................................... 159

Figure 88. Screenshot. Calibration of I–394 MN densities. ....................................................... 159

Figure 89. Screenshot. Formation of queue spillback on I–394 MN. ......................................... 160

Figure 90. Screenshot. Queue spillback extent on I–394 MN. ................................................... 160
ix

LIST OF TABLES

Table 1. Site selection matrix of characteristics. .......................................................................... 29

Table 2. System parameters considered in this study. .................................................................. 47

Table 3. Simulation results for split merge connection spacing. .................................................. 49

Table 4. Simulation results for split diverge distance between gores. .......................................... 51

Table 5. Average speeds due to speed limit and demand. ............................................................ 60

Table 6. Parameters used for the six sensitivity analysis tests—ALINEA. .................................. 76

Table 7. Parameters used for the six sensitivity analysis tests—HERO. ...................................... 77

Table 8. Summary of HERO activation and deactivation times for test six with the master 

ramp and its corresponding slaves....................................................................................... 77

Table 9. Performance comparison among the different tests and for all four scenarios—

mainline average travel time (s). ......................................................................................... 79

Table 10. Performance comparison among the different tests and for all four scenarios—

mainline and ramp total travel time (h). .............................................................................. 80

Table 11. Performance comparison among the different tests and for all four scenarios—

mainline and on-ramp weighted average harmonic speeds (mi/h). ..................................... 80

Table 12. Performance comparison among the different tests and for all four scenarios—

VHT (h). .............................................................................................................................. 80

Table 13. Performance comparison among the different tests and for all four scenarios—

VMT (mi). ........................................................................................................................... 80

Table 14. System parameters considered in study. ....................................................................... 86

Table 15. Driver behavior parameters. ......................................................................................... 87

Table 16. Throughput and delay ANOVA table. .......................................................................... 89

Table 17. T-test results—average delay and throughputs on the GP lanes. ................................. 90

Table 18. T-test results—average delay and throughputs on the HOV lanes. .............................. 90

Table 19. T-test results—total throughputs on the GP and HOV lanes. ....................................... 90

Table 20. T-test results—total delay on the GP and HOV lanes. ................................................. 90

Table 21. ANOVA throughputs. ................................................................................................... 92

Table 22. ANOVA average delay. ................................................................................................ 92

Table 23. ANOVA total throughputs and delay. .......................................................................... 92

Table 24. Total throughput T-tests................................................................................................ 93

Table 25. Total delay T-tests. ....................................................................................................... 93

Table 26. ANOVA tests of total measurements—throughputs. ................................................... 95

Table 27. ANOVA tests of total measurements—average delay. ................................................ 95

Table 28. ANOVA total throughputs and delays. ......................................................................... 95

Table 29. Total throughput and delay T-tests. .............................................................................. 97

Table 30. Westbound RTMS trailer locations. ........................................................................... 107

Table 31. Westbound traffic management center (TMC) locations (INRIX 2018). ................... 108

Table 32. Zone definitions .......................................................................................................... 111

Table 33. O-D example of 3:00–3:15 p.m. (veh/h). .................................................................... 111

Table 34. 2015 a.m. peak hour traffic volumes for I–270. ......................................................... 115

Table 35. 2015 p.m. peak hour traffic volume for I–270. ........................................................... 116

Table 36. 2015 a.m. travel time calibration results for I–270: I–270 northbound from the 

I–495 interchange. ............................................................................................................. 117

Table 37. 2015 a.m. travel time calibration results for I–270: I–270 southbound from I–70..... 118
x 

Table 38. 2015 a.m. travel time calibration results for I–270: I–270 spur northbound from 

Cabin John Parkway. ......................................................................................................... 118

Table 39. 2015 a.m. travel time calibration results for I–270: I–270 spur southbound from 

I–70. ................................................................................................................................... 119

Table 40. I–95 PA throughput calibration plan—Philadelphia County, PA............................... 130

Table 41. I–95 PA throughput calibration plan—Delaware County, PA. .................................. 130

Table 42. I–95 PA throughput calibration summary. ................................................................. 131

Table 43. 2017 calibration of speeds and travel times on I–95 PA—northbound direction. ...... 133

Table 44. 2017 calibration of speeds and travel times on I–95 PA—southbound direction. ..... 133

Table 45. Morning travel time calibration measurements—northbound. ................................... 134

Table 46. Morning travel time calibration measurements—southbound.................................... 134

Table 47. Evening travel time calibration measurements—northbound. ................................... 135

Table 48. Evening travel time calibration measurements—southbound. ................................... 135

Table 49. Calibrated Wiedemann 99 model parameters. ............................................................ 141

Table 50. Calibrated lane changing parameters. ......................................................................... 142

Table 51. Calibration criterial and acceptance targets for 2016. ................................................ 151

Table 52. Number of travel times run and percentage by period. ............................................... 152
xi 

LIST OF ABBREVIATIONS

AASHTO American Association of State Highway and Transportation Officials

ADT average daily traffic

ALINEA asservissement linéaire d’entrée autoroutière 

ANOVA analysis of variance

API application programming interface 

ATDM advanced transportation demand management 

ATM active transportation management

Caltrans California Department of Transportation 

CARMA Continuous Association Rule Mining Algorithm 

CAV connected and automated vehicle

CD collector-distributor 

CPS congestion pricing system 

CRM coordinated ramp metering 

CVSL cycle-based variable speed limits

DHSR dynamic hard shoulder running 

DMC dynamic merge control 

DOT department of transportation 

FHWA Federal Highway Administration

FSP Freeway Service Patrol

GP general purpose 

HCM Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility 

Analysis

HCS Highway Capacity Software 

HERO heuristic ramp metering coordination 

HOT high-occupancy toll 

HOV high-occupancy vehicle

I2V infrastructure-to-vehicle

ICM integrated corridor management

ITE Institute of Transportation Engineers 

ITS intelligent transportation systems

KDOT Kansas Department of Transportation

LHD Latin hypercube sampling design

LOS level of service

ML managed lane

MnDOT Minnesota Department of Transportation 

MOE measure of effectiveness

MOP measure of performance

NCHRP National Cooperative Highway Research Program 

O-D origin–destination

PeMS Caltrans Performance Measurement System

PennDOT Pennsylvania Department of Transportation 

PTSU part-time shoulder use 

RITIS Regional Integrated Transportation Information System 

RMIS ramp metering information system 
xii 

RTMS remote traffic microwave sensor

SDRMS San Diego ramp metering system

STOL Saxton Transportation Operations Laboratory 

SWARM Systemwide adaptive ramp metering

TCRP Transit Cooperative Research Program

V/C volume-to-capacity

VDOT Virginia Department of Transportation 

veh/h vehicles per hour 

veh/h/l vehicles per hour per lane 

VHT vehicle hours traveled

VicRoads Roads Corporation of Victoria 

VMT vehicle miles traveled

VSL variable speed limit

WSDOT Washington State Department of Transportation
1 

EXECUTIVE SUMMARY

The so-called “alternative” intersections and interchanges represent one of the biggest success 

stories for surface transportation mobility in recent years. Indeed, in the 1990s, few would have 

imagined that a small set of radical changes to the fundamental geometric design of signalized 

intersections could reduce delays and crashes by nearly one-half in many instances (Bared and 

Zhang 2016). Nonetheless, currently, real-world implementations of the diverging diamond 

interchange, restricted crossing U-turn, median U-turn, and displaced left turn now collectively 

number in the hundreds in the United States, and expectations are that their implementation will

increase (Sieminski 2017). 

Given the widespread, positive impacts of these alternative designs, it stands to reason that 

similar breakthroughs might well be possible at freeway merge and diverge locations, which 

have long been a major source of traffic congestion, delays, and crashes. This report describes 

the planning and outcomes of a research project that aimed to examine this possibility. The 

primary tool of evaluation was a sensitivity analysis via microscopic traffic simulation, also 

known as microsimulation, which is the ideal platform to test hundreds of possible designs safely 

and efficiently prior to implementation in the field. The objective of this project was to identify 

alternative freeway designs that achieved significant mobility benefits (e.g., reduced delay, 

increased throughput) in the virtual simulation environment, such that subsequent human factors 

studies and/or field testing would be warranted. 

Before the simulation sensitivity analyses could begin, an initial phase of brainstorming and 

literature review was necessary to identify alternative freeway designs as candidates for this 

study. From this review of literature, the researchers selected five top-candidate designs for 

further investigation. These alternative designs incorporated control strategies as well as 

geometric modifications and are listed below:

• Split merge and diverge points.

• Managed lanes (ML) on the right.

• Mainline metering.

• Coordinated ramp metering (CRM).

• Speed optimization via traffic calming devices.

This report details the project’s motivation (chapter 1), synthesis of research and findings 

(chapter 2), research work plan (chapter 3), simulation results (chapter 4), and conclusions 

(chapter 5). An appendix is also included to describe the approach to data collection and 

microsimulation calibration. Ultimately, the top two alternative design candidates (i.e., split 

merge points and MLs on the right-hand side) showed the most promise in the simulation tests. 

Figure 1 illustrates the split merge design, where vehicles can no longer cause turbulence by 

merging in the middle of the acceleration lane.
2 

© 2019 Jiaqi Ma.

Figure 1. Illustration. Split merge design on an acceleration lane.

The split merge design is the most analogous to the alternative intersection and interchange 

designs, which represent low-cost geometric improvements capable of achieving significant 

mobility benefits. The split merge design achieved delay reductions up to 80 percent in some 

cases. The best benefits were observed under near capacity levels of traffic congestion. A split 

diverge design was also tested, but did not consistently achieve significant benefits. The 

simulations in this report only evaluated one-lane ramps, but the researchers believe two-lane 

ramps would exhibit similar impacts.

In the United States, open-access MLs are typically not located on the right side of the freeway 

because it would likely degrade mobility on the ML. However, this project tested the following 

initial hypotheses:

• Although managed lane mobility would be degraded, it would still provide superior

overall level of service when compared to the general purpose (GP) lanes.

• The substantial GP lane benefits would far exceed any mobility losses on the MLs,

resulting in significant improvement in overall freeway mobility.

When comparing simulation tests, MLs located on the right showed overall delay reductions up 

to 30 percent relative to MLs on the left. 

The mainline metering strategy is intended to temporarily treat on-ramp merge junctions like 

conventional signalized intersections once vehicle speeds reach a low enough level. The 

simulation tests showed delay reductions up to 21 percent under mainline metering algorithms 

when stopping mainline traffic and on-ramp traffic successively like a two-phase traffic signal. 

However, the mainline metering strategy would require unprecedented signalization on the 

mainline, a finely-tuned algorithm, and new safety risks. As such, the cost-effectiveness of such 

a strategy remains questionable. It is conceivable that enhanced improvements could be possible 

by deploying the strategy at several locations within a congested corridor. 

CRM has already received significant research attention and a few real-world deployments. This 

study found improvements of only 2 percent over the proven, conventional ramp metering 

approaches of asservissement linéaire d’entrée autoroutière (ALINEA) metering, literally 

meaning “linear enslavement of a freeway entrance.” Thus, the cost-effectiveness of 

coordinating the ramp meters is also questionable under the tested scenarios. CRM may provide 

benefits under other geometric and volume configurations or if combined with mainline metering 
3 

in a “managed motorways” approach, as done in Australia. Indeed, the Roads Corporation of 

Victoria (VicRoads) reports much success with its implementation of city-wide CRM in 

Melbourne. However, this study did not investigate large-scale deployments like this. 

Finally, the researchers considered the possibility of reducing vehicle speeds to prevent 

bottleneck formation through deployment of traffic calming devices. Although traffic calming 

devices cannot be modeled explicitly through microsimulation, in some cases, simply lowering 

posted speed limits (e.g., from 65 to 55 mi/h) resulted in substantial improvements in vehicle 

speed (e.g., from 9 to 52 mi/h). The caveat was that the benefits were only attainable at certain 

site-specific congestion levels, implying the need for accurate simulation models and/or 

historical data to determine the target congestion level for any given site. Moreover, an 

additional assumption is that installing traffic calming devices on freeways would still be safe 

but would make high-speed travel uncomfortable. Therefore, the cost-effectiveness of speed 

optimization via traffic calming devices is questionable.

5 

CHAPTER 1. INTRODUCTION

Cities face increasing freeway congestion, and few short-term options exist for increasing 

freeway capacities. Widening roadways or implementing connected and automated vehicles 

(CAVs) are longer-term solutions that will provide benefits, but there are solutions that show 

short-term promise. This report evaluates freeway merge and diverge area improvements where 

increased lane changing results in slowing due to the increased lane changing and 

accommodation for vehicles entering or exiting the freeway. Freeway merge and diverge areas 

are a primary source of turbulence, congestion, and traffic breakdown, both at the sites and 

upstream. In fact, research has shown that the capacity at merge or diverge segments is 

significantly less than the basic freeway segments reported in the Highway Capacity Manual, 

Sixth Edition: A Guide for Multimodal Mobility Analysis (HCM) (Kondyli et al. 2017). 

In most cases, traffic breakdowns occur on the right-hand side of the mainline, as incoming and 

outgoing ramp vehicles produce excessive friction. Figure 2 illustrates the onset of severe 

congestion and queuing caused by typical freeway weaving maneuvers. Once a breakdown like

this begins, the resulting shockwaves and queue spillback can eventually stretch back for miles, 

causing area-wide travel time reliability problems.

Source: FHWA.

Figure 2. Screenshot. Simulation of congestion formation at a freeway weaving area. 

Similar breakdowns have originated on the left-hand side of the freeway because of managed 

high-occupancy vehicle (HOV) lanes. The need for managed lane (ML) vehicles to weave to and 

from the right-side on-ramps and off-ramps further exacerbates these breakdowns by causing a 

substantial amount of turbulence and interference within the general purpose (GP) lanes.

Given the prevalence of the freeway merge problem, any low-cost solution to reduce the friction 

associated with vehicle maneuvers, or facilitate smoother merging/diverging operations, could 

produce great improvements in traffic flow. This is because a nationwide deployment solution, at 

dozens if not hundreds of sites, could continuously produce daily time savings for peak-hour 

commuters throughout the year. The solution could fall within the realm of dynamic message 

signs, traffic control devices, geometric design modifications, traffic calming devices, new forms 

of driver information, or new traffic management algorithms. The alternative intersection and 

interchange designs (e.g., median U-turn) are examples of such solutions for urban arterial 

streets.

This report describes the development and testing of innovative designs and traffic management 

systems that may alleviate merging problems on the left- and/or right-hand sides of the freeway. 

To verify operational effectiveness of the proposed designs, the team conducted comprehensive 
6 

sensitivity and scenario analyses in a traffic microsimulation environment. The ultimate goal is 

to achieve widespread implementation of one or more effective designs. 

The primary tool of evaluation is sensitivity analysis via microscopic traffic simulation, also 

known as microsimulation. Microsimulation reproduces vehicle behavior and interaction during 

merging, changing lanes, acceleration, and deceleration, and replicates the impact on traffic flow. 

This helps to predict the impact of changes in traffic patterns resulting from changes to traffic 

flow or the physical environment. For this study, microsimulation helped evaluate changes in 

merge lanes, placement of HOV lanes, signaling, and the physical environment, such as the 

narrowing of lanes or the relocation of junction stop lines.
7 

CHAPTER 2. SYNTHESIS OF RESEARCH AND FINDINGS

The team conducted a literature search of current practices and designs for mitigating freeway 

merge and diverge congestion problems on both sides of the freeway, with the left-hand side 

typically involving MLs. The team further identified several real-world sites that would 

potentially benefit from further analysis and weaving mitigation, understanding that some 

findings would be based on reported experiences (i.e., unpublished). The intent was not to 

conduct site-specific analysis, but rather to expand the use of these sites for generic analyses and 

recommendations. Site selection considered the following site-specific criteria:

• The side of the freeway (left- or right-hand) responsible for the congestion.

• Spatial and temporal extent of congestion (too short or too long may not be suitable).

• Availability of sufficient, good-quality data.

• Presence of a preexisting mitigation strategy.

• Availability of preexisting microsimulation datasets.

• Interested local representatives who could provide assistance.

• Expense required to develop a brand-new simulation dataset.

Originally, the following categories defined the team’s synthesis of research and findings: 

• Innovative strategies.

• Relevant literature.

• Relevant individuals, agencies, and sites.

• Known resources (e.g., available data and simulation datasets).

Chapter 2 summarizes the innovative and relevant literature findings. 

INNOVATIVE STRATEGIES

Alternative Geometric Designs

Chapter 1 stated that the alternative intersection and interchange designs are examples of 

innovative geometric design solutions for urban arterial streets. The first alternative geometric 

design conceived within the boundaries of this project is a set of two alternative auxiliary lane 

designs: one design for the acceleration lanes that follow on-ramps and a second design for the 

deceleration lanes that precede off-ramps. Figure 3 shows the alternative acceleration lane design 

intended to reduce merging friction by limiting the exposure of on-ramp vehicles to two specific 

entry points. 

Source: FHWA.

Figure 3. Illustration. Freeway acceleration lane with split merge points.
8 

Figure 4 shows the alternative deceleration lane design intended to reduce diverging friction by 

limiting the weaving of off-ramp vehicles to two specific exit points. Presumably, in this design, 

the two exiting lanes enable merging further downstream on the off-ramp.

Source: FHWA.

Figure 4. Illustration. Freeway deceleration lane with split diverge points.

A third alternative geometric design conceived by the researchers is the placement of MLs on the 

right-hand side of the freeway. This design is intended to be compatible with unpaid open-access 

HOV lanes only. Traditionally, MLs were designed to provide a higher level of service than GP 

lanes. Weaving maneuvers from GP lane vehicles through HOV traffic on a right-side ML could 

compromise the level of service on the ML, undermining its purpose. For example, why would 

drivers carpool if the level of service on an ML is comparable or inferior to the GP lanes? 

However, since there are usually more GP lanes than MLs, it is conceivable that overall traffic 

flow (accounting for all lanes) could benefit. The team further envisioned microsimulation 

experiments that would focus on traffic flow operations as opposed to pricing. 

A fourth alternative geometric design considered by the researchers is the grade-separated ramp 

for MLs, also known as a direct access ramp. Atlanta, GA; the Washington, DC area; and 

San Diego, CA, have deployed grade-separated ML ramps. Following the review of literature 

and findings, the team would decide whether to examine such ramps further. Even though not 

necessarily considered innovative, comparison of their performance to the performance of other 

innovative designs is possible and useful. One stakeholder made the following relevant comment 

on the Institute of Transportation Engineers (ITE) Community blog (ITE Community 2017): 

A useful, but expensive design feature is to provide direct access connections to and from 

the HOV/HOT [high-occupancy toll] lanes. The direct exit connections are the most 

useful because it is very difficult to determine how far in advance of an interchange a 

driver should exit the HOV/HOT lane to reach their destination. It is this exit maneuver 

that causes the greatest turbulence to the through lane flow because it has to happen in a 

given distance. In comparison, an entering HOV can work its way to the diamond lane as 

conditions permit. The arrangement on I–5 in Orange, Co. to the north at Disneyland is a 

good example of this. At that location, there are direct exit ramps from the diamond 

lanes, but entering traffic from Disney must weave from the outside to reach them on 

their return trip. The traffic flow characteristics at Disneyland work well with this 

arrangement since traffic leaves Disneyland after the PM peak on the freeway.
9 

The purpose of grade-separated ramps for MLs is to reduce weaving across GP lanes to enter and 

exit ML facilities. According to the Texas Department of Transportation’s Managed Lanes 

Handbook, which presents 5 years of research by the Texas Transportation Institute on MLs, the 

design of ML facilities should accommodate grade-separated or direct ramps during a later phase 

if not included in the initial design (Kuhn et al. 2005). Several types of grade-separated ramps for 

MLs, also called direct access ramps, are in use and include T-ramps/drop ramps and flyover 

ramps/Y-ramps. T-ramps (figure 5) or drop ramps (figure 6) use a T-shaped ramp design that 

drops from the ML to the freeway, local street, or other facility. This ramp design works best 

with a low ramp speed design (less than 35 mi/h), can be suited to low vehicle volumes, and can 

be spaced frequently (less than 3 mi apart). Construction of T-ramps can also provide direct 

access between the ML and a park-and-ride lot.

Flyover ramps include Y-ramps (figure 7) where each direction of travel diverges from the inside 

to opposite sides of the freeway facility and more conventional flyover designs providing access 

to and from the ML facility. Y-ramps and flyover ramps are suited to high-speed (greater than 

35 mi/h) and high-volume (greater than 400 vehicles per hour (veh/h)) conditions (Kuhn et al. 

2005).
10 

© 2005 Beverly Kuhn.

Figure 5. Illustration. Typical T-ramp for reversible-flow MLs facility (Kuhn et al. 2005). 
11 

© 2005 Beverly Kuhn.

1 ft = 0.3 m.

Figure 6. Illustration. Typical drop ramp for reversible-flow MLs facility (Kuhn et al. 2005). 
12 

© 2005 Beverly Kuhn.

Figure 7. Illustration. Typical Y-ramp for two-lane reversible-flow MLs facility (Kuhn et al. 2005). 
13 

Earlier research supported the development of the Texas Department of Transportation’s

Managed Lanes Handbook, focusing specifically on on-ramp design issues (Fitzpatrick, Brewer, 

and Venglar 2003). Simulation of at-grade ML ramps that varied the ramp spacing, lane 

volumes, and percent of entering ramp traffic to the ML facility using Vissim, a microscopic 

traffic simulator, found that in the 30 percent weaving with 1,750 vehicles per hour per lane 

(veh/h/l), speeds fell below 45 mi/h, and a large percentage of vehicles were not serviced during 

the simulation run due to congestion (PTV Group 2019). The simulation also investigated when 

grade-separated ramps should be used. Results supported guidance that a grade-separated ramp 

should be considered when ramp volume is 400 veh/h. In their abstract and conclusions sections, 

Fitzpatrick, Brewer, and Venglar (2003) also state that, “if a more conservative approach to 

preserving freeway performance is desired, then a direct connect ramp should be considered at 

275 veh/h” to maintain a minimum speed of 45 mi/h. 

Speed Optimization

Extensive research conducted at the Saxton Transportation Operations Laboratory (STOL) 

looked at speed harmonization and variable speed limits (VSLs). One team developed a VSL 

synthesis. Three separate STOL task orders conducted extensive research on 

infrastructure-to-vehicle (I2V)-based speed harmonization (Hale et al. 2016). The projects 

produced an extensive bibliography containing dozens of speed harmonization sources. The 

consensus of this research is that VSL (which optimizes speed limits as opposed to actual 

speeds) is primarily beneficial for improving safety and suffers from driver compliance rates that 

are too low to achieve significant operational benefits in most cases. Regarding speed 

harmonization, the consensus is that the strategy is not highly beneficial without advanced 

vehicle technologies. However, the STOL projects have shown that CAV penetration rates as 

low as 10 percent can produce significant operational benefits. These outcomes imply that speed 

optimization via non-CAV methods are also likely to produce significant operational benefits. 

The simulation experiments from this project attempt to confirm this.

Regarding speed optimization via non-CAV methods, the team focused on two methods. First, 

the team looked at quasi-exhaustive sensitivity analysis of freeway merging (and possibly 

diverging) at “ideal” speeds (i.e., in the 30–35 mi/h range) (figure 8). 

Source: FHWA.

Figure 8. Illustration. Dynamic traffic calming. 
14 

If optimized speeds were found to improve traffic flow by more than a few percentage points, the 

team would then recommend future investigation of dynamic traffic calming devices 

(e.g., rumble strips, circular dots) that could improve driver compliance rates with mid-level 

speeds without producing unsafe rides or conditions. 

Second, the team looked at mainline metering. This would involve dynamic signal control of the 

mainline and ramp when all speeds drop below a fixed threshold (e.g., 5 mi/h). There is no 

known literature on this strategy (figure 9), but the team continued searching and inquired with 

stakeholders. One stakeholder made the following comment on the ITE Community blog 

(ITE Community 2017): 

I do want to mention the idea of metering the mainline, which I haven’t seen 

implemented anywhere. If density at the bottleneck exceeds max flow density, an 

upstream meter could be used to limit density and increase flow to the max through the 

bottleneck, intentionally creating an upstream queue that is shorter than the queue that 

would result from allowing the bottleneck to jam up. We traditionally think of meters as 

red and green traffic signals, but some agencies are considering rumble strips as pseudo 

meters because they slow traffic without requiring a stop. 

Indeed, the rumble strips comment is relevant to both envisioned non-CAV methods of speed 

optimization (i.e., dynamic traffic calming and dynamic mainline metering via signal control).

Source: FHWA.

Figure 9. Illustration. Dynamic signal control for both mainline and ramp. 

Enforcing Late Merge

Enforced “zippering” maneuvers could help to achieve better efficiency at merge areas but may 

be difficult to achieve without CAV technologies. The team considered conducting simulations 

of late merging to assess the potential benefits (figure 10). Conceivably, signage can encourage 

late merging. The main concern is that the potential capacity gain could be too small for the 

strategy to be worthwhile. Therefore, this strategy was ultimately ruled out for detailed 

investigations. 
15 

Coordinated Ramp Metering (CRM) 

Whereas traditional ramp meters have operated in isolation, CRM provides more of a 

facility-wide approach, similar to coordinated signalized surface arterials. Although some of the 

team’s ideas listed in this chapter do not have any known literature or associated studies, several 

studies have investigated CRM. Moreover, a limited number of cities have deployed CRM 

already. As such, the relevant literature section of this chapter gives a more extensive writeup of 

CRM.

Source: FHWA.

Figure 10. Illustrations. Improved capacity utilization from late merges. 

Dynamic Hard Shoulder Running (DHSR)

Several studies have investigated DHSR deployed in a limited number of cities. As such, the 

relevant literature section of this chapter gives a more extensive writeup of DHSR. 

ML Access Point Optimization

Proper design of the distance between managed lane entry/exit points and right-side on/off-ramps 

can facilitate traffic flow (Zitzow, Parikh, and Hourdos 2018). One stakeholder made the 

following comment on the ITE Community blog (ITE Community 2017): 

Where barrier striping is used to separate the diamond lane, locating the entrance and exit 

points requires extensive knowledge of the overall freeway operation in those segments. 

The research team used extensive traffic simulation to determine those locations during 

the design of the I–394 HOT lane project west of Minneapolis. 

Other Strategies

For various reasons, some identified, innovative strategies were ruled out from further 

consideration during the brainstorming phase of the project. These include: 

• Law enforcement: perhaps not suitable for a simulation study. 

• Lane change speed advisories: perhaps not viable with CAV technologies. 

• Lane change location signs (creating gaps): perhaps not viable with CAV technologies. 
16 

• Integrated corridor management (ICM) strategies: the team favored point strategies over

ICM-type demand management strategies.

• ML access restriction.

• Dynamic message signs.

• Dynamic lane markings.

RELEVANT LITERATURE

Recurring Traffic Bottlenecks: a Primer

The primer says agencies should focus on “chokepoints” for which numerous low-cost solutions 

are possible (Spiller, Blizzard, and Margiotta 2017). The primer provides three principles for 

efficient merging: low speeds, large gaps between vehicles, and zippering. This overlaps with 

two of the strategies previously identified in the innovative strategies section: speed optimization

and enforcing late merge. However, the primer does not appear to provide technical insights into 

the implementation of strategies. Large gaps between vehicles would be helpful, but a method 

for achieving that without CAV technologies seems elusive. A method to accomplish zippering 

without CAV technologies also seems elusive.

Part-Time Shoulder Use (PTSU)

PTSU is the temporary conversion of highway shoulders to travel lanes to relieve congestion. 

Also known as temporary shoulder use, hard shoulder running, or dynamic shoulder use, this 

strategy is a low-cost method to increase capacity when most needed, either for general traffic or 

for transit vehicles only. Recent research projects investigated the planning, design, operations, 

and safety aspects of PTSU (Jenior et al. 2016; Dowling, Margiotta, and Jacobsen 2011; 

Dowling et al. 2016; and Martin, Levinson, and Institute 2012). Further, Transit Cooperative 

Research Program (TCRP) Report 151 provides guidance for implementing shoulder use 

strategies for bus transit (Martin et al. 2012). 

Sixteen States have implemented PTSU, but application tends to be ad hoc with little consistency 

across cases. Fourteen States use bus-on-shoulder strategies, with most applications on the 

right-side shoulder. Eight States (Colorado, Georgia, Hawaii, Massachusetts, New Jersey, 

Virginia, Texas, and Washington) allow general traffic to use freeway shoulders during fixed 

times of the day. Minneapolis and Virginia have documented use of dynamic PTSU through 

traffic-responsive dynamic signs, but several states are planning dynamic PTSU. The most 

complex aspect of implementing successful PTSU is the need for multiagency and 

multistakeholder coordination. Before and after study data primarily comes from Europe 

(Netherlands, Germany, United Kingdom, and Ireland) due to the limited number of recent 

deployments in the United States (Jenior et al. 2016). Static part-time, dynamic shoulder use is 

not necessarily an “innovative” strategy, but it is a resourceful strategy and, given its lack of 

widespread but growing implementation, is somewhat innovative. 

In general, PTSU increases capacity; however, applications that do not allow shoulder use 

through interchanges require shoulder traffic to merge back into GP lanes. One strategy to reduce 

weaving at the end of shoulder-use sections is to add a GP lane in advance of an interchange 

(Jenior et al. 2016). Dowling et al. (2011) note that it would be useful to measure the weaving 
17 

intensity factor for weaving sections with PTSU, but data collection is cost prohibitive. 

Microsimulation studies showed that dynamic PTSU could be effective at mitigating traffic 

bottlenecks formed by property-damage-only traffic accidents (Ma et al. 2016). 

Jenior et al. (2016) illustrate various types of merge and diverge areas for PTSU and recommend

temporarily converting taper ramps to parallel-style ramps with speed change lanes to reduce 

conflicts between vehicles using the shoulder and crossing traffic. This could potentially increase 

capacity, as shown in figure 11. 

© 2016 Kittleson & Associates, Inc.

A. Parallel-style off-ramp traffic with PTSU. 

© 2016 Kittleson & Associates, Inc.

B. Taper-style off-ramp ramp traffic with PTSU. 

© 2016 Kittleson & Associates, Inc.

C. Conversion of taper-style to parallel-style off-ramp.

Figure 11. Illustrations. Parallel-style and taper-style off-ramps with PTSU

(Jenior et al. 2016). 
18 

The temporary conversion depicted in figure 11-C can be achieved with pavement markings, but 

the geometry of the existing ramp (specifically the convergence angle at the gore area) will affect 

the degree of maneuverability afforded. A larger convergence angle allows for less maneuver 

area, resulting in greater impacts to capacity. Conversion of two-lane taper ramps may require 

the addition of another speed change lane downstream of the entry gore or upstream of the exit 

gore (Jenior et al. 2016). 

In addition to design and operational considerations, Kuhn et al. (2013) discuss benefits and 

drawbacks of advanced transportation demand management (ATDM) strategies that complement 

PTSU, including VSLs, queue warning, dynamic merge control (DMC), and dynamic traveler 

information. They note that many of these require intelligent transportation systems (ITS) 

technologies and interagency coordination and deployment accompanied by performance 

measures.

ICM

There is an extensive body of literature related to ICM. According to Zhang et al. (2014), ICM is 

a proven congestion relief strategy. However, expectations were that this project would focus on 

facility strategies as opposed to corridor-wide strategies.

CRM

Heuristic Ramp Metering Coordination (HERO)

Professor Markos Papageorgiou and Dr. Ioannis Papamichail at the Dynamic Systems and 

Simulation Laboratory of the Technical University of Crete, Greece, developed a traffic 

responsive feedback control strategy that coordinates local ramp metering actions for freeway 

networks (Papamichail et al. 2010). HERO, the proposed coordination scheme, has seen 

extensive testing via simulation and in-field implementations. HERO is modular in structure and 

includes many interacting and cooperating feedback control loops (such as mainstream 

occupancy control, ramp queue-length control, and waiting time control) as well as two Kalman 

filters for estimation of ramp queue length and mainstream critical occupancy (Samad and 

Annaswamy 2011). 

HERO uses an extended version of the ALINEA feedback control algorithm used in the field at a 

local level (i.e., individual junctions). The HERO control strategy is coordinated, 

feedback-based, ruled-based, reactive (no real-time modeling needed), and generally applicable 

to any freeway network. The HERO algorithm employs at its base the local, uncoordinated 

control algorithm ALINEA, used at 100 on-ramps around the world since its development in the 

1980s. HERO introduces coordination to the system on an as-needed basis by setting minimum 

ramp queue lengths that supersede the ALINEA algorithm. This reallocates congestion from a 

ramp at the bottleneck to a temporary cluster of upstream ramps until the congestion dissolves 

and the cluster deactivates. Figure 12 shows a schematic of the HERO software.
19 

© 2010 Ioannis Papamichail.

Figure 12. Diagram. HERO coordination software (Papamichail et al. 2010). 
20 

The HERO traffic-responsive ramp metering system uses upstream ramp storage to postpone 

freeway congestion. Delft University researchers’ Vissim simulation HERO algorithm study 

showed that HERO-coordinated control outperformed noncoordinated ramp metering 

(Yuan et al. 2009). 

Amini (2015) said that ramp meters affect not only the performance of the motorway, but also 

the performance of the arterial road network. Use of the available literature directs 

implementation of the HERO ramp metering system as closely as possible using the Aimsun 

application programming interface. The implemented algorithm is evaluated using a networkwide approach. In addition, introduction of a set of novel measures facilitates the evaluation of 

the impacts of a ramp metering system on the entire network. 

The conclusion is that HERO provides a maximum mainline speed benefit of approximately 

34 percent over various motorway sections (for the specific network and demand profiles 

examined). However, the arterial speeds were negatively affected in certain cases (thereby 

establishing the need to examine broader metrics than the mainline motorway). For instance, the 

network-wide impact (considering both mainline and arterials) on total system travel time is 

found to range from -1.4 percent to 1.7 percent, further highlighting that the implementation of 

ramp metering could result in network improvements or deterioration depending on the specific 

case being examined, calibration “fine-tuning,” and the coordination with the arterial street 

network. In other words, even in some cases where the mainline improved, the overall system 

could suffer (e.g., if ramp metering is not optimally deployed with a consideration of the network 

impact). This highlights the need to consider metrics beyond only mainline conditions and 

stresses overall deployment, planning, and configuration. 

A study on a HERO pilot project at six consecutive on-ramps on the Monash Freeway in 

Melbourne, Australia, showed significant increase in throughput and reduction in travel times 

compared to the previous ramp metering system (Papamichail et al. 2010). Implementation of the 

system is now taking place at 63 on-ramps. Similarly, in a before-and-after study of a HERO 

pilot application, ramp metering with traffic-responsive feedback control strategy (HERO) 

outperformed fixed rate ramp metering systems and had a benefit–cost ratio of 13.8:1 

(Faulkner et al. 2014). 

Fuzzy Logic 

This is a system-wide ramp metering algorithm in use in Seattle, WA, and Miami, FL. An 

evaluation of the fuzzy logic ramp metering in 2000 in Seattle showed that compared to local 

metering and to the bottleneck ramp metering algorithm, the fuzzy algorithm had improved 

travel times and higher throughput system-wide (Taylor and Meldrum 2000). According to the 

report: 

The fuzzy logic algorithm metered more restrictively than the local algorithm or 

bottleneck algorithm when preventing a mainline bottleneck, secondary queue formation, 

or an excessive queue. In cases where there were no tangible system-wide benefits to 

metering more restrictively, the metering rates during fuzzy metering were higher than 

those of the local or bottleneck algorithms in order to increase the throughput. 
21 

The fuzzy logic algorithm takes traffic data from sensors and converts them to textual values in a 

process called “fuzzification.” Based on this, the controller determines the control actions which 

are defuzzified to metering rates (Taylor, Meldrum, and Jacobson 1998). 

Systemwide Adaptive Ramp Metering (SWARM)

California Department of Transportation (Caltrans) developed SWARM and implemented it in 

Orange, Los Angeles, and Ventura counties in the late 1990s; this system was later installed 

along six corridors in Portland, OR, in 2005. SWARM has two modes: global and local. 

Metering rates calculated from each mode determine the most restrictive one, which is applied. 

In the coordinated global mode, the future state of density is estimated from field data using 

regression and applying a Kalman filter to capture nonlinearity. The excess density (difference 

between forecast density and predetermined threshold value) is used to calculate the volume 

reduction at each detector station. The local mode uses real-time local traffic conditions at each 

ramp. Field experiments by Caltrans revealed that the SWARM algorithm increased mainline 

speed by 11 percent, decreased travel time by 14 percent, and reduced delay by 17 percent. Also, 

the queue lengths increased by more than 40 percent at the nine busiest on-ramps. A pilot study 

in Portland did not show significantly favorable results (Ahn et al. 2007). Total delay is found to 

have increased with SWARM, probably due to higher metering rates at most on-ramps.

DMC

It appears that DMC is not gaining much traction in the United States despite some European 

implementations. Virginia Department of Transportation (VDOT) performed detailed 

microsimulations of DMC in the Richmond area and found it is not an ideal solution (Schneider 

Electric and RK&K 2015). Jiang’s DMC simulation study found DMC to be hugely beneficial, 

but these results seem overly optimistic (Jiang et al. 2015). It is possible that congestion is 

simply moved upstream of the network entry nodes being simulated. DMC should only be 

beneficial when one can afford to remove a mainline lane without substantial negative 

consequences (a dubious proposition in most urban areas). In terms of the ramifications for the 

Alternative Freeway Designs at Merge and Diverge Segments project, deployment of DMC is 

conceivable in tandem with a strategy like ramp metering that assists the mainline at the expense 

of the ramp. In other words, one could dynamically switch back and forth between DMC and 

ramp metering depending on which upstream feeding road flows more favorably. Unfortunately, 

where both upstream feeding roads are congested, this seems costly and ineffective. In essence, 

DMC is a lane closure strategy, and it is difficult to envision an effective freeway merge (or 

diverge) strategy that would be based on lane closure. An ATDM study confirmed little benefit 

from DMC on its own, but some benefits were shown when combined with other ATDM 

strategies (Yelchuru et al. 2017). 

VSLs 

VSL systems utilize information on traffic speed, occupancy, volume detection, weather, and 

road surface conditions to determine the appropriate speeds at which drivers should be traveling 

(Katz et al. 2017). During adverse conditions, such as heavy traffic and adverse weather, the use 

of VSLs can improve safety by decreasing the risks associated with traveling at speeds that are 

higher than appropriate and by reducing speed variance in traffic. In addition, VSLs can be used 
22 

to dynamically manage speeds during planned (rush hour congestion) and unplanned (incidents) 

events. Used in conjunction with MLs and other active traffic management strategies, VSLs can 

respond to downstream congestion to eliminate or delay bottlenecks and mitigate the possibility 

of crashes. Based on a comprehensive literature review along with agency interviews to gather 

information on existing, deactivated, and planned VSL systems, this synthesis provides a 

comprehensive review of current practices on VSL operations, focusing on deployments in the 

United States. The synthesis identifies successful and best practices from the following 

perspectives: planning and policy, design, deployment, and standards, operations and 

maintenance, and outcomes. 

Cycle-based Variable Speed Limits (CVSLs) 

Figure 13 shows the CVSL strategy, which dynamically creates gaps in mainline traffic by 

frequently adjusting speed limits (Zhang et al. 2017). Although this would seemingly require 

CAV technology to be effective, it could work if implemented via dynamic traffic calming 

devices. Consideration of different intervals and durations of speed optimization could show that 

short intervals and durations of speed optimization are successful within the microsimulation 

experiments. If so, the final project report could recommend further research on dynamic traffic 

calming devices and CVSLs. 

Source: FHWA.

Figure 13. Illustration. The CVSL concept.

Design of MLs 

Stanitsas et al. (2014) evaluated shockwaves resulting from two ML access typologies: restricted 

access (I–394) and several points on continuous access (I–35W). Design of the first dynamically 

priced high-occupancy toll (HOT) lane used a closed access philosophy, meaning that for the 

greater length of the roadway, access to the HOT lane is restricted with only specific entry points 

over short-length sections. Specifically, one location on the southbound direction of I–35W 

causes the biggest shockwaves on the facility. The study concluded that it is difficult to compare 

the two design philosophies because they serve the needs of the two distinct roadways. The 
23 

researchers stated that it would have been difficult to follow a closed access design on I–35W, 

and their research results led them to conclude that it would have made little difference in terms 

of mobility and safety. Comparisons of shockwave characteristics of four access zones show 

that, although the volumes at each access zone were different, the shockwave lengths observed 

were comparable, indicating no difference in terms of safety between the two design 

philosophies. California’s most recent designs recommend openings of 2,000 ft in length, 

including dimensions for lane-change maneuvers to adjacent entrance and exit ramps (California 

Department of Transportation 2011). 

The HCM contains a methodology for evaluating the operational performance of MLs 

(Transportation Research Board 2016). The ML analysis is typically performed as part of a 

freeway facility analysis, which encompasses a 10–15-mi-long facility and evaluates its 

operational performance over multiple time periods. Figure 14 distinguishes between five ML 

segment types. 

© 2012 National Cooperative Highway Research Program.

Figure 14. Illustration. ML segment types in the HCM (Wang et al. 2012). 

The HCM ML method was initially developed as part of the National Cooperative Highway 

Research Program (NCHRP) Project 03-96, and is documented in NCHRP web-only 

document 191 (Wang et al. 2012). The research collected data at ML facilities across the United 

States and developed speed-flow relationships of various ML segment types. The methodology 

distinguishes between three separation types, including stripe-only separation, painted buffer 

separation, and physical barrier separation. Figure 15 illustrates the separation types and shows 

two-lane options for buffer and barrier separation. 
24 

© 2010 Transportation Research Board.

Figure 15. Illustration. ML separation types in the HCM

(Transportation Research Board 2010). 

The HCM discusses separate speed-flow relationships developed for each separation type, as 

well as for one-lane versus two-lane ML facilities. For facilities with only paint or buffer 

separation, the HCM further defines an “adjacent friction effect,” which lowers the speed-flow 

curves in the MLs when GP lane densities exceed 35 passenger cars per mile per lane. In other 

words, the performance of the MLs is impacted by congestion in the adjacent GP lanes. 

The HCM ML method also includes a new type of weaving maneuver, referred to as the 

“cross-weave effect.” The cross-weave effect quantifies the capacity reducing impacts of the 

two-sided weaving maneuver between a GP on-ramp and an ML access point (or vice versa). 

Figure 16 illustrates the cross-weave effect. The HCM ML method is limited in its ability to 

evaluate impacts of specific ML terminus configurations, which is beyond the scope of the 

original project. As a result, no specific guidance is given in the HCM about the performance of 

different access point geometries or ML start or end points. 

© 2012 National Cooperative Highway Research Program.

Figure 16. Illustration. ML cross-weave effect (Wang et al. 2012). 
25 

Summary

The brainstorming exercise for conceiving alternative freeway designs produced 12 viable 

strategies for this research project. In the next stage of the project, the team needed to create a 

shorter list of alternative strategies for microsimulation-based sensitivity analysis. Categories of 

the strategies included the following: 

• Innovative geometric designs. 

• Vehicle speed or speed limit control. 

• Dynamic lane closures. 

• Advanced or alternative freeway signalization. 

• ML design. 

• Advanced signage. 

A literature review conducted in parallel found few innovative geometric designs but a fair 

number of sources in the other categories. This literature review helped to expand and finalize 

the list of viable strategies.

27 

CHAPTER 3. RESEARCH WORK PLAN

The researchers developed a comprehensive research work plan for data collection and analysis

after reviewing viable strategies and the relevant literature. The plan included approaches to 

modeling the traffic in microsimulation and calibrating the existing conditions for all selected 

freeway segments. The plan also described a search for and selection of several sites 

experiencing freeway merge/diverge deficiencies and speed-change areas resulting in capacity 

reduction. Similarly, for the left-side ML problem, the team identified sites with open-access 

HOV or HOT lanes that have an ML located on the left-hand side of the freeway cross section. 

The intent is to expand the use of these sites for generic analyses and recommendations. 

The team began with discussion of a shorter list of five top-candidate strategies, listed and 

described below, that the group identified and down-selected during task 2: synthesis of research 

and findings. The strategies are:

• Acceleration and deceleration lane designs.

• Speed optimization (e.g., via dynamic traffic calming devices).

• Mainline metering (i.e., dynamic signal control).

• Coordinated adaptive ramp metering system (e.g., HERO).

• Open-access MLs on the right-hand side.

ACCELERATION AND DECELERATION LANE DESIGNS

Restricting merging drivers to a specific new set of acceleration and deceleration lane locations 

may help to smooth traffic flow. Regarding acceleration lanes, figure 3 shows how drivers are 

prohibited from merging at the midsection of acceleration lanes. By eliminating the uncertainty 

of merge maneuvers for both mainline and ramp drivers, both sets of drivers can prepare for and 

execute the merging maneuver efficiently. Regarding deceleration lanes, figure 4 shows how 

drivers merge at a mid-section of the off-ramp. Separating the turbulence associated with diverge 

maneuvers potentially mitigates mainline congestion.

SPEED OPTIMIZATION (E.G., VIA DYNAMIC TRAFFIC CALMING DEVICES)

Speed harmonization and VSL studies for optimizing freeway traffic flow imply that medium 

free-flow speeds (e.g., 30 mi/h) would be more effective than low speeds (e.g., 5 mi/h) or high 

speeds (e.g., 65 mi/h). Although this project endeavored to focus on strategies not requiring 

CAV technologies, the team believes that dynamic traffic calming devices (e.g., retractable 

rumble strips) may someday be capable of coercing drivers to obey medium speeds at higher 

rates of compliance. Figure 8 illustrates this concept. 

MAINLINE METERING (I.E., DYNAMIC SIGNAL CONTROL) 

A form of mainline metering to reduce demand and improve flow uses traffic metering upstream 

from a bottleneck, thereby regulating the number of vehicles moving through the bottleneck. At 

locations where capacity expansion is not feasible (i.e., at tunnels and bridges), implementation 

of this form of mainline metering can help. For example, metering for traffic arriving at the 
28 

bridge or tunnel is used at the Bay Bridge in Oakland, CA; the Hampton Roads Bridge-Tunnel 

connecting Hampton, VA, and Norfolk, VA; and the Baltimore Harbor Tunnel in Baltimore, 

MD. 

In situations where mainline and ramp vehicle speeds are both very low, and fall below a defined 

threshold, such as 5 or 10 mi/h, traffic signal control applied to both upstream approaches could 

optimize the operation by eliminating merge friction. Figure 9 illustrates this concept. However, 

there is no known application of the treatment.

COORDINATED ADAPTIVE RAMP METERING SYSTEM (E.G., HERO)

Traditional ramp metering measures upstream freeway mainline volume to determine ramp flow, 

but rarely monitors downstream conditions. Downstream problems only show when congestion 

reaches the upstream ramp detector. Coordinated adaptive ramp metering uses feedback logic in 

a closed loop control system. Traffic conditions measured at downstream bottlenecks determine 

critical occupancy and appropriate levels of traffic entering from upstream. The process assumes 

a historic or theoretical value of freeway capacity. Downstream flow conditions provide 

feedback to determine real-time ramp flow and optimal occupancy. When applied on a holistic, 

coordinated, system-wide basis, all ramps continuously communicate with each other to resolve 

complex traffic flow situations. One example of such a system is called HERO, but the team 

identified several similar algorithms and systems during the task 2 effort.

OPEN-ACCESS MLs ON THE RIGHT-HAND SIDE

Typically in the United States, managed HOV/HOT lanes provide an improved level of service 

to drivers and are at the far left-hand side, next to the median. Although locating the ML on the 

right-hand side would reduce its capacity, the overall impact on all lanes could be beneficial. 

This project compared right-side ML designs to left-side designs (i.e., with continuous access or 

partial access). Reduction by 300 veh/h in the overall capacity of a four-lane freeway occurs if 

the right-side ML capacity is reduced from 2,000 to 1,700 veh/h, and the other three lanes remain 

at 2,000 veh/h. However, left-side MLs can result in a much larger capacity loss since all lanes 

can experience the 300 veh/h capacity loss due to vehicles weaving from the right to the left. 

Even if only the middle two lanes were impacted by this weave friction and turbulence, there 

may be a capacity loss of 300 x 2 = 600 veh/h, or twice that of a right-side ML. 

SITE SELECTION

To test the alternative designs noted above, the team considered the following site selection 

criteria: 

• Exhibits one of the two typical merging problems (i.e., right-side on-ramp, left-side ML)

described by the project scope of work.

• Does not currently employ active transportation management (ATM) strategies or already

have reconstruction plans.

• Has nearby stakeholders supportive of pursuing innovative treatments.
29 

• Has existing calibrated microsimulation datasets that realistically replicate car following 

and lane changing operations (or readily available data to support coding of new 

datasets). 

• Well described in project reports and papers in terms of existing conditions, maps, 

endpoints, and so on. 

Table 1. Site selection matrix of characteristics.

Site Merge ATM Support Datasets Details

I–270 MD — None Yes Vissim (calibrated) Yes

I–95 PA Both 

(no ML) None — Vissim (uncalibrated) Yes

I–35 KS Right side Ramp 

meters Yes Vissim (uncalibrated) Yes

I–66 VA Both — — Vissim (calibrated) Yes

US 95 NV — None — Aimsun (uncalibrated) Yes

I–15 CA Both None — Aimsun (calibrated) Yes

I–35W MN(1) — — Yes Aimsun (calibrated) — 

I–394 MN(1) Both — Yes Aimsun (calibrated) — 

I–94 MN — — Yes Aimsun (calibrated) — 

I–35W MN(2) Both None Yes TSIS-CORSIM 

(calibrated) Yes

I–394 MN(2) — — Yes TSIS-CORSIM 

(calibrated) Yes

C–470 CO Right side — Yes (Possibly) Yes

I–5 WA Right side None Yes Yes Yes

SR 535 FL Right side None Yes Vissim — 

—No data. 

Note: Numbers in parenthesis indicate particular run of simulation model. 

Table 1 reveals some mismatches between sites having local support and sites where usable 

datasets are currently available. During the 3-mo task 3 effort for data collection and model 

calibration (i.e., October 12, 2017–January 12, 2018), the team endeavored to find local support 

for innovative solutions and to find data (or datasets) at sites where local support already exists. 

The team accepted support offered by Washington State, Colorado, and Maryland. As shown in 

table 1, the initial datasets provided mostly included calibrated or uncalibrated microsimulation 

models. For the sites that were selected to test the alternative designs, the team ensured that the 

simulation models were calibrated properly to site conditions by adjusting car following and/or 

lane changing behavior. Detailed calibration information along with calibration results are 

provided in the appendix. 

The specific sites selected for potential use in the study were:

• I–270 MD. 

• I–95 PA. 

• I–35 KS. 

• I–66 VA. 
30 

• US 95 NV.

• I–15 CA.

• I–35W MN.

• C–470 CO.

• I–5 WA.

I–270 MD 

This project used a calibrated I–270 MD Vissim dataset developed for the Federal Highway 

Administration (FHWA) Narrowing Freeway Lanes and Shoulders to Create Additional Travel 

Lanes project. The dataset was recently exported from Visum (a macroscopic simulation tool, 

unlike Vissim), which implies that it might not be calibrated. Moreover, because the Narrowing 

Freeway Lanes and Shoulders to Create Additional Travel Lanes project developed the dataset, it 

might not satisfy criterion A listed above. Separately, FHWA and the Maryland State Highway 

Administration provided an I–270 MD Vissim dataset with static demands and an accompanying 

calibration report1

. 

I–95 PA 

The team identified an I–95 facility exhibiting right-side merge problems and an available 

Vissim dataset. The team contacted Pennsylvania and Delaware officials to solicit local support. 

In 2011, Vissim models were developed in Pennsylvania Department of Transportation 

(PennDOT) District 6 for the a.m., p.m., and weekend peak hours. The study area extends 

approximately 16 mi along I–95, from the Delaware State line to the Schuykill River. The 

following interchanges were also included: 

• Chichester Avenue interchange (two unsignalized ramp terminals).

• Market Street interchange (one signalized and one unsignalized ramp terminal).

• US 322 Conchester Highway interchange (one signalized and one unsignalized ramp

terminal).

• US 322 Barry Bridge approach (system interchange).

• Kerlin Street ramps (ramp terminals not modeled).

• Edgemont Avenue interchange (clustered signal at Edgemont Avenue).

• Chestnut Street interchange (ramp terminal not modeled).

• I–476 interchange (system interchange).

• Stewart Avenue interchange (two signalized ramp terminals).

• Wanamaker Avenue interchange (cloverleaf interchange).

• Airport area (see model for specific details).

• Bartram Avenue interchange (signalized ramp terminal).

• Enterprise Avenue ramps.

1

I–270 Modeling Calibration Methodologies Memorandum.pdf 
31 

Figure 17 through figure 19 show that the study corridor consists of several right- and left-side 

merge and diverge locations; however, it does not include MLs. Traffic volumes for the mainline 

of I–95 and all ramps were developed by using two sources of data: 

• Hourly mainline counts at select locations collected by the Delaware Valley Regional

Planning Commission, the region’s metropolitan planning organization.

• Origin–destination (O-D) data for each interchange recorded using Bluetooth data. The

O-D data consisted entirely of percentages of vehicle routes and not counts. Separate O-D

data are provided for each time period (a.m., p.m., and weekend).

The combination of hourly mainline counts with the O-D percent data allowed computing all 

volumes in the study area, maintaining the O-D route pattern structure. This ensured that the 

weaving behavior and its effect on capacity were better captured in the simulation, particularly at 

the merge/diverge locations. Vissim models used static demand data (i.e., not varying within the 

peak period). The model includes 7,200 s of demand data, where the first 3,600 s comprises the 

“warm-up” period. The heavy vehicle proportion is 9 percent in both the a.m. and p.m. peaks. 

Original photo: © 2017 Microsoft® (see acknowledgements).

Figure 17. Map. Southern part of the I–95 Vissim study corridor in PennDOT District 6.
32 

Original photo: © 2017 Microsoft® (see acknowledgements).

Figure 18. Map. Middle part of the I–95 Vissim study corridor in PennDOT District 6.

Original photo: © 2017 Microsoft® (see acknowledgements).

Figure 19. Map. Northern part of the I–95 Vissim study corridor in PennDOT District 6.

Field Observations and Bottleneck Locations 

The study corridor experiences both right side and left side merge and diverge issues. For some 

of the on-ramp locations, relatively short merge on-ramps further exacerbate the weaving 

problem. As part of the initial modeling project, the team conducted site visits to observe 

operations from the field. Although the site visits took place in October 2011, the problem 

locations identified as part of the 2011 study still existed for this project based on 2017 

Google® MapsTM traffic information and estimated travel times along the corridor during the 

peak periods. Google® MapsTM provided the data and detailed information discussed below. 

Google® MapsTM traffic information and field visits indicated the bottleneck locations in the 

a.m. peak. Field observations indicated northbound congestion prior to Exit 3 where traffic

speeds drop approximately to 30 mi/h due to the left-side US 322 on-ramp. High traffic volumes

and a relatively short merge area are the primary sources of congestion. While the observations

are from 2011, 2017 Google® MapsTM traffic prediction showed similar patterns. Figure 20 and

figure 21 display both the ramp configuration in Vissim and present year estimated travel time

and bottleneck locations from Google® MapsTM.
33 

Original photo: © 2017 Microsoft® (see acknowledgements).

Figure 20. Map. Northbound US 322 left-side merge in Vissim.

Original photo: © 2017 Google® (see acknowledgements).

Figure 21. Map. Estimated travel times in the northbound direction during the a.m. peak: 

estimated travel time for a typical Wednesday around 8 a.m. 

Field observations from 2011 in conjunction with 2017 Google® MapsTM traffic data indicated 

that the I–476 interchange is another bottleneck in the corridor in the southbound direction 

during the a.m. peak. Figure 22 and figure 23 show the ramp configuration in Vissim and the 

estimated southbound travel time along the corridor on a typical Wednesday around 8 a.m. and 

highlights bottleneck locations. The congestion resulted from the I–476 on-ramp and the weave 

between it and the exit 6/Providence Avenue/Edgmont off-ramp.
34 

Original photo: © 2017 Microsoft® (see acknowledgements).

Figure 22. Map. Southbound I–476 right-side merge in Vissim.

Original photo: © 2017 Google® (see acknowledgements).

Figure 23. Map. Estimated travel times in the southbound direction during the a.m. peak: 

estimated travel time for a typical Wednesday around 8 a.m. 

Google® MapsTM traffic information and field visits indicated the bottleneck locations in the 

p.m. peak. Similar to the a.m. peak, field observations indicated that the US 322 left-side 

on-ramp is a source of congestion, resulting in lower speeds and stop-and-go conditions during 

certain times. The observations from 2011 were also confirmed with the estimated travel time
35 

from Google® MapsTM, as illustrated in figure 24, indicating heavy congestion in the northbound 

direction resulting from the US 322 on-ramp and weaving. 

Original photo: © 2017 Google® (see acknowledgements).

Figure 24. Map. Estimated travel times in the northbound direction during the p.m. peak: 

estimated travel time for a typical Wednesday around 5 p.m.

Field results indicated that the I–476 interchange appeared to be the primary source of 

congestion. According to the 2011 field observations, I–95 traffic flowed around 30–40 mi/h

through the interchange. Figure 25 shows similar Google® MapsTM traffic information findings. 

Original photo: © 2017 Google® (see acknowledgements).

Figure 25. Map. Estimated travel times in the southbound direction during the p.m. peak: 

estimated travel time for a typical Wednesday around 5 p.m. 
36 

Data Needs 

Since the 2011 development of the initial Vissim model, including demand data, the data need 

updating to reflect existing conditions more accurately. Use of Bluetooth provided the O-D data. 

Conversion of these data within Visum gave both entry volumes and routing decisions within 

Vissim. Given the extent of the study corridor and the comprehensive data collection efforts as 

part of the previous effort, the team developed 2017 volumes by applying a “global” growth rate. 

This allows for maintaining the originally developed O-D demand structure while simulating 

traffic volumes closer to the field values.

For the development of the global rate, the latest average daily traffic (ADT) data comparison of 

the 2011 ADT data, combined with local knowledge and engineering judgment, can help 

estimate a growth rate along the corridor. Where available, the team will also compare a.m. and 

p.m. peak hour or peak period traffic data to identify whether this is a big discrepancy between 

the a.m. and p.m. peak growth rates.

The calibration used collected travel time (see below for details). The Regional Integrated 

Transportation Information System (RITIS) platform was used to obtain travel time results from 

the corridor. 

Calibration Approach 

As part of the calibration process, the team used the travel time information obtained through 

probe data. Simulation runs were performed 10 times with a modeling resolution of 10 time steps 

per second. Performance of each model run gives a total of 7,200 s, with the first 3,600 s being 

used as the warmup period. For the calibration, the team followed the guidance provided in the 

FHWA Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation 

Modeling Software (Dowling et al. 2004). 

In addition to the travel time calibration, the team ensured Vissim yields realistic capacity 

outputs at the bottleneck locations. For segments where freeway capacity was found to be high 

(or low), car following and lane changing driving behavior parameters were updated in Vissim to 

decrease (or increase) freeway capacity. The team also observed driving behavior at 

merge/diverge locations to capture friction accurately along the freeway. Calibration details, 

including the results, are discussed in the appendix. 

The Vissim model was calibrated for the p.m. peak conditions. The team reviewed existing 

traffic volumes including directionality and volume profiles as well as bottleneck locations 

(e.g., speed profiles through probe data) to determine the peak that would be most useful for the 

testing of innovative strategies. Since there would be additional sensitivity analysis as part of the 

analysis, the primary objective of the calibration effort was to setup a realistic base model that 

reflects actual traffic conditions.

I–35 KS 

The team obtained permission from Kansas Department of Transportation (KDOT) to use their 

I–35 Vissim model. This facility is approximately 10 mi long and it consists of several 

interchanges. The bottlenecks at the southbound direction are located at Southwest Boulevard 
37 

(merge), Shawnee Mission Parkway (weave), and 67th Street (merge). The last two bottlenecks 

interact with each other and it appears that queue from 67th Street propagates upstream to 

Shawnee Mission Parkway, and even further upstream (spillback reaches the Metcalf Avenue 

interchange). At the southbound direction, KDOT is operating ramp metering at 7th Street, 

Southwest Boulevard, 18th Street, and 67th Street. 

Northbound, the facility experiences recurrent congestion at the 87th Street interchange (merge). 

In that direction, ramp meters operate at the 7th Street on-ramp and at Johnson Drive. Figure 26

shows a map of the facility, the congestion areas, and the locations of the ramp meters. The 

bottleneck locations had only two of the six ramp meters installed in the summer of 2017. The 

algorithm currently used is the Continuous Association Rule Mining Algorithm (CARMA) 

algorithm—a system-wide algorithm based on speeds. KDOT decided to switch to another 

system-wide algorithm that uses occupancies as thresholds for metering rate determination. The 

exact specifications of the new algorithm are not known to date. However, given that the metered 

ramps are not consecutive, it is not possible to leverage the full capabilities of a system-wide 

ramp metering algorithm. 

Original photo: © 2017 Google® (see acknowledgements).

Figure 26. Map. I–35 Kansas City, KS, facility.
38 

KDOT provided the Vissim simulation network of the I–35 corridor, and the team performed the 

model calibration task. The facility is equipped with radar sensors. Field data at those sensor 

locations along the freeway and the on-ramps are available to the researchers through Kansas 

City Scout (Kansas DOT and Missouri DOT 2018). 

I–66 VA 

Various research projects use the I–66 VA Vissim dataset extensively. VDOT has multiple ATM 

deployments along the corridor. Currently, VDOT is upgrading I–66 outside I–495 (the Capital 

Beltway) to include three regular lanes in each direction and two express lanes in each direction 

from the Capital Beltway to Gainesville, VA. Calibration of the existing Vissim simulation 

network takes place before the upgrade, but the team still can use the dataset in the sensitivity 

analysis experiments. The simulation network is a 13-mi stretch of I–66 outside the Capital

Beltway. This is a major east-west commuter corridor near Washington, DC, with four lanes in 

each direction. This stretch of the freeway experiences recurring congestion westbound in the 

p.m. peak every weekday. Figure 27 illustrates the layout of the road. The existing HOV lane has 

open access to the adjacent lanes with two HOV-only entrances/exits.

© 2016 Google®. 

Figure 27. Map. Illustration of the I–66 freeway testbed.

The network further contains the following characteristics: 

• Start: mile marker 64 (intersection with I–495). 

• End: mile marker 51 (intersection with US 29). 

• Length: 13 mi. 

• Number of Interchanges: six. 

• Average distance between interchanges: 2.3 mi.

• Number of lanes: four lanes/direction.

• Full length lanes: three (in some sections, the rightmost lane turns into a hard shoulder). 

The team collected field data to build time-dependent travel demand O-D matrices for Vissim. 

This included collecting traffic counts and speeds on each individual lane, including HOV lanes, 

for the mainline, on-ramps, and off-ramps. A combination of remote traffic microwave sensor 

(RTMS) radar detectors, video cameras, portable action cameras, and manual counts provided 

speeds, volumes, and occupancies. Figure 28 shows their deployment locations. The QueensOD 

software, a model for estimating O-D traffic demands, provided estimates of the O-D matrices
39 

(Van Aerde 1993). Results indicate an excellent correlation between field-measured volume and 

volume estimated from using O-D trips in simulation, as shown in figure 29. 

Original photo: © 2017 Google® (see acknowledgements).

Figure 28. Map. Locations of RTMS trailers and portable action cameras.

Source: FHWA.

Figure 29. Chart. Comparison of estimated flow and observed flow from 3:00–3:15 p.m. 

I–15 CA 

The team applied coordinated adaptive ramp metering strategies in complex networks, such as 

San Diego, CA, and locations around the world. The team will utilize the San Diego, CA, I–15 

network for testing ramp metering strategies. This network was modeled using Aimsun (2019). 

The placement of ramp meters is ideal for testing adaptive ramp metering algorithms. The other 

network the team will utilize for this task is the San Diego, CA, ICM network shown in 

figure 30. This network has been developed and calibrated. 
40 

© 2019 Aimsun.

Figure 30. Map. San Diego, CA, I–15 network.

I–35W MN 

The Minnesota Department of Transportation (MnDOT) has 85 percent of their freeway system 

covered by TSIS-CORSIM simulation models, a freely available microscopic simulation 

software tool. Calibration of the I–35 dataset is described within Xiao et al. (2015). MnDOT has 

a public access area for retrieval of their loop detector data. Some of these date back to the early 

1990s. 

The first link of the MnDOT Traffic Operations website under “How?” goes to the freeway data 

site (the traffic surveillance line) (Minnesota Department of Transportation 2019). At the top of 

the next screen, there are three items highlighted in blue: 

• The “DataPlot” program allows the user to examine different detector data.

• The “DataExtract” program allows the user to filter and extract the freeway data in a

number of ways.

• The “All Detector Report” lists all of the detectors and their locations.

C–470 CO

C–470 stretches from Quincy, CO, to Morrison, CO. US 285 falls within these limits (figure 31). 

There is no ML on this corridor yet since it is a long-range plan. US 285 comes onto C–470 as a 

lane add, but just north of the lane add is a buttonhook ramp (figure 32) that provided the major 
41 

merging conditions needed for this project. FHWA also provided a document to explain their 

calibration procedure (Cambridge Systematics, Inc. 2017). 

© 2018 Colorado Department of Transportation.

Figure 31. Photo. Aerial view of C–470 at US 285. 

© 2018 Colorado Department of Transportation.

Figure 32. Photo. Buttonhook ramp near the junction of C–470 and US 285. 
42 

I–5 WA 

At the I–5/US 101 interchange in Olympia, WA, (figure 33) peak hour I–5 southbound p.m. 

movement creates congestion that backs up for miles each weekday. Washington State 

Department of Transportation (WSDOT) has a microsimulation model and a dynamic traffic 

assignment model available for this area. There are no MLs or ATM strategies through this 

stretch, but WSDOT is currently working on a study to develop operational/transportation 

systems management and operations/transportation demand management strategies.

© 2017 Google®. 

Figure 33. Map. Aerial view of I–5 at US 101. 

EXPERIMENTAL APPROACH

The task 3 timeframe (October 12, 2017–January 12, 2018) was devoted to calibration of 

simulation datasets to existing conditions and coordination with interested agencies. To the 

extent possible, the team calibrated existing simulation datasets and/or acquired calibrated 

datasets, as opposed to coding new datasets from scratch. The task 4 timeframe (January 12, 

2018–July 12, 2018) was devoted to simulating the proposed alternative designs. The goal is to 

demonstrate effectiveness of the alternative designs at real-world locations, using the task 3 

collection of calibrated datasets.

The team also decided to use relatively simple networks as the first step of the simulation 

evaluation. For example, the evaluation of strategy 1 (acceleration and deceleration lane designs) 

can benefit by using a simple straight segment with an on-ramp or off-ramp. On one hand, the 

evaluation avoids potential influence of confounding factors (e.g., roadway curvature) on the 

results. On the other hand, simulations on simple segments are faster than on large, real-world 

networks, and the evaluation can benefit by running a large number of scenarios (e.g., factorial 

design of system parameters, such as demand volume, speed limit, and distance between two 

access points) to obtain systematic insights of the new, innovative geometric treatment.

Simulation data collected from the large number of simulation runs were then analyzed using 

advanced statistical methods, such as analysis of variance (ANOVA) and post-hoc tests, to 

understand the effects of various system parameters. This step can help the team understand how 
43 

to optimize the design of the new treatment and under what circumstances (e.g., traffic volume) 

the treatment can be most effective. After obtaining these insights, applying the new treatment’s

optimal design to a selected site will provide further evaluation capabilities, dependent on the 

attributes of the selected site. In the base case, the new treatment will enhance the calibrated 

simulation network. Based on results from the simulation on simple networks, the team can 

identify the most relevant design scenarios. The team conducted simulation runs of the selected 

scenarios to confirm effectiveness of the new treatment when deployed at the selected site. 

Managerial insights gave the local agency understanding for designing the treatment and its 

potential effectiveness.

Input and Output Parameters

The following inputs provided sensitivity analysis during the simulation studies: 

• Demand volumes (ramp and mainline).

• Car following/lane changing aggressiveness.

• Heavy vehicle percentage.

• Acceleration/deceleration lane geometries (strategy 1).

• Speed limit (strategy 2).

• Metering rate/timing (strategies 3 and 4).

• Algorithms, detection strategies (strategy 4).

• ML access point locations (strategy 5).

• Random number seeds.

The team progressively determined the exact number of runs and variations after they conducted

their preliminary runs. 

The team examined the following output performance measures during the simulation studies:

• Network-wide outputs.

• Segment-specific outputs (on both ramps and mainlines).

o Throughput.

o Travel times.

o Speeds (using heat maps sometimes).

o Delays.

o Congestion duration.

o Latent delays and queues.

o Number of lane changes.

Prioritization of Strategies

The team ultimately assigned three simulation modelers who completed a thorough analysis of 

the five top-candidate strategies during the available 6-mo window. This made prioritization of 

the strategies unnecessary during the sensitivity analysis phase of the project. The sensitivity 

analysis results ultimately implied that some strategies were more promising in terms of 

cost-effectiveness, as discussed in the Executive Summary and chapter 5.

45 

CHAPTER 4. SENSITIVITY ANALYSIS EXPERIMENTS

Following a 3-mo data collection and simulation model calibration process described in the 

appendix, the team carried out the main 6-mo period of simulation sensitivity analysis 

experiments. These experiments would determine which alternative design strategies show the 

most promise for improving traffic flow at freeway merge and diverge locations across the 

country. The work plan for conducting these experiments is described in chapter 3. This chapter 

describes the experimental parameters and results that cover:

• Acceleration and deceleration lane designs.

• Speed optimization.

• Mainline metering.

• Coordinated adaptive ramp metering system.

• Open-access MLs on the right-hand side.

ACCELERATION AND DECELERATION LANE DESIGNS

Restricting merging drivers to a specific new set of acceleration and deceleration lane locations 

may help to smooth traffic flow. Regarding acceleration lanes, figure 3 shows how drivers are 

prohibited from merging at the mid-section of acceleration lanes. By eliminating the uncertainty 

of merge maneuvers for both mainline and ramp drivers, both sets of drivers can prepare for and 

execute the merging maneuver efficiently. Regarding deceleration lanes, figure 4 shows how 

drivers merge at a mid-section of the off-ramp. Separating the turbulence associated with diverge 

maneuvers potentially mitigates mainline congestion.

This study uses microscopic simulation to evaluate the effectiveness of the alternative geometric 

design of split merging, diverging, and weaving for reducing bottleneck congestion. Making 

geometric changes to create two merge or diverge points (as well as multiple points for weaving) 

at off-ramp areas spatially distributes potential conflicts for mainline and ramp traffic. This can 

potentially decrease the unnecessary interactions between the mainline and ramp vehicles and 

relieve congestion at these bottleneck areas with limited additional construction. This provides 

managerial insights for State departments of transportation (DOT) to better design, enhance, and 

manage their freeway systems in the future. 

Experimental Design

The simulation experiments used two sets of simple, synthetic networks to study the 

effectiveness of the proposed split design while excluding confounding factors that can exist in 

real-world segments (e.g., effects of the nearby downstream merge area). The first simulation 

network set represents one type of geometry, such as merge or diverge. The simulation also uses

a calibrated I–66 network by converting selected merge areas with congestion problems to the 

proposed split merge design. The goal is to test the proposed split design in a complex 

environment and inform the State DOT concerning the design potential. These simulations only 

evaluated one-lane ramps, but the researchers believe two-lane ramps would exhibit similar 

impacts.
46 

Figure 34 provides sketches of the proposed split merge and diverge areas used in the simulation. 

It shows the approximate length of the synthetic network (mainline) is 3.35 mi for the merge 

simulation and 3.21 mi for the diverge simulation. The assumed simulation traffic speed is 

approximately 70 mi/h. The American Association of State Highway and Transportation 

Officials’ (AASHTO) book, A Policy on Geometric Design of Highways and Streets, 

(the AASHTO Green Book) recommends 30 mi/h for the lower range on-ramp and off-ramp 

speeds, 0.25 mi for the length of acceleration lane, and 0.1 mi for the length of the deceleration 

lane (AASHTO 2018). The team conducted the simulations for merge (on-ramp area) and 

diverge (off-ramp area) separately to see how different factors would affect operations of the 

network under the split merge/diverge design.

© 2019 Jiaqi Ma.

A. On-ramp benchmark.

© 2019 Jiaqi Ma.

B. On-ramp alternative design.

© 2019 Jiaqi Ma.

C. Off-ramp benchmark.

© 2019 Jiaqi Ma.

D. Off-ramp alternative design.

Figure 34. Illustrations. Split merge and diverge areas on a synthetic simulation network.

Figure 35 shows a possible geometry for the weaving segment, which is a usual urban case 

where the distance between the on-ramp and off-ramp is about 1.0 mi. It is the basic design of 

weaving segment, as defined in the HCM, where both on-ramp and off-ramp vehicles need to 

make at least one lane change to reach their destination. In addition, the length between the 

ramps is usually less than a maximum length, beyond which the merge and diverge area may not 

affect each other and the network reduces to the simple combination of the merge and diverge. 

Figure 35 is essentially a full split weaving design. An alternative geometry is given by the 

partial split weaving, for which only one of the merge or diverge is converted to the split design. 

The hypothesis of this study is that it is only necessary to adopt the full split weaving if both split 

merge and diverge designs are effective in improving the bottleneck performance. 
47 

© 2019 Jiaqi Ma.

A. Weave benchmark design.

© 2019 Jiaqi Ma.

B. Weave alternative design.

Figure 35. Illustrations. Two designs for the synthetic simulation network geometry 

(weaving). 

Studying factors that can influence the effectiveness of the proposed split design include three 

additional key factors in the simulation-based sensitivity analysis: the mainline congestion 

(volume-to-capacity ratio (V/C)), ramp traffic volume, and the length between the gores 

(connection spacing). Table 2 lists different levels of the three factors. For the merge or diverge, 

there are 81 scenarios in total; for each scenario, the team performed 10 simulation runs. 

Table 2. System parameters considered in this study.

Parameters (Units) Levels Number of 

Levels

Mainline input volume—

VolPerLane (veh/h/l) 

1,221 (V/C = 0.6); 1,832 

(V/C = 0.9); 2,443 (V/C = 1.2) Three

On-ramp volume—VolMerge 

(veh/h/l) 500; 1,000; 1,500 Three

Off-ramp volume—VolDiverge 

(veh/h/l) 500; 1,000; 1,500 Three

Connection spacing—ConSpace 

(mi)

0.25, 0.375, 0.5 (merge) 

0.125, 0.25, 0.5 (diverge) 

Three

Weaving area length (mi) 1 One

After initial data collection on the synthetic simulation network (with specified driver parameters 

used in previous studies and networks), the team determined the freeway mainline capacity of 

the highway in Vissim at 2,036 veh/h/l. Therefore, the team set the three input volumes: 

1,221 (V/C = 0.6); 1,832 (V/C = 0.9); and 2,443 (V/C = 1.2) to test the alternative design under 

different mainline congestion levels. Ramp traffic volume usually affects the number of lane 

changes directly, which indicates the intensity of disruptions to the original mainline traffic. The 
48 

team used different on/off-ramp volumes of 500 veh/h/l; 1,000 veh/h/l; and 1,500 veh/h/l to 

indicate low, medium, and high levels of ramp traffic.

The length between the gores (connection spacing), can also affect the performance of the split 

design. Long connection spacing can have two distinct effects: reduce disruption and reduce 

throughput. It can reduce the disruption of on-ramp vehicles on the mainline traffic by 

distributing the disruption to two locations. However, the vehicles on the frontage road travel at a 

lower speed than on the parallel freeway, and this can reduce the throughput. Therefore, the team 

set three connection spacing values for merge or diverge and conducted sensitivity analysis to 

understand their effects. 

The team used two performance measures: average delay and total throughput. The average 

delay reported in this study is the average travel delay for individuals, while the total throughput 

is the sum of mainline and on/off-ramp throughputs under different scenarios. In the final 

statistical analysis, comparing the performance measures between the benchmark and split 

design across different scenarios provides understanding of the effectiveness of the proposed 

split design. 

Synthetic Network Results

Table 3 presents the simulation results of the synthetic simulation networks for the split merge

design. The team reported three values for each scenario: the absolute value, the absolute 

difference, and the percent difference. The team found that the split merge design solution can 

generally increase the throughput and reduce the average delay, except when the sum of the 

mainline and ramp traffic volumes are low (e.g., V/C = 0.6, V/C = 0.9, and ramp volume = 

500 veh/h). Under light traffic conditions, there are few conflict points between the mainline and 

on-ramp traffic and therefore the split merge design, whose effect mostly comes from the spatial 

distribution of the conflict points, may not be significantly effective. When the traffic volumes 

are at the medium or high level, the team can see substantial improvements in terms of both 

throughput and average delay in many cases. 

For all of the congested cases, the team found that the best bottleneck performance occurs when 

the connection space is 0.25 or 0.5 mi; longer connection spacing (e.g., 0.5 mi) deteriorates the 

performance. The team explained the sensitivity analysis of the lengths of connection spacing as 

follows: 

• When the connection spacing is short, the traffic that arrives at the second conflict point

is still less than at the regular merge conflict point and may still be traveling slowly.

Therefore, a small disturbance at the second conflict point may not cause problems.

• If the connection spacing is long, the traffic that arrives at the second conflict point may

have already resumed full travel speed and the volume that has already resumed their full

speed is already relatively high. In this case, the disturbance from the second conflict area

may have a relatively significant negative impact on the mainline arriving traffic.

Based on these results, the team recommend short connection spacing to maximize operational 

benefits. This also helps reduce the construction and right-of-way costs by occupying less land 

space.
49 

Table 3. Simulation results for split merge connection spacing. 

V/C

Ratio

Merge 

Volume

Benchmark 

Throughput

(veh/h) 

Benchmark 

Average 

Delay(s) 

1,312 ft 

Throughput

(veh/h) 

1,312 ft 

Average 

Delay(s) 

1,969 ft 

Throughput

(veh/h) 

1,969 ft 

Average 

Delay(s) 

2,625 ft 

Throughput

(veh/h) 

2,625 ft 

Average 

Delay(s) 

0.6 500 4,168 12.15 4,166 

(−2, −0.05%) 

11.90 

(−0.25, 

−2.06%)

4,165 

(−3, −0.07%) 

11.92 

(−0.23, 

−1.89%)

4,167 

(−1, −0.02%) 

12.02 

(−0.13, 

−1.07%)

0.6 1,000 4,669 13.77 4,665 

(−4, −0.09%) 

13.36 

(−0.41, 

−2.98%)

4,666 

(−3, −0.06%) 

13.37 

(−0.4, −2.90%) 

4,668 

(−1, −0.02%) 

13.47 

(−0.3, −2.18%) 

0.6 1,500 5,175 15.98 5,169 

(−6, −0.12%) 

15.50 

(−0.48, 

−3.00%)

5,170 

(−5, −0.10%) 

15.53 

(−0.45, 

−2.82%)

5,175 

(0, 0.00%) 

15.77 

(−0.21, 

−1.31%)

0.9 500 6,024 19.41 6,030 

(6, 0.10%) 

19.21 

(−0.2, −1.03%) 

6,011 

(−13, −0.22%) 

23.32 

(3.91, 20.14%) 

6,024 

(0, 0.00%) 

19.32 

(−0.9, −0.46%) 

0.9 1,000 6,022 195.45 6,515 

(493, 8.19%) 

38.00 

(−157.45, 

−80.56%)

6,442 

(420, 6.97%) 

60.04 

(−135.41, 

−69.28%)

6,197 

(175, 2.91%) 

157.82 

(−37.63, 

−19.25%)

0.9 1,500 5,954 329.95 6,520 

(566, 9.51%) 

191.67 

(−138.28, 

−41.91%)

6,396 

(442, 7.42%) 

214.96 

(−114.99, 

−34.85%)

6,293 

(339, 5.69%) 

262.90 

(−67.05, 

−20.32%)

1.2 500 5,965 107.77 6,094 

(129, 2.16%) 

30.32 

(−77.45, 

−71.87%)

6,090 

(125, 2.10%) 

30.27 

(−77.5, 

−71.91%)

6,082 

(117, 1.96%) 

47.04 

(−60.73, 

−56.35%)

1.2 1,000 5,966 254.84 6,427 

(461, 7.74%) 

82.28 

(−172.56, 

−67.71%)

6,531 

(565, 9.48%) 

52.12 

(−202.72, 

−79.55%)

6,250 

(284, 4.76%) 

174.51 

(−80.33, 

−31.52%)

1.2 1,500 6,011 319.32 6,422 

(411, 6.85%) 

245.19 

(−74.13, 

−23.21%)

6,478 

(467, 7.77%) 

206.29 

(−113.03, 

−35.40%)

6,316 

(305, 5.07%) 

282.82 

(−36.5, 

−11.43%)

Note: When three values are reported, this indicates the absolute value, the absolute difference relative to the benchmark case (first value in parentheses), and the 

percent difference relative to the benchmark case (second value in parentheses). 
50 

The researchers conducted the simulation described in table 3 with the assumption of a 50–50 

split scenario, meaning that 50 percent of the merge traffic uses the first merge location and 

50 percent uses the second merge location. This is a reasonable assumption because if the public 

can freely choose between the two merge locations, the long-term expected value of volume split 

is 50–50. However, it is also possible that the traffic managers can control the volume split with 

advanced traffic control strategies, such as dynamic message signs or in-vehicle information. 

Therefore, the team conducted sensitivity analyses and compared the bottleneck performance 

under three different splits: 30–70, 50–50, and 70–30. The team tested medium and high levels 

of traffic with the 0.25-mi connection spacing. Results from figure 36 clearly demonstrate that 

the 50–50 split results in the best performance under most scenarios or performs almost as well 

as the remaining two splits for other scenarios. This result implies that the traffic managers need 

to let the public know that drivers can freely choose either merge location through road signs or 

public outreach. If the traffic managers detect that more drivers are using one of the merge 

locations, there needs to be a method to guide drivers to use the merge locations on a 50–50 

basis. Advanced methods with ITS equipment, such as using dynamic message signs, is another 

consideration. 

Table 4 presents the results of the same analyses for the split diverge design, in which the team 

reported the results under different scenarios of the V/C ratios, diverge volumes, and connection 

spacing. The percent changes in each scenario, including the scenarios under heavy traffic, are 

small. Statistical tests show that these minor differences are not statistically significant, 

indicating that the team find the split diverge design results in enhanced diverge area 

performance insignificant compared to the regular diverge. The insignificance of the benefits of 

the split diverge design implies that the spatial distribution strategy is not effective at the diverge 

area. This may be partly due to the fact that diverge area congestion is less severe than at the 

merge. Since the split diverge design is not effective even under the most congested scenarios, 

the team did not evaluate the full split weaving design in figure 35. The team did not evaluate the 

full split weaving design because the split diverge portion may not experience high traffic 

volume as in the pure split merge design simulation due to the congestion upstream at the merge 

portion. The team believes it will not generate any operational benefits with the additional 

construction at the diverge in figure 35. Therefore, the team recommend the use of the split 

merge design at congested merge or weaving areas. 
51 

Table 4. Simulation results for split diverge distance between gores. 

V/C

Ratio

Merge 

Volume

(veh/h) 

Benchmark 

Throughput

(veh/h) 

Benchmark 

Average 

Delay(s)

1,312 ft 

Throughput

(veh/h) 

1,312 ft 

Average 

Delay(s) 

1,969 ft 

Throughput

(veh/h) 

1,969 ft 

Average 

Delay(s) 

2,625 ft 

Throughput

(veh/h) 

2,625 ft 

Average 

Delay(s) 

0.6 500 3,650 8.20 3,647 

(−3, −0.08%) 

8.43 

(0.23, 2.80%) 

3,647 

(−3, −0.08%) 

8.57 

(0.37, 4.51%) 

3,651 

(1, 0.03%) 

8.76 

(0.56, 6.83%) 

0.6 1,000 3,658 8.32 3,649 

(−9, −0.25%) 

8.48 

(0.16, 1.92%) 

3,652 

(−6, −0.16%) 

8.50 

(0.18, 2.16%) 

3,653 

(−5, −0.14%) 

8.74 

(0.42, 5.05%) 

0.6 1,500 3,659 9.61 3,651 

(−8, −0.22%) 

9.65 

(0.04, 0.42%) 

3,651 

(−8, −0.22%) 

9.53 

(−0.08, 

−0.83%)

3,653 

(−6, −0.16%) 

9.81 

(0.20, 2.08%) 

0.9 500 5,489 14.45 5,485 

(−4, −0.07%) 

14.73 

(0.28, 1.94%) 

5,482 

(−7, −0.13%) 

14.97 

(0.52, 3.60%) 

5,486 

(−3, −0.05%) 

15.24 

(0.79, 5.47%) 

0.9 1,000 5,494 13.66 5,488 

(−6, −0.11%) 

13.91 

(0.25, 1.83%) 

5,484 

(−10, −0.18%) 

14.06 

(0.40, 2.93%) 

5,487 

(−7, −0.13%) 

14.41 

(0.75, 5.49%) 

0.9 1,500 5,492 13.66 5,495 

(3, 0.05%) 

13.80 

(0.14, 1.02%) 

5,491 

(−1, −0.02%) 

13.88 

(0.22, 1.61%) 

5,494 

(2, 0.04%) 

14.14 

(0.48, 3.51%) 

1.2 500 7,260 23.02 7,258 

(−2, −0.03%) 

24.42 

(1.40, 6.08%) 

7,262 

(2, 0.03%) 

24.53 

(1.51, 6.56%) 

7,257 

(−3, −0.04%) 

24.85 

(1.83, 7.95%) 

1.2 1,000 7,260 23.02 7,260 

(0, 0.00%) 

23.80 

(0.78, 3.39%) 

7,260 

(0, 0.00%) 

23.60 

(0.58, 2.52%) 

7,251 

(−9, −0.12%) 

24.39 

(1.37, 5.95%) 

1.2 1,500 7,261 23.01 7,266 

(5, 0.07%) 

23.74 

(0.73, 3.17%) 

7,263 

(2, 0.03%) 

23.4 

(0.39, 1.69%) 

7,266 

(5, 0.07%) 

24.82 

(1.81, 7.87%) 

Note: When three values are reported, this indicates the absolute value, the absolute difference relative to the benchmark case (first value in parentheses), and the 

percent difference relative to the benchmark case (second value in parentheses).
52 

© 2019 Jiaqi Ma.

A. V/C ratio 1.2, merge volume 1,500 veh/h.

© 2019 Jiaqi Ma.

B. V/C ratio 1.2, merge volume 1,500 veh/h.
53 

© 2019 Jiaqi Ma.

C. V/C ratio 1.2, merge volume 1,000 veh/h.

© 2019 Jiaqi Ma.

D. V/C ratio 1.2, merge volume 1,000 veh/h.
54 

© 2019 Jiaqi Ma.

E. V/C ratio 1.2, merge volume 500 veh/h.

© 2019 Jiaqi Ma.

F. V/C ratio 1.2, merge volume 500 veh/h.
55 

© 2019 Jiaqi Ma.

G. V/C ratio 0.9, merge volume 1,500 veh/h.

© 2019 Jiaqi Ma.

H. V/C ratio 0.9, merge volume 1,500 veh/h.

Figure 36. Graphs. Bottleneck performance at 0.25-mi connection spacing.
56 

Simulation Results in a Real-World Network

To test whether the alternative design would improve the performance under complex real-world 

traffic and geometric conditions, the team used a calibrated network of I–66 freeway segment in 

Vissim. The simulation network was I–66 westbound between interchange I–495 (MM 64) and 

US 29 (MM 51), six interchanges on this 13-mi-long section. RTMS trailers collected speed and 

volume data along major mainline segments and on- and off-ramp volume data collected by 

cameras (see figure 35 for a map and the locations of RTMS trailers, cameras, and interchanges). 

Initial calibration helped the team narrow down parameter set candidates by using the Latin 

hypercube sampling design (LHD) approach. Key car following and lane changing parameters 

were calibrated. Vissim evaluated 500 scenarios created by LHD, with 5 replications for each 

scenario to choose the best candidate scenario. The selected candidate was fine-tuned to obtain 

the final simulation model. Vissim used O-D matrices to specify travel demand. The I–66 

freeway network has 10 zones. Zones 1 and 10 are the starting and ending points of the corridor. 

Zones 2–9 contain the intermediate interchanges. Two of the zones are only applicable to the 

existing HOV vehicles (i.e., exits for westbound, entrances for eastbound). The field-collected 

data in this study identified how many vehicles traveled between some, but not all, O-D pairs. To 

fill in the gaps, QueensOD software estimated the O-D matrices (Van Aerde 1993). Results 

indicated an excellent correlation between estimated and field-measured O-D trips. There is an 

HOV lane on the left-hand side of the base case calibrated network. For the HOV lane on the 

right-hand side, the team modified the network and specified the rightmost lane as the HOV lane. 

The team assigned the same HOV demand to the HOV lane on the right-hand side, and 

performed simulations 10 times to account for stochasticity.

Based on the base year data and simulation results of the calibrated benchmark network, 

congestion at interchanges 1, 2, 3, and 4 is relatively severe; therefore, the team made geometric 

conversions to create a split merge design at these locations. Figure 37-A shows the before and 

after designs for interchange 3. The connection spacing used here is approximately 0.25 mi based 

on the simulation results in the previous section. This design may not be the optimal location for 

the horizontal lines because many other real-world design factors, such as vertical grades, 

right-of-way, and construction costs, deserve consideration. This study only aims to study the 

potential traffic impact under one potential line location.

Original photo: © 2017 Google® (see acknowledgements).

A. Benchmark geometric design.
57 

Original photo: © 2017 Google® (see acknowledgements).

B. Split merge design.

Figure 37. Photos. Interchange 3 before and after design.

The simulation results show improvements in both throughput and average delay, and both 

improvements are statistically significant at the 95 percent confidence level, as illustrated in 

figure 38. On average, the throughput increases from 7,589 to 7,798 veh/h, representing a 

2.75-percent improvement. The average delay decreases from 643 to 608 s, representing a 

reduction of 35 s of delay for each individual traveler on average, an improvement of 

5.5 percent. Based on the results from the previous section and under the current demand 

scenarios, converting interchanges 1, 2, 3, and 4 mentioned above to the split merge design can 

generate significant improvements, which can be even greater when the demand becomes higher. 

© 2019 Jiaqi Ma.

A. Throughput for the benchmark and split merge configurations.
58 

© 2019 Jiaqi Ma.

B. Average delay for the benchmark and split merge configurations.

Figure 38. Charts. Throughput and average delay. 

Summary

In this study, the team conducted microscopic simulation to evaluate the effectiveness of the 

alternative geometric design of split merging, diverging, and weaving for reducing bottleneck 

congestion. The simulation also allowed the team to develop managerial insights for State DOTs 

to better design, enhance, and manage their freeway systems in the future. 

The split merge design can effectively reduce the average delay and increase the traffic 

throughput under medium-to-high levels of traffic congestion. The split diverge is not effective 

under many traffic conditions and therefore not recommended for construction. Based on the 

results of the split merge and diverge designs, a partial split weaving design with the merge 

portion converted to the split merge design is recommended at congested weaving areas. Overall, 

this strategy can potentially increase throughput by 6 to 9 percent and reduce delay by 40 to 80 

percent. 

The team also analyzed scenarios having different percentages of merge volumes using the two 

merge locations. The team found that under most scenarios, a 50–50 split of the on-ramp volume 

generates the best bottleneck performance. This indicates that traffic managers only need to let 

the public know that both merge locations can be freely used and design the road sign such that a 

50–50 split of ramp traffic volume can be achieved. The agencies can also use ITS equipment, 

such as dynamic message signs, to ensure a 50–50 split if a deviation is detected.

SPEED OPTIMIZATION (E.G., VIA DYNAMIC TRAFFIC CALMING DEVICES)

Speed harmonization and VSL studies imply that medium free-flow speeds (e.g., 30 mi/h) would 

be more effective than low speeds (e.g., 5 mi/h) or high speeds (e.g., 65 mi/h) at optimizing 

freeway traffic flow. In an age of high CAV market penetration, speed harmonization may be 

obtained directly by changing vehicle behavior. However, this project endeavors to focus on 

strategies not requiring CAV technologies, and the team believes that dynamic traffic calming 
59 

devices (e.g., overhead gantries, retractable rumble strips) may be capable of coercing drivers to 

obey medium speeds at higher rates of compliance. Figure 8 illustrates this concept. 

Sensitivity analysis experiments for examining speed optimization effects were conducted in 

TSIS-CORSIM. Small generic datasets (also known as “toy networks”) were generated for the 

initial experiments. The original expectation was to transition to the MnDOT I–394 

TSIS-CORSIM datasets for further testing. Figure 39 shows the freeway geometry associated 

with the base dataset. For some experiments, the team eliminated the off-ramp to focus on merge 

maneuvers. For other experiments, the on-ramp is eliminated to focus on diverge maneuvers. 

Finally, a third set of experiments considered the full weaving section. 

Source: FHWA.

Figure 39. Illustration. Simple weaving section. 

Experimental Design

Experimental design parameters were as follows:

• Base demands of 2,000 veh/h/l on the mainline and 1,800 veh/h on the single-lane ramp.

• Demand multipliers (both mainline and ramp) between 75–125 percent at 5 percent

increments.

• Mainline free-flow speeds between 35–70 mi/h at 5 mi/h increments.

• Number of mainline lanes: two, three, and four.

• Car following aggressiveness: low, medium, and high.

• Various distances between on-ramp and off-ramp (only for weave section experiments).

• Key output measures: average segment speed and average network-wide speed.

Results and Discussion

The most optimistic results saw average vehicle speeds more than doubling when imposing a 

lower free-flow speed. For example, in one weaving section experiment, average vehicle speeds 

upstream of the merge point jumped from 20.4 to 47.3 mi/h after reducing the free-flow speed 

from 65 to 50 mi/h. The impact on average vehicle speeds is generally less significant at higher 

numbers of mainline lanes and on other links downstream of the merge point (figure 40).

Source: FHWA.

Figure 40. Illustration. Location of maximum benefits for speed optimization. 
60 

In a second merge-only experiment (no off-ramp), average vehicle speeds upstream of the merge 

point jumped from 7.5 to 37.1 mi/h after reducing the free-flow speed from 65 to 45 mi/h. 

Table 5 shows these results. Many of the successful speed optimization experiments produced a 

similar “zipper” pattern of speeds with an asterisk, indicating the optimum combinations of 

demand versus posted limit. The red text indicates the location in the middle of the zipper, where 

the posted speed limit can produce the greatest benefits. The implication is that significant speed 

optimization benefits only occurred under a narrow range of demands (i.e., near capacity) and 

speed limit changes. The downside of this finding is that this narrow range of conditions 

producing maximum benefits, in terms of demand ranges and free-flow speed ranges, is often 

difficult to predict without first running a simulation.

Table 5. Average speeds due to speed limit and demand.

Posted 

Speed 

Limit 

(mi/h)

Demand Multiplier (Percent)

75 80 85 90 95 100 105 110 115 120 125 

70 68.6* 67.7* 66.8* 62.8* 9.6 7.3 7.6 7.6 6.6 6.5 6.5 

65 63.3 63.7 62.6 62.1* 9.4✝ 7.5✝ 7.2 6.4 6.9 6.9 6.4 

60 58.8 58.0 57.8 57.1 35.9✝ 9.6✝ 7.0 7.2 6.6 6.9 6.4 

55 53.4 53.7 52.4 53.0 52.6*✝ 14.3✝ 7.0 7.5 7.2 7.0 6.4 

50 48.1 47.8 47.6 47.5 47.6✝ 34.4✝ 9.4 7.7 7.1 6.5 6.4 

45 42.7 43.3 42.6 42.5 42.3✝ 37.1*✝ 11.0 7.7 7.0 6.8 6.2 

40 38.1 37.6 37.9 37.8 37.2 17.0 12.5* 8.0* 7.0 6.8 6.3 

35 33.2 33.1 32.9 31.8 30.1 9.0 7.4 6.9 6.0 5.9 5.4 

*Optimum combinations of demand versus posted limit.

✝Location in the middle of the zipper where the posted speed limit can produce the greatest benefits.

Procedures from the HCM can prescreen alternatives without running a simulation. However, a 

series of test runs conducted in the Highway Capacity Software (HCS) (merge module and 

weaving module) using similar input data did not predict any benefits of free-flow speed and/or 

speed limit optimization. The resulting hypothesis is that the HCM methods are perhaps not 

capable of recognizing or analyzing the same speed harmonization-type effects and benefits that 

are commonly observed in a microsimulation (and in the field). As such, using an offline or 

real-time microsimulation model might detect when the zipper conditions from table 5 are in 

effect for any local site or local conditions. 

Although the significant speed optimization benefits were observed for some weaving and merge 

conditions as shown in table 5, the benefits were weaker (e.g., on the order of 2 to 6 percent) for 

diverge conditions. 

Upon reaching the conclusion that speed optimization benefits can probably only be predicted by 

establishing an offline or real-time microsimulation model for local conditions, the need for 

additional experiments on a larger network (e.g., I–394 MN) diminished. As such, agencies 

willing to detect zipper conditions in real time can consider implementing countermeasures to 

reduce free-flow speeds (or speed limits) when such conditions are present. Such 

countermeasures could include dynamic traffic calming devices (e.g., retractable rumble strips), 
61 

dynamic VSL signs, or CAV speed controls. This approach also assumes that the offline or 

real-time microsimulation model is consistently and periodically recalibrated to reflect local field 

conditions accurately. 

MAINLINE METERING (I.E., DYNAMIC SIGNAL CONTROL)

A form of mainline metering to reduce demand and improve flow uses traffic metering upstream 

from a bottleneck, thereby regulating the number of vehicles moving through the bottleneck. At 

locations where capacity expansion is not feasible (i.e., at tunnels and bridges), implementation 

of this form of mainline metering can help. For example, metering for traffic arriving at the 

bridge or tunnel is used at the Bay Bridge in Oakland, CA; the Hampton Roads Bridge-Tunnel 

connecting Hampton, VA, and Norfolk, VA; and the Baltimore Harbor Tunnel in Baltimore, 

MD. 

In situations where mainline and ramp vehicle speeds are both very low and fall below a defined 

threshold, such as 5 or 10 mi/h, traffic signal control applied to both upstream approaches could 

optimize the operation by eliminating merge friction. Figure 9 illustrates this concept. However, 

there is no known application of the treatment.

Dynamic Signal Control Algorithm 

For this strategy, the team developed a dynamic signal control algorithm to apply in an integrated 

ramp and mainline metering control strategy. The proposed dynamic signal control algorithm 

includes three main modules: 

• In the first module, downstream traffic sensors update traffic flow and speed at every 

time point, and the proposed algorithm runs iteratively at the same discrete time points.

• The second module activates the dynamic signal controller when detecting a certain 

traffic speed drop at all lanes of the merge point. This module aims to stop the mainline 

traffic from entering the downstream queue until it is about to dissipate.

• The third module predicts the downstream traffic queue dissipation time based on the 

information provided by the traffic sensors. This process uses a reliable and efficient 

real-time algorithm, which the second module then uses to let the upstream traffic 

smoothly merge into the downstream queue (Ghiasi et al. 2017). For more detailed 

information about the developed algorithm, refer to Ghiasi et al. (2018). 

Test Environment

This project uses a merge section of I–35 in Kansas City, KS, to test the proposed algorithm. 

This section is approximately 1.4 mi long and is from the calibrated Vissim simulation network. 

The analysis is on the westbound side of the network and includes two merges. The team studied 

the second merge, located about 0.75 mi downstream of the input traffic. The dynamic signal 

control zone in the second merge area begins at the merge point. Figure 41 shows the control 

zone on the left-hand side. At the merge point, an on-ramp roadway merges the three-lane 

mainline freeway. Right after the merge point, an acceleration lane with the length of 922 ft 

begins. Therefore, a lane drop lies at the end of this acceleration lane, and traffic congestion may 

occur at this point of the freeway and can propagate backwards to the upstream sections. 
62 

Original photo: © 2017 Google® (see acknowledgements).

Figure 41. Map. Case study map for the dynamic signal control on I–35 Kansas City, KS. 

Simulation Setup and Application

The simulation model uses six sets of loop detectors in this area, where each set contains a 

number of loop detectors embedded next to one another to cover all lanes at a specific 

longitudinal location. Each loop detector set represents a traffic sensor in the analyses. These 

traffic sensors sit evenly along the merge section with the distance of 226 ft between sensors. 

Traffic includes 3.5 percent trucks, and the length of the regular vehicles and trucks are set as 

23 and 80 ft, respectively. To evaluate the algorithm performance, four measures of effectiveness 

were chosen: throughput (ΔET

), average traffic speed (ΔES), average traffic delay (per vehicle)

(ΔED), and total carbon monoxide emissions (ΔEE). Note that the throughput values are recorded 

at the end of the control zone (after the lane drop), but the other measures are network-wide 

average values.

Three time periods divide the simulation analysis. The first 900-s period is the warm-up period, 

with no traffic evaluation measurements conducted in this period. Traffic evaluation 

measurements start at the beginning of the second 900-s period. During this period 

(i.e., 900–1800 s), the team measured ΔET

, ΔES, and ΔEE. To measure ΔED, the team extended

the simulation experiment to the third time period that includes no further mainline traffic input 

and the ramp traffic input of 970 veh/h that is the average of the field measured volumes. This 

period continues until the time when the last vehicle of the mainline approach exits the control 

zone. The reasoning for this is to let the existing traffic queue in the network discharge before 

obtaining the delay measurements. Although considering no further mainline traffic input in the 

third simulation period may look unrealistic, the simulation experiments end after the last vehicle 

of the mainline approach exits the control zone. Therefore, considering any further mainline 

traffic would not affect the simulation results. This zero-traffic-input assumption is only for the 

mainline approach because the mainline input traffic exits the control zone mostly after the 

merge input traffic.
63 

For the simulation, the traffic demands of the mainline were denoted as Dmain, the traffic 

demands of ramp approaches were denoted as Dramp, the maximum red intervals for the mainline 

were denoted as Rmain, and the maximum red intervals for ramp approaches were denoted as

Rramp. Further, the speed threshold at which the mainline signal control activates was denoted as

Vthreshold. 

The team first presented the simulation results for a default parameter setting. In this setting, the 

team set Dmain = 8,000 veh/h; Dramp = 1,500 veh/h; Rmain = 60 s; Rramp = 20 s; and 

Vthreshold = 6 mi/h. Further, to ensure stability and efficiency of the signal timing plan, the team 

required that the red and green intervals at both approaches be no less than 10 s. Moreover, the 

algorithm update interval is set to 2 s. To consider the stochasticity effects of the simulation 

experiments, each simulation scenario runs 10 times with different random seeds. The obtained 

average mainline and ramp green intervals among all random seeds are 151 and 170 s, 

respectively, where on average 72 percent of the green intervals overlap. In this algorithm, the 

ramp red interval lasts until another mainline control activation happens or the red interval 

reaches Rramp. In the default case scenario, the time between every consecutive mainline control 

activation is greater than 20 s, and the ramp red interval is always equal to Rramp = 20 s. 

Moreover, the average mainline red interval is 40 s. Figure 43 shows an example of a signal 

timing plan for the first two mainline and ramp cycles that resulted from the first simulation 

instance. Further, figure 42 compares the simulation results of the signal control algorithm with 

the benchmark case and without any control. The results indicate that the control algorithm does 

not significantly affect throughput. However, the other measures of effectiveness show 

significant improvements.

Source: FHWA.

A. Throughput. 

Source: FHWA.

B. Average speed.

Source: FHWA.

C. Average delay.

Source: FHWA.

D. Carbon 

monoxide 

emissions.

Figure 42. Charts. Simulation results for the default setting. 
64 

Source: FHWA.

Figure 43. Illustration. Example signal timing plan. 

The team performed a sensitivity analysis on the Dmain and Dramp values. Then, the team looked

at the effects of Rramp through simulation experiments. For these simulation analyses, different 

scenarios were considered for the Dmain and Dramp values, while other parameters were kept as 

their default values. Figure 44 shows the results of the sensitivity analysis on Dmain and Dramp

values and shows the three-dimensional box-plots among all 10 random instances. 

Source: FHWA.

A. Throughput impacts resulting from the changes in ramp and mainline demands. 
65 

Source: FHWA.

B. Average traffic speed impacts resulting from the changes in ramp and mainline demands. 

Source: FHWA.

C. Average traffic delay impacts resulting from the changes in ramp and mainline demands. 
66 

Source: FHWA.

D. Emissions impacts resulting from the changes in ramp and mainline demands. 

Figure 44. Graphs. Sensitivity analysis on Dmain and Dramp. 

Again, the results indicate no significant effects on throughput; however, the other measures 

improve at various demand scenarios: 

• Improvement of up to 15.7 percent in average speed. 

• Reduction of 20.6 percent in average delay. 

• Decrease of 13.7 percent in carbon monoxide emissions. 

Achievement of the maximum benefits occurs at the mainline demand values of 

7,000–7,500 veh/h (or 2,333–2,500 veh/h/l) and the ramp demand values of 1,000–1,500 veh/h. 

The minimum improvements result in relatively low demand values when traffic is 

undersaturated in such demand scenarios. Overall, this experiment reveals that the proposed 

algorithm can achieve significant improvements when high traffic saturation exists. 

Next, the team performed a sensitivity analysis on the impacts of the Rramp parameter on the 

algorithm results, shown as box-plots in figure 45, in which the dotted-dashed green, solid 

yellow, and dashed red box-plots correspond to reasonably low, moderate, and high time 

intervals for Rramp, respectively. The results indicate that throughput improves with Rramp because 

as Rramp increases the frictions between two approaches decrease and thus throughput increases. 

However, the other measures decrease with Rramp. This is because greater Rramp values would 

worsen the ramp traffic performance that affects the network average speed, delay, and total 

emission. The maximum obtained benefits are a 3.1-percent increase in throughput at 

Rramp = 120 s, a 15.7-percent improvement in average speed at Rramp = 20 s, a 20.9-percent 
67 

reduction in average delay at Rramp = 10 s, and a 13.4-percent decrease in carbon monoxide 

emissions at Rramp = 20 s.

Source: FHWA.

A. The impact of maximum red intervals on 

throughput. 

Source: FHWA.

B. The impact of maximum red intervals on 

average speed.

Source: FHWA.

C. The impact of maximum red intervals on 

average traffic delay.

Source: FHWA.

D. The impact of maximum red intervals on 

emissions.

Figure 45. Box-and-whisker plots. Sensitivity analysis on Rramp. 

Summary and Conclusions

The team developed a dynamic signal control algorithm to apply as an integrated ramp and 

mainline metering strategy. When the traffic queue occurs due to a downstream lane drop or if a 

freeway merge spills all the way back to the merge point, the dynamic signal control algorithm is 

activated and turns the mainline signal to red to keep the mainline traffic from entering the 

downstream queue, which otherwise would exacerbate the traffic congestion. For safety reasons, 

the proposed algorithm does not activate the signal control unless the traffic speed at all mainline 

lanes drops below a safe speed threshold. This threshold is set to 6 mi/h in the experiments. After 
68 

activation, this algorithm predicts the future downstream queue status using the information 

provided by the deployed traffic sensors. 

This prediction updates at every decision time point; as a result, the newly received information 

guides changes to the prediction. The mainline traffic signal turns to green a few seconds in 

advance of the predicted downstream queue dissipation time to let the mainline traffic smoothly 

join the downstream traffic. At the same time, the ramp signal turns to red to remove frictions 

between the merging and the mainline traffic. This red interval ends when detecting another 

speed drop at the mainline approach, and the mainline signal turns to red or reaches a maximum 

ramp red interval.

The team conducted simulation analyses using a calibrated Vissim network to evaluate algorithm 

performance under different traffic conditions and parameter settings. The implemented case 

study is a section of I–35 located in Kansas City, KS. This study considers four performance 

measures: traffic throughput, average network speed, average network delay, and total carbon 

monoxide emissions. Results of the simulation analyses show maximums of a 15.7-percent 

improvement in average speed, a 20.9-percent reduction in average delay, and a 13.7-percent 

decrease in carbon monoxide emissions. It is found that maximum improvements are obtained at 

relatively high traffic demands (i.e., the mainline demand values of 7,000–7,500 veh/h 

(or 2,333–2,500 veh/h/l) and the ramp demand values of 1,000–1,500 veh/h). Further, the 

improvements at low traffic demand values are comparatively limited. This implies that such 

treatments may not be needed when traffic demand is not sufficiently high. Results also indicate 

that no significant throughout improvement is achieved when Rramp is set to 30 s or less. That is 

because with low Rramp, frictions between the mainline and on-ramp traffic increase. On the other 

hand, when Rramp is high, on-ramp traffic may greatly suffer, which impacts overall 

network-wide traffic performance. It should be noted that although throughput is not 

significantly improved when Rramp is set to a low value, as demonstrated in the network-wide 

average traffic speed, delay, and emission results, this control strategy does not simply shift the 

end of the queue to upstream sections without improving the overall traffic measures. Moreover,

although these benefits are limited to a relatively small section of a network, like most other 

local control strategies, the proposed control approach can be implemented in other parts of the 

network. Therefore, achievement of further benefits for larger areas could justify system 

deployment costs. 

Before implementing this control treatment, it is recommended that local transportation agencies 

perform more site-specific analyses. First, while the speed at which the control algorithm 

activates (i.e., Vthreshold) is supposed to be set to a reasonably safe value, further safety analyses 

should be conducted to investigate the potential safety concerns of the proposed freeway signal 

control. The level of users’ acceptance and compliance is another issue that needs to be 

comprehensively explored and addressed. Overall, this study can be represented as a baseline for 

highway mainline metering techniques. Thus, with some future improvements in the control 

algorithm, a superior and more cost-effective model, which relies on fewer traffic sensors, can be 

developed. 
69 

COORDINATED ADAPTIVE RAMP METERING SYSTEM (E.G., HERO)

Traditional ramp metering measures upstream freeway mainline volume to determine ramp flow, 

but rarely monitors downstream conditions. Downstream problems only show when congestion 

reaches the upstream ramp detector. Coordinated adaptive ramp metering (figure 12) uses 

feedback logic in a closed-loop control system. Traffic conditions measured at downstream 

bottlenecks determine critical occupancy and appropriate levels of traffic entering from 

upstream. The process assumes a historic or theoretical value of freeway capacity. Downstream 

flow conditions provide feedback to determine real-time ramp flow (qr) and optimal occupancy 

(qcapN). When applied on a holistic, coordinated, system-wide basis, all ramps continuously 

communicate with each other to resolve complex traffic flow situations. One example of such a 

system is HERO, but the team identified several similar algorithms and systems during the task 2 

effort. The team used the I–15 corridor model in San Diego, CA, to analyze the performance of 

the CRM algorithm HERO versus standalone algorithms (ALINEA and the San Diego ramp 

metering system (SDRMS)) and a base case without ramp metering. The team compared these 

scenarios to highlight the benefits provided by each ramp metering method for specific subsets of 

users in the corridor. 

Model Setup 

During an initial phase prior to the algorithm testing, the team added specific elements related to 

the ALINEA ramp meter implementation to the existing SDRMS along the I–15 corridor south 

of Lakes Hodges, CA, in a southbound direction. This area includes major congested areas of the 

corridor. The team coded the detection equipment for a total of 16 on-ramps and corresponding 

mainline sections. This setup allows the evaluation of the three different methods (SDRMS, 

ALINEA, and HERO) using the same Aimsun file, ensuring consistency among the different 

runs. Objects added to the network (figure 46) include: 

• Queue (flush) detector. 

• Middle (of the ramp) detector.

• Ramp meter.

• Exit detector.

• Mainline lane detectors.
70 

© 2019 Aimsun.

Figure 46. Illustration. Selected area of the I–15 corridor and model setup for ALINEA and 

SDRMS ramp metering.

ALINEA Algorithm

As stated in chapter 2, ALINEA is a linear proportional-integral-derivative feedback process 

used locally to control the flow from the on-ramp to the mainline. The system is comprised of a 

ramp meter and mainline detectors. Expressed formally, the flow change from the on-ramp is 

proportional to the distance of the mainline occupancy from its target, as can be seen from the 

following equation. For a given on-ramp:
71 

(1)

Where:

qr

k+1 and qrk = on-ramp flows at time step k+1 and k, respectively. 

k = time step.

k-1 = previous time step. 

Kr = proportional-integral-derivative gain factor. 

ρtarget = target occupancy on the mainline. 

ρk = measured occupancy on the mainline at time step k. 

ALINEA has two principal parameters to calibrate and three secondary parameters:

• The gain factor. 

• The target occupancy.

• The time step or calculation interval on which to apply the adjustment. 

• The minimum and maximum values that can be used as flows for a given ramp meter. 

• The distance of the mainline detectors to the on-ramp position.1

HERO Algorithm

As stated in chapter 2, HERO is based on ALINEA, coordinating the response from the ALINEA 

algorithm on a subset of upstream on-ramps (i.e., slaves). In addition to Aimsun’s built-in 

ALINEA algorithm, queue control is added to limit the length of queues on the on-ramps. For a 

given on-ramp:

(2)

Where:

qw

k = flow rate for the current queue at time step k. 

Tc = calculation interval.

wmax = maximum possible queue at the on-ramp.

wk = current queue at time step k. 

k = time step.

dk-1 = on-ramp demand at time step k-1. 

k-1 = previous time step. 

1

It should be noted that depending on the type of trigger and the underlying congestion environment, ALINEA 

can show sub-optimal results and exhibit an oscillating behavior, activating and deactivating frequently, or 

“flip-flopping.” It is suggested by Wang and Papageorgiou (2006) to add a proportional term to the equation.
72 

The following description of the HERO algorithm is taken from Papamichael (Papamichael 

2008). To link the on-ramps together, changes to the previous local queue control formula 

include a coordination parameter on the minimum queue possible on a given on-ramp in the 

following linked control strategy. For a given on-ramp: 

(3)

Where:

qLCk = flow for local control at time step k. 

Kw = control parameter that is set to 1Tc like in the previous local queue control. 

1Tc = one calculation interval. 

wmink = minimum queue forced at time step k. 

wk = current queue at time step k. 

dk-1 = previous on-ramp demand at time step k-1. 

The flow from a given on-ramp is then chosen as the minimum from either ALINEA or the 

coordinated version of ALINEA with minimum queue, given that the storage space is available.

(4)

Where:

qk = on-ramp flow at time step k. 

qr

k = flow for coordinated control at time step k. 

qLCk = flow for local control at time step k. 

qw

k = flow rate for the current queue at time step k. 

The minimum queue at time step k (wmink) is calculated by coordinating all the queue information 

from the on-ramps, using all of the upstream on-ramps of a given on-ramp as a common storage 

space with local demand.

(5)

Where:

wi.mink = minimum queue for all time steps.

Σwj

k = summation of queues at all time steps.

Σn

j ≠ i wj.max = summation of maximum queues at all time steps.

wi.maxk = maximum queue for all time steps.
73 

In effect, the relative queues at on-ramps (the ratio of the current queue to maximum queue) 

being equal for all ramps implies that the traffic conditions are being leveled across ramps by 

using the extra storage spaces of underused ramps. Furthermore, the HERO algorithm is not 

always in function; its authors intended it to trigger when the local ALINEA ramp metering 

response is failing for given traffic conditions. The triggering conditions are: 

• The mainline occupancy is over a threshold of the target occupancy. 

• The queue on the on-ramp (which will become the master ramp) is over a threshold of the 

maximum queue (corresponding to the ramp storage space available for vehicles). 

In a similar fashion, deactivation is controlled by one of the following two conditions: 

• The mainline occupancy drops below a threshold of the target occupancy.

• The queue on the master ramp is lower than a threshold of the maximum queue 

(corresponding to the ramp storage space available for vehicles). 

When the activation threshold is met in one location, this embattled on-ramp becomes the master

on-ramp of any upstream on-ramps who work as slaves to reduce the flow released to the 

mainline; this proceeds until one of the deactivation thresholds is met.

With the use of a “real” test case, the team added new features to the algorithm in this research to 

reflect certain specific issues that were not addressed in the literature, in particular:

• Minimum number of slaves. 

• Maximum number of slaves. 

• Concurrent triggering of independent activations. 

• Merging behavior of overlapping activations. 

The minimum number of slaves controls the possibility for an on-ramp to eventually become a 

master, should the conditions be satisfied. This parameter is useful to limit the trigger of HERO 

for an on-ramp as a master that is at the limit of a given network or study area or is just 

downstream from a major interchange. The maximum number of slaves controls the number of 

on-ramps used as extra storage space for a given master. This parameter is useful to limit the 

upstream impact of a bottleneck when the travel time of vehicles from an upstream on-ramp to 

the bottleneck is longer than the time it takes to reabsorb the congestion with the help of “closer” 

slaves.

Having the maximum number of slaves on a given network enables HERO to have multiple 

concurrent activations. This is useful to reabsorb multiple congestion hotspots occurring 

independently over the network (e.g., multiple interchanges of major highways). The behavior of 

HERO in those instances is the same. Finally, when HERO activations overlap (e.g. when a slave 

of a given master has itself a higher mainline occupancy and a high on-ramp queue), HERO 

activates this new master’s slaves and transfers them to the original master, in effect using more 

slaves than the original maximum number. When deactivating the original master and not the 

“second,” HERO will reactive for this on-ramp. When HERO deactivates, all the original slaves 

are reinitialized.
74 

In summary, HERO uses ALINEA as a base and uses upstream on-ramps as extra storage space, 

using relative queues to level conditions across on-ramps. HERO uses the following 12 

parameters:

From ALINEA:

• Kr. 

• Target occupancy on the mainline. 

• Calculation at the time step.

• Minimum and maximum values that can be used as flows for a given ramp meter. 

• The distance of the mainline detectors to the on-ramp position. 

Specific to HERO:

• Maximum queue. 

• Mainline occupancy activation threshold. 

• Mainline occupancy deactivation threshold. 

• Ramp queue length activation threshold. 

• Ramp queue length deactivation threshold. 

• Minimum number of slaves. 

• Maximum number of slaves. 

HERO’s time step calculation is set to be the same as ALINEA’s.

The 12 parameters needed for HERO must be calibrated for the given traffic conditions. In 

favorable conditions, these parameters would be optimized using the Aimsun model as a cost 

function, using mathematical frameworks, such as the derivative free optimization project 

NOMAD, to determine the best parameter set that minimizes the objective function (e.g., global 

delay in a simulation) (Gerad Polytechnique Institute 2018). However, because of the relatively 

high dimensionality of the problem and the long computation time of the many ICM replications, 

an approximate sensitivity method is used to determine the best parameters, mostly based on a 

simplified version of the well-known “one at a time” (also known as “one factor at a time”) and 

traffic engineering considerations. The team assumed that parameters have an independent 

impact on the overall cost—each parameter varying in a given simulation between two extremes, 

while all other values remain constant at a “reasonable” value. Observing the better result of 

these two extreme experiments, the team selected a new experiment by shifting the parameter 

value in the direction of the better extreme.

Target Occupancy

When the mainline is close to capacity, estimation of an algorithm to measure the proper target 

occupancy from downstream detector flow drop helps increment target occupancy on the 

calibrated network ramp meter until the capacity drops on the mainline. The target is just below 

this upper limit, without imposing strict constraints on the on-ramp itself. This way, ALINEA 

triggers before the occupancy on the mainline provokes an artificial flow drop due to reaching 

capacity. This parameter is set at around 23 percent for this corridor. A lower value would 
75 

penalize the on-ramps too much, while with a higher value the freeway would reach capacity too 

quickly, thus not preventing slowdowns on time. 

Regulator Parameter (kr) and Calculation Interval

The metered flow allowed through the on-ramp will vary depending on the difference between 

the target occupancy and the measured occupancy for each time interval. The kr and the time 

interval manage how fast the flow can vary and how often to take these measurements. Testing 

of different values of kr avoided a flip-flop behavior at the on-ramps and concluded with a value 

of 10. Moreover, using a smaller calculation interval produced better results. The best results 

were attained at a 1-min interval, while a 30-s interval showed no additional benefits. 

Minimum and Maximum Flow 

A minimum and maximum flow can be set to ensure that the on-ramp performs at a certain level 

no matter what the occupancy. Placement of the detector is on the mainline to measure 

occupancy after the acceleration lane, in the merge portion of the mainline and the on-ramp. A 

wider range provides more flexibility for ALINEA and HERO to be more effective. A range of 

200–2,500 veh/h is set to provide this flexibility. The ranges are explained as follows:

• 200 veh/h is very aggressive for the mainline. Depending on the geometrical 

considerations of the on-ramps and typical demand peak hour, this is the smallest flow 

that does not cause overflow to the arterial network.

• 2,500 veh/h is above the normal capacity for a typical on-ramp. By setting the maximum 

this way, the throughput is not limited by an artificial constraint given by the ramp meter 

when there is no traffic on the mainline. The typical throughput will be lower for all 

on-ramps, limited only by the characteristics of the on-ramps (e.g., geometry, speed 

limits) and the vehicle behavior itself.

Maximum Queue

Estimation of the maximum queue used the length of the on-ramp divided by the average length 

of all the vehicles contained in the traffic demand of the experiment. Even weighted by the 

overall count of each vehicle class and given observations from the experiments, the team 

adjusted this number manually to represent an approximation. 

Mainline Occupancy Activation and Deactivation Thresholds 

These thresholds, as applied to the target occupancy from ALINEA, represent the level of 

congestion in the mainline that would trigger HERO and deactivate it when the congestion is 

gone. Because the team calibrated the ALINEA target occupancy to trigger just before a capacity 

drop, the team used values between 75–125 percent. The team presented the results using values 

between 75–100 percent since higher values showed obvious degradation in the overall results. 

Ramp Queue Length Activation and Deactivation Thresholds 

Ramp queue length activation and deactivation thresholds measure the ratio of the current queue 

length to the maximum queue needed for activation and deactivation. For activation, the team 
76 

used values ranging from 30–50 percent of maximum queue, while deactivation values ranging 

from 15–35 percent we used. Higher values for the activation threshold will not give enough 

time for HERO to prevent on-ramps from reaching capacity (maximum storage), while lower 

values of the deactivation threshold would have HERO activate without queues on the on-ramp.

Minimum and Maximum Number of Slaves

The minimum number of slaves is set to one to eliminate on-ramps at the edge of the network. 

The maximum number of slaves is set at three, and further experiments should show the impact 

of changing this number on the overall results. 

Tests and Measures of Performance (MOPs) 

As explained in the previous sections, a plausible range for the different values of the parameters 

required by both HERO and ALINEA were established after performing several tests. Table 8

presents the log of the activations and deactivations of the HERO algorithm that shows when, 

where, how long, and how many on-ramps were slaves during a given simulation. Table 6 and 

table 7 present a summary of six of the tests, showing the need to balance delay on the mainline, 

the on-ramps, and the arterial network for ALINEA and HERO algorithms, respectively. The 

following test performance measures describe the results qualitatively and quantitatively:

• Average speed on the mainline (mi/h). 

• Speed contours on the mainline and on-ramps (descriptive). 

• Weighted average harmonic speed on all on-ramps and mainlines (mi/h).

• Total travel time (h). This measure includes the travel time of the vehicles having exited 

the entire network (mainline, on-ramps, and arterials), the vehicles currently in the 

network, and the vehicles waiting to enter.

• Total distance traveled (mi). This measure includes the distance traveled by the vehicles 

having exited the network (mainline, on-ramps, and arterials) and the vehicles currently 

in the network. 

Table 8 presents the log of the activations and deactivations of the HERO algorithm that shows 

when, where, how long, and how many on-ramps were slaves during a given simulation. 

Table 6. Parameters used for the six sensitivity analysis tests—ALINEA. 

Test 

Number

Target 

Occupancy

(Percent) 

Minimum Flow 

(veh/h) 

Maximum Flow

(veh/h) 

Calculation 

Interval(s)

One 23 450 2,500 60 

Two 23 200 2,500 60 

Three 23 450 2,500 60 

Four 23 450 2,500 30 

Five 23 100 2,500 30 

Six 23 300 2,500 30 
77 

Table 7. Parameters used for the six sensitivity analysis tests—HERO.

Test 

Number

ML Activation 

(Percent) 

ML Deactivation

(Percent) 

Queue Activation

(Percent) 

Queue 

Deactivation 

(Percent) 

One 100 75 30 15 

Two 100 75 30 15 

Three 100 75 50 30 

Four 100 75 30 15 

Five 100 75 50 30 

Six 100 75 50 30 

Table 8. Summary of HERO activation and deactivation times for test six with the master 

ramp and its corresponding slaves. 

Activation Time Deactivation Time Master Ramp Slaves 

6:53:29 6:59:59 8 9, 10, 11 

7:02:59 7:09:29* 9 10, 11, 12 

7:09:29 7:17:29 8 9, 10, 11, 12 

7:21:59 7:23:29 9 10, 11, 12 

7:29:59 7:42:29* 9 10, 11, 12 

7:42:29 7:46:29 8 9, 10, 11, 12 

7:50:59 7:53:59 9 10, 11, 12 

8:21:59 8:32:59 12 13, 14, 15 

*When the current master merged with a downstream new master and became a slave.

Results and Discussion

All experiments have similar traffic conditions (e.g., same replication seed, demand), but none 

have virtual queues at the end of the simulations, meaning that all the demand has been pushed 

through the network during the simulation time. Differences in results are due to differences in 

behavior of the ramp metering algorithms and the ensuing decisions taken by individual vehicles 

which cause (or do not cause) congestion. 

When looking at the average travel times on the mainline in a series of heat maps, HERO 

exhibits faster overall times at 835 s for all experiments, representing a 19-s-per-vehicle 

(2-percent) gain over ALINEA and a 43-s-per-vehicle (5-percent) improvement over the no ramp 

metering alternatives (Belisle et al. 2019). Similarly, if the team includes the on-ramps with the 

mainline, the gain in total travel time is 282 h (3 percent) for HERO over ALINEA and 480 h 

(4 percent) over the no ramp metering scenario. While the focus of this study is the prevalence of 

the conditions on the mainline, the results do not focus solely on having the lowest possible 

travel times on the mainline. Other tests showing better mainline conditions at the expense of 

on-ramps and arterials were too severe.

The team generated speed contours showing overall traffic conditions for both the mainline and 

on-ramps for HERO, ALINEA, SDRMS, and no ramp metering. For example, starting at 

7:15 a.m. near ramp 12, HERO limits the mainline bottleneck expansion both spatially 

(i.e., upstream) and temporally (i.e., to approximately 8:00 a.m.) relative to the other scenarios, 
78 

especially the no ramp metering scenario, and eases the merge, eliminating the upstream 

slowdown. On-ramp speeds were visibly slower in HERO than no ramp metering, with speeds 

ranging from 1–8 mi/h at 7:00 a.m. for HERO versus 5–30 mi/h for no ramp metering for onramps 13 to 16. The same is true for on-ramps 8–10. 

By looking at the HERO logs, a correlation can be seen between the improvement of the speed 

contours graphs of the different methods and the activations (and deactivations) of HERO. For 

example, in the case of mainlines 8 and 9 between 7:00–8:00 a.m., a higher vehicular speed is 

the resultant of the activations of on-ramps 8 and 9 as masters with slaves 10, 11, and 12. 

Comparing the speeds locally for two mainline sections just upstream of a given bottleneck 

highlights this behavior, as shown in figure 47. In all cases, the speed recovers faster with HERO 

than with the other algorithms. The fluctuation of the speed as the algorithm adapts to the current 

conditions is also noticeable on figure 47. 

In summary, the main finding of this study is that for all three principal MOP (average speed on 

the mainline, weighted average harmonic speed on all on-ramps and mainlines, and global 

vehicle miles traveled (VMT)), HERO outperforms ALINEA by only a marginal amount, 

ALINEA outperforms SDRMS, and SDRMS outperforms the no ramp metering scenario. Ramp 

metering, and especially CRM, has the effect of transferring delay from the mainline to the 

on-ramps. Generally, this is a zero-sum game and no overall gain is expected; what the mainline 

gets is taken from the on-ramps. However, with the right set of parameters for test six, a 

“non-zero sum” result can be realized. By reducing the mainline friction and regulating flow, 

on-ramp merges are easier and travel times are shorter overall for the mainline, on-ramps, and 

arterials. This means that what the mainline gains is taken from the on-ramps but given back to 

the on-ramp vehicles on their mainline journey.

© 2019 Aimsun.

A. Merging area on-ramp 9.

© 2019 Aimsun.

B. Merging area on-ramp 8. 

Figure 47. Graphs. Mainline speed/time just upstream of on-ramp 9 and on-ramp 8. 
79 

Table 9 through table 13 present a numerical comparison of the data shown in the speed 

contours, with the weighted average of the harmonic speed on all 16 on-ramps and the 

corresponding mainline sections. HERO had the fastest average harmonic speed in test six at 

59.74 mi/h (decimals for differentiation, not significance), which is a 1-percent (0.7-mi/h) gain 

from ALINEA and a 2-percent (1.3-mi/h) gain from the no ramp metering alternative. Table 9

through table 13 also show that HERO is fastest on tests one, three, five, and six, while ALINEA

is fastest on tests two and four by a small margin. These tables also present the overall total 

vehicle hours traveled (VHT) and total VMT, including the arterial network. The lowest total 

time traveled is for the no ramp metering scenario at 67,250 h; however, the total distance 

travelled MOP is a better indicator of the throughput of the network, with a higher number 

meaning a higher throughput. In this regard, HERO test six had the highest VMT at 

2,781,946 mi, a very small improvement from both ALINEA and the no ramp metering base 

scenario. This small difference can be explained by the magnitude of the network and the large 

portions (both in time and space) when congestion is not present. A focus on the peak hour 

would show a larger relative improvement, but in all cases HERO still has the advantage, if only 

marginally, over all the other methods. 

In summary, the main finding of this study is that for all three principal MOPs (average speed on 

the mainline, weighted average harmonic speed on all on-ramps and mainlines, and global 

VMTs), HERO outperforms ALINEA by only a marginal amount, ALINEA outperforms 

SDRMS, and SDRMS outperforms the no ramp metering scenario. Ramp metering, and 

especially CRM, has the effect of transferring delay from the mainline to the on-ramps. 

Generally, this is a zero-sum game and no overall gain is expected; what the mainline gets is 

taken from the on-ramps. However, with the right set of parameters for test six, a 

“non-zero sum” result can be realized. By reducing the mainline friction and regulating flow, 

on-ramp merges are easier and travel times are shorter overall for the mainline, on-ramps, and 

arterials. This means that what the mainline gains is taken from the on-ramps but given back to 

the on-ramp vehicles on their mainline journey. 

Table 9. Performance comparison among the different tests and for all four scenarios—

mainline average travel time (s). 

Test Number HERO ALINEA SDRMS No Ramp Metering

One 836 854 858 878 

Two 849 846 858 878 

Three 838 843 858 878 

Four 854 849 858 878 

Five 838 848 858 878 

Six 835 854 858 878 
80 

Table 10. Performance comparison among the different tests and for all four scenarios—

mainline and ramp total travel time (h).

Test Number HERO ALINEA SDRMS No Ramp Metering

One 10,627 10,872 10,807 11,038 

Two 10,852 10,684 10,807 11,038 

Three 10,627 10,627 10,807 11,038 

Four 10,851 10,762 10,807 11,038 

Five 10,583 10,766 10,807 11,038 

Six 10,558 10,840 10,807 11,038 

Table 11. Performance comparison among the different tests and for all four scenarios—

mainline and on-ramp weighted average harmonic speeds (mi/h).

Test Number HERO ALINEA SDRMS No Ramp Metering

One 59.2 59.2 59.1 58.4 

Two 59.2 59.2 59.1 58.4 

Three 59.6 59.4 59.1 58.4 

Four 59.2 59.2 59.1 58.4 

Five 59.6 59.1 59.1 58.4 

Six 59.7 59.0 59.1 58.4 

Table 12. Performance comparison among the different tests and for all four scenarios—

VHT (h).

Test Number HERO ALINEA SDRMS No Ramp Metering

One 68,313 67,859 67,794 67,250 

Two 68,065 68,168 67,794 67,250 

Three 67,696 67,692 67,794 67,250 

Four 67,554 67,717 67,794 67,250 

Five 67,688 67,794 67,794 67,250 

Six 68,157 67,890 67,794 67,250 

Table 13. Performance comparison among the different tests and for all four scenarios—

VMT (mi).

Test Number HERO ALINEA SDRMS No Ramp Metering

One 2,781,614 2,780,741 2,778,855 2,777,243 

Two 2,781,273 2,779,039 2,778,855 2,777,243 

Three 2,779,885 2,779,039 2,778,855 2,777,243 

Four 2,779,701 2,778,783 2,778,855 2,777,243 

Five 2,780,570 2,780,307 2,778,855 2,777,243 

Six 2,781,946 2,780,461 2,778,855 2,777,243 

Importantly, HERO must be adequately calibrated to attain good results (Amini 2015). Results

will vary greatly depending on the underlying conditions and different networks, so while it is 

relatively easy to have better conditions on the mainline with HERO rather than any other 
81 

algorithm, it is just as easy to worsen the situation overall. Depending on the operators’ 

objectives, finding the right balance can be a complicated task. 

Furthermore, while the team proposed some basic enhancements to the “simple” version of the 

HERO algorithm, such as maximum number of slaves per master, other changes are possible. In 

particular, the mainline occupancy is an average of multiple lanes that exhibit different 

characteristics—this is especially true of a mainline with four or more lanes. The algorithm could 

be adapted to measure not the average occupancy but the maximum occupancy for a lane (or the 

average of a subset of lanes) at a given time when the bottleneck is slowly building up laterally 

downstream, which would help alleviate congestion by having a more precise indicator of when 

traffic is building up. Furthermore, an important issue is the concept of maximum queue. While 

sound in theory, this value does not correspond to a specific practical concept. This could be 

revised as a probability distribution, and the activation and deactivation thresholds could be 

translated as statistical concepts (i.e., distance from mean queue). While this would require more 

data and a small change in the basic algorithm, it would better indicate when a given queue is 

judged problematic and would limit the over triggering of HERO. 

More generally, the question of whether ramp metering should be activated at all times needs 

studying. Indeed, having a ramp meter is analogous to a signalized intersection; delay of vehicles 

can happen even though no opposing traffic warrants a stop at a red light. However, it is easier to 

shutoff a ramp meter than it is a traffic signal. The question that remains is, “Which conditions 

should be met to start and stop the metering?”

In summary, when testing the HERO algorithm against the ALINEA, SDRMS, and no ramp 

metering scenarios using the ICM Aimsun network, a complete and wide-ranging network 

covers the San Diego, CA, I–15 mainline corridor, on/off-ramps, and arterial roads.

Different MOPs to assess the efficiency of each algorithm include: 

• Average travel time on the entire mainline.

• Average weighted harmonic speed on the entire mainline and all ramps.

• The total distance and time traveled by a vehicle over the entire network (mainline, 

on-ramps, and arterials).

The team analyzed many tests to calibrate the different parameters needed by each method. In 

the best scenario, HERO outperformed all of the other algorithms, specifically:

• A 2-percent (19-s/vehicle (s/veh)) gain over ALINEA and a 5-percent (45-s/veh) gain 

over no ramp metering for mainline travel time.

• A 1-percent (0.7-mi/h) gain over ALINEA and a 2-percent (1.3-mi/h) gain over no ramp 

metering for the average weighted harmonic speed on the mainline and ramps. 

• A higher vehicle distance traveled over the other methods, showing a higher throughput. 

These results show that HERO is more efficient at reducing travel time on the mainline and 

on-ramps, regulating on-ramp merges to the mainline, and reducing the overall delay. This 

benefit is at no cost to the overall network, indicating that no delay transfers from the mainline 

travelers to other users of the network. 
82 

While these results favor HERO, this method requires careful calibration to obtain overall 

benefits. It is relatively simple to lower travel times on the mainline but still lead to overall worse 

results. Because of the need for more infrastructure (e.g., detectors, communications equipment) 

and the refinements needed by the HERO method, the ramp metering operator must carefully 

evaluate the cost-benefit ratio associated with its use. Even though ALINEA may show worse 

results, its simplicity and robustness is a factor in its favor. Finally, while the team examined 

only preliminary results, another method was being tested: optimized ramp coordination (based 

on California Partners for Advanced Transit and Highways’ CRM algorithm). The objective of 

developing this new method is to use the strengths of HERO (the coordination of the behavior of 

multiple ramps) while limiting its weaknesses (the need to calibrate a several parameters).

OPEN-ACCESS MLs ON THE RIGHT-HAND SIDE

Typically in the United States, managed HOV/HOT lanes provide an improved level of service 

to drivers and are at the far left-hand side, next to the median. Although locating the ML on the 

right-hand side would reduce its capacity, the overall impact on all lanes could be beneficial. The 

Alternative Freeway Designs at Merge and Diverge Segments project compared right-side ML

designs to left-side designs (i.e., with continuous access or partial access). Reduction by 

300 veh/h in the overall capacity of a four-lane freeway occurs if the right-side ML capacity is 

reduced from 2,000 to 1,700 veh/h and the other three lanes remain at 2,000 veh/h. However, 

MLs on the left-hand side can result in a much larger capacity loss since all lanes can experience 

the 300-veh/h capacity loss due to vehicles weaving from the right to the left. Even if only the 

middle two lanes were impacted by this weave friction and turbulence, there may be a capacity 

loss of 300 x 2 = 600 veh/h, or twice that of a right-side ML. 

HOV lanes are usually for vehicles with a driver and one or more passengers, and are mostly on 

freeways with high demand and traffic congestion. HOV lanes have the potential to save travel 

time, increase throughput, and offer more reliable travel experiences. There is extensive 

literature investigating the effectiveness of HOV lanes, both in simulation and after field 

deployments. Many studies investigated different HOV lane designs, and most of them focused 

on the interaction between the HOV lane and the GP lane. Avelar et al. (2016) investigated the 

influence of GP lane traffic on ML speeds. The results from this study indicate that there is a 

significant positive relationship between the ML speed and the GP lane speed. Besides, the 

presence of pylons may help reduce the influence of GP lane speed on the ML. To better 

understand the relationship between GP and HOV lanes, Qi et al. (2015) conducted research on 

lane changing behavior along HOV facilities. The study used aerial photo data collected from the 

segment of SR 60 in Moreno Valley, CA, and compared traffic data of limited HOV lane access 

and continuous HOV lane access. Statistical tests compared the two HOV lane configuration 

types. The results suggest that two configuration types (i.e., limited and continuous) have no 

significant difference in the percentage of traffic volume of HOV lanes. The difference between 

the average speeds of HOV and mix-flow lanes is lower for the design with continuous HOV 

lane access.

Shan et al. (2016) investigated a partially limited access design of HOV lane to improve the 

performance of HOV facilities. To reduce the influence of cross-weave flow between the HOV 

and GP lanes, buffers were placed on selected segments of freeway between HOV and GP lanes. 

Simulation tests with three influential factors (cross-weave flow, number of GP lanes, and buffer 
83 

length) indicate that the HOV cross-weave flow has a negative effect on the capacity of GP 

lanes, while buffer-separated HOV facilities are better at regulating traffic flow. The length of 

the buffer does not affect the capacity of GP lanes. Furthermore, a simulation network of the 

same segment of eastbound SR 210 shows that the efficiency of the overall network with 

partially limited access design is 9 percent higher than that of the designs with limited access 

HOV facility and 4 percent higher than that of the designs with continuous access HOV facility. 

Fitzpatrick and Avelar (2016) investigated safety issues involving buffer-separated MLs. This 

study looked at the relationships between crashes and buffered ML features including buffer 

width, lane width, and shoulder width. Annual ADT data of freeways with three and four GP 

lanes in California and Texas used generalized data analysis of linear mixed models and showed 

that reductions in ML envelope widths (shoulder, lane, and buffer width) are associated with 

more crashes. In addition, reductions in freeway lane width or shoulder width are associated with 

more crashes.

Previous studies suggest that the traffic condition of GP lane has a significant association with 

the HOV lane, and many features of the freeway, such as lane width, shoulder width, and 

separation designs, affect the performance of the HOV lane. However, all of the previous studies 

looked at left-side HOV lanes. To investigate whether the location of the ML would influence its 

performance, the team conducted simulation evaluations for HOV lanes on the right-hand side. 

Methods

This section introduces modeling and analysis of the proposed new design (right-side HOV 

lanes) in a microsimulation environment to derive insights on the effectiveness. To evaluate the 

proposed strategy comprehensively under varying conditions, the team first built synthetic 

simulation networks for merging, diverging, and weaving segments and conducted a large 

number of sensitivity analyses to select best design parameters for different traffic conditions. 

Then, the team implemented the strategy with selected design parameters on a real-world 

simulation network to help the local agency understand the potential of this strategy to solve 

local congestion problems. 

Assumptions

The total length of the synthetic network mainline segment is around 3.7 mi for on-ramp 

simulations and 3.5 mi for off-ramp simulations, as shown in figure 48. Based on the AASHTO 

Green Book, the team assumed a traffic speed of 70 mi/h for the mainline and 30 mi/h as the 

lower speed range for the on-ramp and off-ramp in the simulations (AASHTO 2018). In the 

simulations, the team used recommended lengths for the acceleration and deceleration lanes of 

1,300 and 560 ft, respectively (WSDOT 2017). The team conducted the simulations for the 

merge (on-ramp area) and diverge (off-ramp area) separately to assess how various parameters 

would affect operations of the network under different ML strategies.
84 

© 2019 Jiaqi Ma.

A. Merge benchmark design. 

© 2019 Jiaqi Ma.

B. Merge alternative design.

© 2019 Jiaqi Ma.

C. Diverge benchmark design. 

© 2019 Jiaqi Ma.

D. Diverge alternative design. 

Figure 48. Illustrations. Synthetic simulation network (merge and diverge).

In addition to the merge and diverge segments, the team conducted simulation experiments on a 

weaving segment. The geometry of the weaving segment is shown in figure 49 and it is the basic 

design of a weaving segment as defined in the HCM, where both on-ramp and off-ramp vehicles 

are required to make at least one lane change to reach their destination. Also, the length between 

the ramps is usually less than a threshold, beyond which the merge and diverge area may not 

affect each other. The network reduces to the simple combination of the merge and diverge in 

figure 48. 
85 

© 2019 Jiaqi Ma.

A. Weaving benchmark design, shared acceleration lane. 

© 2019 Jiaqi Ma.

B. Weaving benchmark design, separate acceleration lanes. 

© 2019 Jiaqi Ma.

C. Weaving alternative design, shared acceleration lane. 

© 2019 Jiaqi Ma.

D. Weaving alternative design, separate acceleration lanes. 

Figure 49. Illustrations. Synthetic simulation network geometry (weaving). 

Experimental Design

As listed in table 14 below, five key factors that may affect the performance of the two designs 

are included in the simulation for sensitivity analysis: the mainline congestion (V/C ratio), ramp 

volume, number of lanes, ramp traffic HOV percentage, and merge/diverge buffer area length. 

The team also considered analysis of weaving area length. For each of the 72 total scenarios, the 

team conducted 10 simulations to account for stochasticity.
86 

Table 14. System parameters considered in study.

Parameters Levels Number of 

Levels

HOV position—HOV_POS HOV lane on the left-hand side 

HOV lane on the right-hand side Two

Mainline input volume to capacity 

ratio—Volperlane 55 percent, 80 percent, 110 percent Three

On/off-ramp volume—VolRamp 

(veh/h/l) 500; 1,000; 1,500 Three

Number of lanes—NoLanes 3, 4 Two

Ramp traffic HOV percentage 0.25, 0.5 Two

Weaving area length (mi) 0.5, 1 Two

The team found the capacity of the freeway network in Vissim is about 2,000 veh/h/l based on 

the initial data collection on the synthetic simulation network with specified driver parameters

used in the previous studies and calibrated networks (TariVerdi and Miller-Hooks 2012). 

Therefore, the team set the three mainline input volumes to 1,100 veh/h/l (V/C = 0.55); 

1,600 veh/h/l (V/C = 0.8); and 2,200 veh/h/l (V/C = 1.1) to test the alternative design under 

different congestion levels. Ramp traffic HOV percentage directly affects the number of lane 

changes, which indicates the intensity of disruptions to the mainline traffic. Generally, a merge 

area buffer of 0.25 mi is desirable for HOV lanes (Cothron et al. 2003). These openings should 

safely accommodate vehicles weaving to and from the HOV lane. Longer merge (or diverge) 

area buffers can have two distinct effects. Positively, they may give merging vehicles more 

chances to merge into the mainline smoothly. On the negative side, particularly for the 

alternative design, regular vehicles may have a higher impact on HOV lane operations. 

The results analyzed two performance measures: average delay and throughput. GP vehicles and

HOVs on both the mainline and on/off-ramp are measured separately. The sum of mainline and 

on/off-ramp throughputs determines total throughputs for both types of vehicles. The team

calculated performance measures separately for all scenarios, including throughputs and delays 

of GP, HOV, and total vehicles on both the mainline and off-ramp. For merge scenarios, the

team collected data on the HOV and GP lanes at the exit of the network. These variables are 

compared across different scenarios in the final statistical analysis.

Simulation Approach 

The team used the PTV Vissim 10.0 microscopic traffic flow simulation tool to perform the 

experiments. The team modeled HOV lanes by setting the blocked vehicle type as a GP vehicle. 

To avoid additional confounding factors in the simulation, HOV vehicles only use HOV lanes 

and GP vehicles only use GP lanes on the freeway. The simulation on synthetic networks uses a 

set of calibrated Vissim behavior model parameters from an earlier study (TariVerdi and 

Miller-Hooks 2012). Table 15 lists the driver behavior parameters. 
87 

Table 15. Driver behavior parameters. 

Parameter Definition Default 

Value

Value for this 

Study

CC2 Desired safety following distance 13.12 ft 39.37 ft 

CC4 Lower following threshold 0.35 mi/h 0.1 mi/h

CC5 Upper following threshold 0.35 mi/h 0.1 mi/h 

SDRF Safety distance reduced factor: affects safety 

distance during lane changing 0.6 0.1 

LBD Look-back distance 656 ft 3,280 ft 

Results and Analysis—Simulation Runs

Positioning HOV lanes on the right-hand side of the freeway may result in a deterioration in 

HOV lane performance, though the GP lanes and total system performance may improve. 

Therefore, the team first wanted to make sure the travel speed in the HOV lane is always faster 

than the GP lanes because the purpose the HOV lane is to provide better and more reliable 

services. Figure 50 shows that when the HOV percentage is 0.5, the HOV delay of the alternative 

design is higher than the GP delay in most cases. The plots show the differences between GP 

delay and HOV delay. The delay of HOV is smaller than the GP only in extremely congested 

conditions where the HOV percentage is 0.5. In reality, the HOV vehicles will not stay in the 

HOV lane if the HOV lane is slower than the GP lanes. Besides, the 50 percent HOV case is rare 

in the real world. Because of its rarity, the team did not present the results for the 50 percent

HOV cases. When there is 25 percent HOV, there are a few cases where the delay of HOV is a 

little higher than GP for both traditional (i.e., HOV lane positioned on the left-hand side) and 

alternative (i.e., HOV lane positioned on the right-hand side) designs. These cases happen when 

the mainline traffic volume is relatively low for the four-lane alternative network in which the 

improvements to GP vehicles as a result of enhanced merging behavior is smaller compared to 

the adverse impacts on the HOV vehicles. In this study, the team was more interested in the 

performance under congested scenarios. Figure 50 indicates similar patterns of before and after 

delay differences between GP and HOV when there is 25 percent HOV.

Implementation of all levels of the six simulation parameters uses a factorial design of the 

simulation experiment. Plots of comparison between the two designs under different parameter 

combinations are presented. Next, the team conducted statistical analyses to investigate the 

significance of effectiveness for each parameter under a range of network scenarios. Considering 

that multiple scenarios provide similar insights, this report shows the following selected samples 

of plots and analysis: four-lane freeway, V/C = 0.8/1.1, HOV percentage = 0.25, on-ramp 

volume = 1,500. Note that the results presented in this example generally apply to all other 

scenarios unless the exceptions are specifically mentioned.

Analysis of Merge Area

Results showed that when traffic density is low (e.g., when the input volume is 55 percent of 

capacity and on-ramp volume is 500 veh/h/l), both GP and HOV throughputs of the alternative 

design with the HOV lane on the right-hand side are the same as the normal design. The number 

of lanes show no significant impacts, thus only four lane cases are discussed here. When the 
88 

traffic density becomes higher, GP throughputs of the alternative design become higher than the 

normal design; however, HOV throughputs decline slightly in most of the scenarios. The team 

interpreted this to the fact that congestion caused by merging traffic blocks the HOV lane under 

the alternative design, while HOV vehicles in the normal design could travel with no disruption. 

In addition, the sensitivity analysis indicated that going from a certain degree of congestion 

(i.e., V/C ratio of 0.8) to severe congestion (i.e., V/C of 1.1) does not affect the HOV throughput 

performance, which can be observed by comparing the two plots in equation 6. In terms of total 

throughputs, the alternative design still performs significantly better than the normal design. 

Next, the GP delay and total delay of the alternative design is remarkably lower than the delay of 

the normal design even though the HOV delay becomes higher. The GP vehicles have higher 

delays and the differences are between 0–700 s. To better understand how much improvement is 

gained by changing HOV lanes to the right-hand side, the percentage of change was calculated.

(6)

Where:

p = percentage of change.

MOEright = measure of effectiveness for HOV lanes on the right. 

MOEleft = measure of effectiveness for HOV lanes on the left.

Generally, the overall percent change in throughput is negligible. GP delay is lower, but mainline 

and HOV delay is much higher in the alternative design. However, since the number of GP 

vehicles greatly exceeds the number of HOV vehicles, the proposed right-side HOV design 

results in approximately a 20-percent delay reduction.

ANOVA Tests

To further test and quantify the effects of each parameter on throughputs, ANOVA tests and 

paired T-tests were applied to GP throughputs and HOV throughputs separately. Then, a 

regression analysis was applied separately. 

Table 16 shows the results of the ANOVA tests in throughputs and delays. Table 16 suggests 

that all factors significantly affect the throughput values, except for the number of GP lanes. 

Similar to the throughput findings, all factors significantly affect the delay, except that the 

number of lanes is insignificant to HOV delay.
89 

Table 16. Throughput and delay ANOVA table.

Measure Response GP p-Value HOV p-Value Total p-Value 

Throughputs HOV (right) <2.2E−16 0.003615 1.434E−07 

Throughputs Number of lanes 0.1881 0.005253 <2.2E−16 

Throughputs V/C ratio <2.2E−16 <2.2E−16 <2.2E−16 

Throughputs On-ramp volume 1.789E−09 1.092E−07 2.820E−10 

Throughputs HOV percentage 3.578E−05 <2.2E−16 <2.2E−16 

Average delay HOV (right) <2.2E−16 <2.2E−16 <2.2E−16 

Average delay Number of lanes <2.2E−16 0.1932 <2.2E−16 

Average delay V/C ratio <2.2E−16 <2.2E−16 <2.2E−16 

Average delay On-ramp volume <2.2E−16 <2.2E−16 <2.2E−16 

Average delay HOV percentage 0.00837 <2.2E−16 9.211E−07 

p = probability indicating the extent to which a factor is insignificant. 

Paired T-tests

Paired T-tests evaluate whether the two designs are different in each scenario. The null 

hypothesis is that the mean difference of the two designs is 0, while the alternative hypothesis is 

that the mean difference of the two designs is not 0. Estimations of mean difference and the 

probability indicating the extent to which a factor is insignificant (p) values are provided. Results 

of representative scenarios are listed. There is a significant difference between the throughputs of 

GP vehicles as shown in table 17 and table 19. 

Contrarily, there is no significant difference between the HOV throughputs. The estimations of 

GP throughput differences between the design of the HOV lane on the right-hand side and the 

normal design HOV lane. Table 17 also indicates that in terms of GP delay, the two designs are 

different. For the GP delays, the column “estimated mean of right—left” are all negative, 

indicating the GP delay is lower in the alternative design compared to the normal design. In 

terms of HOV delays in table 18, the two designs are also significantly different. The column 

“estimated mean of right—left” are all positive, indicating the HOV delays are larger in the 

alternative design than the normal design. Table 19 shows the difference between the weighted 

total throughputs of the two designs for selected scenarios. Generally, when the V/C ratio is 0.55, 

the tests show there are no significant differences between the two. When the V/C ratio is higher, 

the test results are significantly different. The total throughputs of the alternative design are 

higher than the normal design where the improvements range from 140–170 veh/h/l. The total 

average vehicle delay shown in table 20 decreases in the alternative design, ranging from 

70–150 s per vehicle. In total, the alternative design outperforms the normal design for the merge 

area for the synthetic simulation network and under the given system parameters.
90 

Table 17. T-test results—average delay and throughputs on the GP lanes.

Measure Number of 

Lanes

HOV 

(Percent) 

V/C 

Ratio

OnRamp 

Volume

(veh/h) 

Estimated 

Mean of 

Right—

Left

p-Value 

Throughputs Four 0.25 0.8 1,000 152 4.02E−13 

Throughputs Four 0.25 0.8 1,500 130 2.56E−06 

Throughputs Four 0.25 1.1 1,000 147 2.18E−13 

Throughputs Four 0.25 1.1 1,500 124 7.73E−07 

Average delay Four 0.25 0.8 1,000 −161 1.34E−14 

Average delay Four 0.25 0.8 1,500 −131 7.93E−09 

Average delay Four 0.25 1.1 1,000 −306 5.90E−14 

Average delay Four 0.25 1.1 1,500 −223 6.21E−06 

Table 18. T-test results—average delay and throughputs on the HOV lanes. 

Measure Number of 

Lanes

HOV 

(Percent) 

V/C 

Ratio

OnRamp 

Volume

(veh/h)

Estimated 

Mean of 

Right—

Left

p-Value 

Throughputs Four 0.25 0.8 1,000 0.1 0.88 

Throughputs Four 0.25 0.8 1,500 10.6 0.11 

Throughputs Four 0.25 1.1 1,000 −0.9 0.49 

Throughputs Four 0.25 1.1 1,500 19.1 0.29 

Average delay Four 0.25 0.8 1,000 19 2.75E−07 

Average delay Four 0.25 0.8 1,500 41 2.41E−15 

Average delay Four 0.25 1.1 1,000 25 3.89E−10 

Average delay Four 0.25 1.1 1,500 50 5.20E−10 

Table 19. T-test results—total throughputs on the GP and HOV lanes.

HOV (Percent) V/C 

Ratio

On-Ramp 

Volume (veh/h) 

Estimated Mean 

of Right—Left p-Value 

0.25 0.8 1,000 171 4.59E−11 

0.25 0.8 1,500 149 7.29E−10 

0.25 1.1 1,000 165 2.05E−11 

0.25 1.1 1,500 144 5.60E−14 

Table 20. T-test results—total delay on the GP and HOV lanes. 

HOV (Percent) V/C Ratio On-Ramp Volume

(veh/h) 

Estimated Mean 

of Right—Left p-Value 

0.25 0.8 1,000 −151 7.49E−11 

0.25 0.8 1,500 −101 3.67E−09 

0.25 1.1 1,000 −77 7.67E−13 

0.25 1.1 1,500 −102 1.19E−13 
91 

Prediction Models 

The team performed multivariate linear regression analyses to predict the total throughputs and 

total delays. The purpose of the model was to provide an analytical tool to estimate the potential 

benefits of positioning HOV lanes on the right-hand side. All statistical analyses were performed 

using the statistical software R (The R Foundation 2019). The level of significance is set at

p < 0.05, and the confidence level is set at 95 percent. The following influencing factors are 

analyzed as numerical values: V/C ratio and on/off-ramp volume. Categorical variables are used 

for number of lanes, HOV location, and weaving length. The percent of HOV is not included 

since high-HOV-percent simulations are not realistic in real-world practices. Regression 

prediction models are generated based on simulation data. The R-squared value of the throughput 

model is 0.82. This value indicates that the model can explain and approximate about 82 percent 

of the throughput data. Similarly, the delay model with an R-squared value of 0.81 indicates that 

the model can explain and approximate about 81 percent of the delay data.

(7)

(8)

Where:

TotalTP = total throughput. 

Iright = equal to 1 for right-side HOV lanes.

Lanes = number of lanes. 

VCRatio = volume-to-capacity ratio.

OnV = on-ramp volume. 

R2

 = coefficient of determination. 

Analysis of Diverge Area 

By checking the results of the GP and HOV throughputs on the mainline and off-ramp 

separately, the team found there is no difference between the two designs in terms of 

throughputs. When the off-ramp traffic volume is 1,500 veh/h/l and number of lanes is four, 

generally the alternative design performs better than the normal design in terms of delay. The GP 

delay is lower for both the mainline and off-ramp in the alternative design. The HOV delay is 

also lower in the alternative design for the off-ramp because the HOV lane is on the right-hand 

side and HOV vehicles do not need to wait for the GP vehicles to exit the freeway. The mainline 

delay of the alternative design is higher because the congestion at the diverge area blocks the 

HOV lane located on the right-hand side. In terms of total delay, the difference is small between 

the two, and the total delay is slightly lower in the alternative design than in the normal design. 

Generally, the GP vehicles have higher delays, but the differences are minor (less than 15 s). 
92 

Thus, the team concluded that the performance between the two designs in the diverge area is not 

significant as compared to the performance in the merge area. The differences in GP delay are 

less than 10 percent. The mainline HOV delay increases from 20 to 40 percent, and off-ramp 

HOV delay decreases from 30 to 40 percent. The total delay decreases about 10 percent.

To test and quantify the effects of parameters on throughputs and delays, ANOVA tests were

applied to GP and HOV lanes separately. Table 21 through table 23 suggest that the two designs 

have no significant difference in throughputs since the p-values for HOV positions are larger 

than 0.05. They also suggest that the two designs are significantly different in terms of delay 

since the p-values are all smaller than 0.05. ANOVA tests of total measurements show that the 

two designs have no difference in total throughputs, while the differences in total delays are 

relatively significant.

Table 21. ANOVA throughputs.

Response Mainline 

GP p-Value 

Mainline HOV 

p-Value 

Off-Ramp 

GP p-Value 

Off-Ramp 

HOV p-Value 

HOV (right) 0.7054 0.9783 0.78758 0.9888 

Number of lanes <2.2E−16 <2.2E−16 0.01002 <2.2E−16 

V/C ratio <2.2E−16 <2.2E−16 <2.2E−16 0.4532 

Off-ramp volume <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

HOV percentage 0.9942 <2.2E−16 0.82848 <2.2E−16 

Table 22. ANOVA average delay. 

Response Mainline 

GP p-Value 

Mainline HOV 

p-Value 

Off-Ramp 

GP p-Value 

Off-Ramp 

HOV p-Value 

HOV (right) 0.0146 <2.2E−16 0.02479 <2.2E−16 

Number of lanes 0.45810 <2.2E−16 0.01002 0.234211 

V/C ratio <2.2E−16 <2.2E−16 <2.2E−16 0.003537 

Off-ramp volume <2.2E−16 0.434557 <2.2E−16 <2.2E−16 

HOV percentage 0.06190 <2.2E−16 0.82848 <2.2E−16 

Table 23. ANOVA total throughputs and delay. 

Response Throughputs Delays 

HOV (right) 0.71170 0.05985 

Number of lanes 0.01625 0.02167 

V/C ratio <2.2E−16 <2.2E−16 

Off-ramp volume 0.92918 <2.2E−16 

HOV percentage <2.2E−16 <2.2E−16 

Paired T-tests

Paired T-tests were conducted for all scenarios. Table 24 and table 25 list the results of total 

throughputs and total delays, respectively. The throughputs show no differences, while the 

differences in delays are significant. The estimate means the difference between the designs 

where the HOV lane is on the left-hand side and where the HOV lane is on the right-hand side 
93 

are negative, but the values are small (nearly 0), indicating that the improvement in total delays 

is small.

Table 24. Total throughput T-tests. 

Number of 

Lanes

HOV 

(Percent) V/C Ratio

On-ramp 

Volume

(veh/h) 

Estimated Mean 

of Right—Left p-Value 

Four 0.25 0.9 1,000 0.1 0.56 

Four 0.25 0.9 1,500 −0.42 0.07 

Four 0.25 1.2 1,000 22.72 0.1 

Four 0.25 1.2 1,500 16.15 0.12 

Table 25. Total delay T-tests.

Number of 

Lanes

HOV

(Percent) V/C Ratio

On-ramp 

Volume

(veh/h) 

Estimated Mean 

of Right—Left p-Value 

Four 0.25 0.9 1,000 −0.48 5.34E−08 

Four 0.25 0.9 1,500 −0.38 0.005 

Four 0.25 1.2 1,000 −0.36 0.54 

Four 0.25 1.2 1,500 −1.74 0.018 

Prediction Model 

Since there is no substantial difference between the total throughputs of the two designs, and 

only the V/C ratio is significant in the test, the team only predicted total delays in this case. 

Regression prediction models were generated based on simulation data. The R-squared value of 

the model is 0.86. It indicates that the model can explain and approximate about 86 percent of 

the delay data.

(9)

Where:

Iright = equal to 1 for right-side HOV lanes.

Lanes = number of lanes. 

VCRatio = volume-to-capacity ratio.

OnV = on-ramp volume.

R2 = coefficient of determination. 
94 

Analysis of Weaving Area 

The results of the 0.5-mi and 1.0-mi weaving length area are presented separately. In this design, 

the team allowed 0.25 mi before or after the merge/diverge area for vehicles to make lane 

changes and enter the target lanes. When the diverge length is 0.5 mi, the acceleration lanes are 

connected as an additional lane. In this case, the on/off-ramp vehicles actually have a longer 

distance to change to their target lanes and potentially improve the system. 

Case 1: Weaving Length = 0.5 mi 

Generally, the GP throughputs are higher in the alternative design. The mainline HOV 

throughput is also higher in the alternative design, while the off-ramp HOV throughput shows no 

significant difference. The alternative design performs much better than the normal design in 

total throughputs. When the V/C ratio is 0.8, the alternative design provides higher HOV 

throughputs than GP. By checking the simulation, the team found that in this case, the on-ramp is 

congested in the normal design while there is no congestion in the alternative design. Thus, in the 

normal design case, a substantial number of vehicles were not able to enter the network, which 

causes lower HOV throughputs. When the V/C ratio is 1.1, congestion happens in both designs. 

There is no significant difference between the throughputs. 

The GP delay was lower and the HOV delay was higher in the alternative design for both the 

mainline and off-ramp. However, the differences in HOV delays between the two were smaller 

(less than 50 s) than those of GP. In terms of total delays, the alternative designs were about 60 s 

lower than the normal delay. The delay of off-ramp HOV drops in the normal design as the V/C 

ratio increases. This was due to blocking of GP vehicles at the merge area, which leads to a 

decrease in the traffic volume at the weaving area. Thus, the HOV vehicles could exit the 

freeway more smoothly. 

The total throughputs increased about 8 percent. The GP delays and total delays decreased about 

60 percent when the V/C ratio is 0.8, and by 20 percent when the V/C ratio is 1.1. HOV delay 

increases about 100 percent when the V/C ratio is 0.8, and by 500 percent when the V/C ratio is 

1.1. Thus, the team concluded that the alternative design performs better for the weaving area 

with 0.5-mi weaving length, and when the V/C ratio is 0.8, the improvement is most significant.

Case 2: Weaving Length = 1 mi 

There is higher GP throughput in the alternative design, and the off-ramp shows no significant 

difference between the two designs. Total throughputs are higher in the alternative design. When 

the V/C ratio is 0.8, the alternative design also provides higher HOV throughputs than GP as the 

team mentioned in case 1. The reason is the same: congestion happens only in the design with 

the HOV lane on the left-hand side when V/C ratio is 0.8. 

The GP delay is lower and HOV delay is higher for both the mainline and off-ramp in the 

alternative design. In terms of total delays, the alternative design with the HOV lane on the 

right-hand side performs better in general with lower total delays and higher total throughputs. 

Generally, the change in throughputs are insignificant. The amount of total GP delays decreases 

between 20–30 percent, while total HOV delays increase by 600 percent. Since the quantity of 

HOV delays is small, the total delays still decrease by about 20 percent. 
95 

ANOVA Tests

Table 26 and table 27 show the results of ANOVA tests in throughputs and delays, respectively. 

The results suggest that all of the parameters significantly affect throughputs and delays, except 

that the off-ramp volume does not affect the mainline GP delay. Table 28 provides the ANOVA 

tests of total measurements. The two designs show no difference in total throughputs, while there 

are significant differences in total delays. 

Table 26. ANOVA tests of total measurements—throughputs. 

Response Mainline GP 

p-Value 

Mainline HOV 

p-Value 

Off-Ramp GP 

p-Value 

Off-Ramp 

HOV p-Value 

HOV (right) 8.049E−11 <2.2E−16 <2.2E−16 <2.2E−16 

Number of lanes <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

V/C ratio <2.2E−16 <2.2E−16 <2.2E−16 3.368E−06 

Off-ramp volume <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

HOV percentage <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

Weaving length <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

Table 27. ANOVA tests of total measurements—average delay. 

Response Mainline GP 

p-Value 

Mainline HOV 

p-Value 

Off-Ramp GP 

p-Value 

Off-Ramp 

HOV p-Value 

HOV (right) <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

Number of lanes <2.2E−16 0.01179 <2.2E−16 0.024792 

V/C ratio <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

Off-ramp volume 0.2373 <2.2E−16 0.00047 0.00208 

HOV percentage <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

Weaving length <2.2E−16 <2.2E−16 <2.2E−16 <2.2E−16 

Table 28. ANOVA total throughputs and delays. 

Response: Total Throughputs Delays 

HOV (right) 0.3719 <2.2E−16 

Number of lanes <2.2E−16 <2.2E−16 

V/C ratio <2.2E−16 <2.2E−16 

Off-ramp volume 0.7192 <2.2E−16 

HOV percentage <2.2E−16 <2.2E−16 

Weaving length <2.2E−16 <2.2E−16 

Paired T-tests

Further paired T-tests quantify differences between the two designs. Table 29 shows that for both 

0.5-mi and 1.0-mi cases, the alternative design performs better with higher throughputs and 

lower delays. When the weaving length is 1.0 mi, improvements in throughput are between 

30–80 veh/h/l. This is more stable and greater on average compared with the 0.5-mi case. The 

delay decrease in the 1.0-mi case is between 40–90 s, which is better on average compared to the 

0.5-mi case. Overall, the alternative design performs better at the weaving segments.
96 

Prediction Model 

The team also performed multivariate linear regression analyses to predict the total throughputs 

and delays. The length of the weaving segment is a categorical variable since only two lengths 

were tested, and the network designs vary in separate ways based on real-world cases. The 

R-squared values of the two models are 0.85 and 0.75, respectively. It indicates that the 

throughput model can approximate about 85 percent of the throughput data, while the delay 

model can approximate about 75 percent of the delay data. 

(10) 

(11) 

Where:

TotalTP = total throughput. 

Iright = equal to 1 for right-side HOV lanes.

Lanes = number of lanes. 

VCRatio = volume-to-capacity ratio.

OnV = on-ramp volume. 

OffV = off-ramp volume. 

I1-mile = equal to 1 for 1-mile weaving lengths. 

R2

 = coefficient of determination. 
97 

Table 29. Total throughput and delay T-tests.

Number 

of Lanes

HOV 

Percent

Weaving 

Length

(ft)

V/C Ratio

On-ramp 

Volume

(veh/h) 

Off-ramp 

Volume

(veh/h) 

Estimated Mean 

of Throughput 

(Right)—

Throughput 

(Left)

Throughputs 

p-Value 

Estimated 

Mean of Delay 

(Right)—

Delay (Left)

Delay

p-Value 

Four 0.25 2,625 0.8 1,000 1,000 8.3 0.01 −20.51 0.007 

Four 0.25 2,625 0.8 1,000 1,500 2.45 0.17 −7.29 2.0E−06 

Four 0.25 2,625 0.8 1,500 1,000 83.95 2.4E−07 −72.59 7.3E−06 

Four 0.25 2,625 0.8 1,500 1,500 106.72 3.8E−09 −70.06 0.0002 

Four 0.25 2,625 1.1 1,000 1,000 45.1 7.1E−08 −9.80 0.002 

Four 0.25 2,625 1.1 1,000 1,500 50 1.4E−05 −34.33 7.47E−07 

Four 0.25 2,625 1.1 1,500 1,000 41.37 0.001 −1.78 0.061 

Four 0.25 2,625 1.1 1,500 1,500 97.4 5.06E−09 −38.48 6.16E−10 

Four 0.25 5,250 0.8 1,000 1,000 65.6 4.1E−11 −80.04 2.47E−06 

Four 0.25 5,250 0.8 1,000 1,500 76.82 1.5E−10 −91.59 6.98E−10 

Four 0.25 5,250 0.8 1,500 1,000 53.35 1.9E−09 −73.73 1.66E−07 

Four 0.25 5,250 0.8 1,500 1,500 39.75 2.4E−06 −42.88 5.92E−05 

Four 0.25 5,250 1.1 1,000 1,000 59.85 4.1E−11 −38.65 4.23E−10 

Four 0.25 5,250 1.1 1,000 1,500 66.175 1.6E−13 −42.04 9.56E−07 

Four 0.25 5,250 1.1 1,500 1,000 61.87 9.4E−07 −69.70 3.37E−10 

Four 0.25 5,250 1.1 1,500 1,500 49.97 1.0E−08 −57.70 3.51E−06 
98 

I–66 Calibrated Network 

To test whether the alternative design would improve the performance on a real network since 

the previously presented results were from synthetic networks, a calibrated network of I–66’s 

freeway segment in Vissim tested the design for real-world applications. The normal design and 

the alternative design were simulated in this network, and the total throughputs and delays were

compared. The same calibrated network as shown in figure 28 was used. The section of split 

merging, diverging, and weaving design shows details of the model calibration. There is an HOV 

lane on the left-hand side of the base case calibrated network. For the HOV lane on the 

right-hand side, the team modified the network and specified the rightmost lane as the HOV lane, 

with the same HOV demand assigned to the HOV lane on the right-hand side. The team 

performed simulations 10 times to account for stochasticity.

Overall performance of the alternative design is consistent with the previous findings from 

simple networks. With the proposed HOV lane on the right-hand side, GP lane performance 

improves significantly, with a 4-percent throughput increase and a 19-percent delay reduction. 

Also, HOV lane performance degrades as expected. In terms of total performance, the alternative 

design has slightly higher throughput overall, but the difference is not significant. The average 

delay of the alternative design is much lower than the original case. The decrease in average 

delay is about 9 percent. Based on the I–66 calibrated network simulation, changing the HOV 

lane to the right-hand side would improve overall network performance in terms of vehicle 

delay, and total vehicle throughput would remain the same. In addition, GP lane delay was 

higher than HOV lane delay, as was assumed. This means the new design still ensures a higher 

level of service for HOV vehicles than GP vehicles, though the relative difference in 

performance is smaller. This verifies the feasibility of implementing HOV lanes on the 

right-hand side for this real-world freeway segment.

Conclusions

HOV lanes are in use worldwide. This study investigated an alternative design of freeways with 

the HOV lane on the right-hand side and how it can solve the congestion problem at 

merge/diverge and weaving areas. The objective was to find whether the alternative design 

would be helpful in reducing congestion, and to develop recommendations for the DOT and local 

agencies to manage HOV lanes and estimate traffic performance. Based on the simulations, 

several conclusions derived from the simulation tests and statistical analyses, including: 

• Generally, the alternative design with the HOV lane on the right-hand side will help 

improve the performance of GP lane vehicles in most cases. However, the alternative 

design will degrade HOV lane performance, especially for HOV vehicles that do not exit 

at ramps. The system throughout improvements are significant for merge and weaving 

areas, ranging from 5–10 percent on average. The system delay enhancements are 

significant under all scenarios, ranging from 10–30 percent on average. 

• The alternative design will reduce delay and increase throughput, though the 

improvements in throughput are sometimes not significant. 
99 

• Even though the HOV performance may be disturbed more in the case of HOV lanes on 

the right-hand side, HOV lane vehicles still experience a higher level of service than GP 

lane vehicles under the same background settings, though the difference in the level of 

service is smaller compared to HOV lanes on the left-hand side. This is dependent on the 

HOV traffic percentage. Lower HOV percentages lead to higher differences and better 

levels of service.

• The study also suggests that it is practical to implement the alternative design on freeway 

segments, especially for areas with low HOV traffic volume.

o A case study on the calibrated I–66 network shows the alternative design contributes 

to a 10-percent decrease in total delays and a 20-percent decrease in GP vehicle 

delays, which indicates a significant improvement in the network performance. 

For future research, more levels of HOV percentages and V/C ratios could be tried to provide 

more accurate results. Especially for the purpose of traffic performance estimation, more tests 

and simulations would be helpful to improve the accuracy of the analysis, particularly the 

regression models. Next, it might be valuable to add more system parameters, such as vehicle 

composition and more complex geometric designs. Furthermore, in terms of its influence on 

HOV vehicles, it is necessary to investigate the effects on people’s willingness to drive in HOV 

lanes. People’s willingness to drive in HOV lanes also concerns policy questions of 

implementing the new design.

101 

CHAPTER 5. CONCLUSIONS

Given the prevalence of the heavy friction problem that exists at freeway merge and diverge 

areas, any low-cost solution to reduce the friction associated with vehicle maneuvers, or facilitate 

smoother merging/diverging operations, could produce great savings. This project identified five 

candidate strategies to assess within microsimulation environments.

ACCELERATION AND DECELERATION LANE DESIGNS

Restricting merging drivers to a specific new set of acceleration and deceleration lane locations 

may help to smooth traffic flow. Regarding acceleration lanes, figure 3 shows how drivers are 

prohibited from merging at the mid-section of acceleration lanes. By eliminating the uncertainty 

of merge maneuvers for both mainline and on-ramp drivers, both sets of drivers can prepare for 

and execute the merging maneuver efficiently. Regarding deceleration lanes, figure 4 shows how 

drivers merge at a mid-section of the off-ramp. Separating the turbulence associated with diverge 

maneuvers potentially mitigates mainline congestion.

The team conducted microscopic simulations to evaluate the effectiveness of the alternative 

geometric design of split merging, diverging, and weaving for reducing bottleneck congestion, 

and developed managerial insights for State DOTs to better design, enhance, and manage their 

freeway systems in the future. A partial split weaving design (with the merge portion converted 

to the split merge design) seems to work best at congested weaving areas and is the 

recommended method. These simulations only evaluated one-lane ramps, but the researchers

believe two-lane ramps would exhibit similar impacts. Although the split merge design 

effectively reduced average delays (by up to 80 percent) and increased traffic throughputs, 

particularly under medium-to-high levels of traffic congestion, the results of this study show that 

the split diverge is not effective under many traffic conditions and therefore is not recommended. 

The team tested medium and high levels of traffic demand with the 0.25-mi connection spacing. 

Results from figure 36 clearly demonstrate that the 50–50 split results in the best performance 

under most scenarios, and performs almost as well for other scenarios. Statistical tests show that 

these minor differences are not statistically significant, indicating that the team did not find that 

the split diverge design enhances diverge area performance compared to the regular diverge. 

Therefore, the team recommend the use of the split merge design at congested merge or weaving 

areas.

SPEED OPTIMIZATION (E.G., VIA DYNAMIC TRAFFIC CALMING DEVICES)

Speed harmonization and VSL studies for optimizing freeway traffic flow imply that medium 

free-flow speeds (e.g., 30 mi/h) would be more effective than low speeds (e.g., 5 mi/h) or high 

speeds (e.g., 65 mi/h). Although this project endeavors to focus on strategies not requiring CAV 

technologies, the team believes that dynamic traffic calming devices (e.g., retractable rumble 

strips) may someday be capable of coercing drivers to obey medium speeds at higher rates of 

compliance. Figure 8 illustrates this concept. 

Many of the successful speed optimization experiments produced a “zipper” pattern as shown in 

table 5. The implication is that significant speed optimization benefits only occurred under a 

narrow range of demands (i.e., near capacity) and speed limit changes. The downside of this 
102 

finding is that this narrow range of conditions producing maximum benefits, in terms of demand 

ranges and free-flow speed ranges, is often difficult to predict without first running simulations. 

HCM procedures are often used to prescreen alternatives without running simulations. However, 

a series of test runs conducted in the HCS (merge module and weaving module) using similar 

input data did not predict any benefits of free-flow speed and/or speed limit optimization. The 

resulting hypothesis is that the HCM methods are perhaps not capable of recognizing or 

analyzing the same speed harmonization-type effects and benefits that are commonly observed in 

microsimulation (and in the field). As such, an offline or real-time microsimulation model may 

be needed to detect when the zipper conditions from table 5 are in effect for any local site or 

local conditions. 

In one merge area experiment, average vehicle speeds increased from 9 to 52 mi/h simply by 

reducing the posted speed limit from 65 to 55 mi/h. Although the significant speed optimization 

benefits were observed for some weaving and merge conditions as described in chapter 4, the 

benefits were weaker (e.g., on the order of 2 to 6 percent) for diverge conditions. As such, the 

researchers’ recommendation is that agencies willing to detect zipper conditions in real time can 

consider implementing countermeasures to reduce free-flow speeds (or speed limits) when such 

conditions are present. Such countermeasures could include dynamic traffic calming devices 

(e.g., retractable rumble strips), dynamic VSL signs, or CAV speed controls. This approach also 

assumes that the offline or real-time microsimulation model is consistently and periodically 

recalibrated to reflect local field conditions accurately. 

MAINLINE METERING (I.E., DYNAMIC SIGNAL CONTROL)

A form of mainline metering to reduce demand and improve flow uses traffic metering upstream 

from a bottleneck, thereby regulating the number of vehicles moving through the bottleneck. At 

locations where capacity expansion is not feasible (i.e., at tunnels and bridges), implementation 

of this form of mainline metering can help. For example, metering for traffic arriving at the 

bridge or tunnel is used at the Bay Bridge in Oakland, CA; the Hampton Roads Bridge-Tunnel 

connecting Hampton, VA, and Norfolk, VA; and the Baltimore Harbor Tunnel in Baltimore, 

MD. 

In situations where mainline and ramp vehicle speeds are both very low, and fall below a defined 

threshold, such as 5 or 10 mi/h, traffic signal control applied to both upstream approaches could 

optimize the operation by eliminating merge friction. Figure 9 illustrates this concept. However, 

there is no known application of the treatment.

Some mainline metering experiments produced approximately 20-percent delay reductions. 

Future research could investigate enhancing the benefits by deploying the strategy at several 

locations along a congested corridor. However, the improvements seen here used a refined 

algorithm. In addition, it is unclear whether drivers will comply with some sort of red signal on 

the mainline, even at low speeds. Finally, researchers noted that the left-most freeway lanes often 

did not show speeds below 10 mi/h, so it was often necessary to stop mainline vehicles traveling 

between 10 and 20 mi/h. 
103 

COORDINATED ADAPTIVE RAMP METERING SYSTEM (E.G., HERO)

Traditional ramp metering measures upstream freeway mainline volume to determine ramp flow, 

but rarely monitors downstream conditions. Downstream problems only show when congestion 

reaches the upstream ramp detector. Coordinated adaptive ramp metering uses feedback logic in 

a closed loop control system. Traffic conditions measured at downstream bottlenecks determine 

critical occupancy and appropriate levels of traffic entering from upstream. The process assumes 

an historic or theoretical value of freeway capacity. Downstream flow conditions provide 

feedback to determine real time ramp flow and optimal occupancy. 

When applied on a holistic, coordinated, system-wide basis, all ramps continuously communicate 

with each other to resolve complex traffic-flow situations. One example of such a system is 

called HERO, but the team identified several similar algorithms and systems during the literature

review effort. Simulation experiments conducted during this project found that HERO produced 

better traffic flow than ALINEA, and ALINEA outperformed SDRMS. Although the HERO 

method produced better traffic flow than using ALINEA or doing nothing, the improvements 

were minimal. However, it can be seen in test six that, with the right set of parameters, a 

“non-zero sum” game can be created; by reducing the mainline friction and regulating flow, 

on-ramp merges were made easier and travel times were shorter overall for the mainline, ramps 

and arterials. In addition, this study did not investigate city-wide CRM, which VicRoads has 

reported to be successful in Melbourne, Australia. 

OPEN-ACCESS MLs ON THE RIGHT-HAND SIDE

Typically in the United States, managed HOV/HOT lanes provide an improved level of service 

to drivers and are at the far left, next to the median. Although locating the ML on the right side 

would reduce its capacity, the overall impact on all lanes could be beneficial. The Alternative 

Freeway Designs at Merge and Diverge Segments project compared right-side ML design to 

left-side designs (i.e., with continuous access or partial access). Reduction by 300 veh/h in the 

overall capacity of a four-lane freeway occurs if the right-side ML capacity is reduced from 

2,000 to 1,700 veh/h and the other three lanes remain at 2,000 veh/h. However, MLs on the 

left-hand side can result in a much larger capacity loss since all lanes can experience the 

300 veh/h capacity loss due to vehicles weaving from the right to the left. Even if only the 

middle two lanes were impacted by this weave friction and turbulence, there may be a capacity 

loss of 300 x 2 = 600 veh/h, or twice that of a right-side ML. 

Simulation experiments conducted during this project found that the performance of GP lane 

vehicles improves in most cases when placing HOV lanes on the right-hand side. However, the 

alternative design degrades HOV lane performance, especially for HOV vehicles that do not exit 

at ramps. The system throughput improvement is significant for merge and weaving areas, 

ranging from 5 to 10 percent on average. The system delay enhancement is significant under all 

scenarios, ranging from 10 to 30 percent on average. 

The alternative design will reduce delay and increase throughput, although the improvements are 

sometimes not significant. Even if HOV performance degrades more with HOV lanes on the 

right-hand side, HOV lane vehicles still experience a higher level of service than GP lane 

vehicles under the same background settings. However, the difference in the level of service is 
104 

smaller than with HOV lanes on the left. This is dependent on the HOV traffic percentage. 

Lower HOV percentages lead to higher differences and better levels of service. The simulation 

results also suggest that it is practical to implement the alternative design on freeway segments, 

especially for areas with low HOV traffic volume. Generally, the alternative design with the 

HOV lane on the right-hand side will help improve the performance of GP lane vehicles in most 

cases. However, the alternative design will degrade HOV lane performance, especially for HOV 

vehicles that do not exit at ramps.
105 

APPENDIX. DATA COLLECTION AND CALIBRATION

Following the down-selection of six candidate strategies and identification of nine candidate 

sites, the team then matched sites with strategies to begin the simulation experiments. For 

various reasons outlined in the next paragraph, some candidate sites were ultimately not used in 

the simulation experiments. This appendix describes a 3-mo data collection and simulation 

model calibration process, which took place prior to the main 6-mo period of simulation 

sensitivity analysis experiments.

To assess the readiness of the available datasets for project experiments, the team considered 

several factors. The first set of information pertains to throughput calibration. If throughput was

already calibrated, when was this done (e.g., 2012), what was the methodology, and where is the 

documentation on this process or results? If throughput was not calibrated, what is the tentative 

plan for doing so? The second set of information pertains to the calibration of other performance 

measures (e.g., speed, travel time, density). Were any other performance measures calibrated? 

If tested, what was the methodology, and where is the documentation on this process or results? 

If other performance measures were not calibrated, what is the tentative plan for doing so? For 

each dataset, the team should try to calibrate at least one performance measure beyond 

throughput. Third, was there a “reasonableness” check on the animation? Did the team attempt to 

provide a few sentences to describe how the animation demonstrates how the simulation 

reasonably replicates real-world operations. If not, the team would develop a simple plan for how 

to use animation to verify the simulation. Last, is there sufficient documentation of the site for 

the project report(s) and journal publication(s)? At a minimum, this should include a Google® 

MapsTM screenshot or image. Preferably, it should further include a description of local 

bottlenecks, speed limits, number of mainline lanes, percent heavy vehicles, and average number 

of ramps per mile. During the 3-mo task 3 effort for data collection and model calibration 

(i.e., October 12, 2017–January 12, 2018), the team worked on assembling the data and 

information categories described above:

• Throughput calibration. 

o Date. 

o Methodology. 

o Documentation of results. 

• Calibration of other performance measures (e.g., speed, travel time, density). 

o Selected measure(s). 

o Date. 

o Methodology. 

o Documentation of results. 

• Reasonableness check on the animation. 

o Date. 

o Methodology. 

o Documentation of results. 
106 

• Documentation of site conditions. 

o Mandatory info. 

 Google® MapsTM. 

o Optional info. 

 Number of mainline lanes. 

 Description of local bottlenecks. 

 Speed limits. 

 Percent heavy vehicles. 

 Average number of ramps per mile. 

The subsequent sections give this site-specific information.

I–66 VA VISSIM

Background Information 

This simulation network was obtained from a previous FHWA research project in which the 

network was used to evaluate the effectiveness of cooperative adaptive cruise control. It is worth 

mentioning that among the many scenarios analyzed in the study, one scenario converted the 

leftmost lane of I–66 to an ML and evaluated the effectiveness of the cooperative adaptive cruise 

control ML concept. This was helpful for analyzing HOV lanes on the left-hand side, and the 

scenario was then edited to test the impacts of HOV lanes on the right-hand side. 

Overview

Below is an overview of the network calibration: 

• I–66 westbound between interchange I–495 (MM 64) and US 29 (MM 51), six 

interchanges on this 13-mi-long section. 

• Data collection period: Wednesday, November 13, from 3:00–7:30 p.m. 

• Data: 

o Speed and volume data collected by six RTMS trailers along major mainline 

segments. 

o On- and off-ramp volume data collected by portable action cameras. 

• Calibration approach: 

o Initial calibration is performed to narrow down parameter set candidates by using the 

LHD approach and key car following and lane changing parameters were calibrated.

o Vissim evaluated 500 scenarios created by the LHD approach with five replications 

for each scenario to choose the best candidate scenario.

o The selected candidate is fine-tuned to obtain the final simulation model. 

• Segment travel time data (INRIX 2018). 
107 

Table 30. Westbound RTMS trailer locations.

Interchange 

ID

Nearest 

Exit Nearest Crossing Mile Marker Latitude Longitude

1 62 Rt 243/Nutley 

Street 61.6 38.879646 −77.250504 

2 60 Rt 123 60.0 38.8705306 −77.3009333 

3 57 Rt 50 58.0 38.861246 −77.352807 

4 55 Rt 286 55.5 38.856415 −77.378025 

5 53 Rt 28/Sully Road 53.0 38.847060 −77.429654 

6 52 Rt 29 51.8 38.8386833 −77.4462111 
108 

Table 31. Westbound traffic management center (TMC) locations (INRIX 2018). 

TMC 

Code

From

(Latitude, Longitude) 

From

(Latitude, 

Longitude) 

From

(Latitude, 

Longitude) 

To

(Latitude, 

Longitude) 

To

(Latitude, 

Longitude) 

To

(Latitude, 

Longitude) 

Mileage

110+04176 I–495/exit 64 38.88308 −77.2295 

VA–243/ 

Nutley

Street/ 

exit 62 

38.87883 −77.2628 1.8 

110P04176 I–495/exit 64 38.87883 −77.2628 

VA–243/ 

Nutley

Street/ 

exit 62 

38.87824 −77.2703 0.4 

110+04177 Vaden Drive/exit 62 38.87824 −77.2703 VA–123/ 

exit 60 38.87741 −77.2748 0.3 

110+04178 VA–243/Nutely Street/exit 62 38.87741 −77.2748 VA–123/ 

exit 60 38.8704 −77.3006 1.4 

110P04178 VA–123/exit 60 38.87041 −77.30064 US 50/ 

exit 57 38.8676 −77.3162 0.9 
109 

Original photo: © 2017 Google® (see acknowledgements).

A. TMC 110+04176. 

Original photo: © 2017 Google® (see acknowledgements).

B. TMC 110P04176. 

Original photo: © 2017 Google® (see acknowledgements).

C. TMC 110P04177. 
110 

Original photo: © 2017 Google® (see acknowledgements).

D. TMC 110+04178. 

Original photo: © 2017 Google® (see acknowledgements).

E. TMC 11P04178. 

Figure 50. Maps. TMCs used in the calibration.

O-D Estimation

Vissim uses O-D matrices to specify travel demand. The I–66 freeway network has 10 zones, as 

shown in figure 51 and table 32. Zones 1 and 10 are the starting and ending points of the 

corridor. Zones 2–9 contain the intermediate interchanges. Note that zones 5 and 7 are only 

applicable to HOV vehicles (i.e., exits for westbound, entrances for eastbound). 

The field-collected data in this study identified how many vehicles traveled between some, but 

not all, O-D pairs. To fill in the gaps, the QueensOD software estimated O-D matrices. For 

example, table 33 lists an estimated O-D matrix for the 3:00–3:15 p.m. time period. Figure 29

compares estimated flows with observed flows of the same period. Results indicate an excellent 

correlation between estimated and field-measured O-D trips.
111 

Table 32. Zone definitions

Zone ID Zone Description

1 I–66 eastbound or I–495 

2 Exit 62 on/off-ramp

3 Exit 60 on/off-ramp to/from north

4 Exit 57 on/off-ramp to/from north

5 Exit to Monument Drive, destination only 

6 Exit 55 on/off-ramp

7 Exit to Stringfellow Road, destination only 

8 Exit 53 on/off-ramp to/from north

9 Exit 52 on/off-ramp to/from north

10 I–66 westbound 

Original photo: © 2017 Google® (see acknowledgements).

Figure 51. Map. Zone locations. 

Table 33. O-D example of 3:00–3:15 p.m. (veh/h).

Zone ID 2 3 4 5 6 7 8 9 10 

1 457 97.2 375.8 149 1.4 228.1 756.1 417.9 3599.7 

2 — 287.2 414.8 — 14.2 — 31.3 — — 

3 — — 861.4 — 72.2 — 0.1 — — 

4 — — — — 242 — 71.7 39.1 41.7 

6 — — — — — — 1164.5 8.9 401.8 

8 — — — — — — — 64.2 1223.8 

9 — — — — — — — — 925.6 

—No data. 
112 

Throughput Calibration

Throughput calibration was performed from 3:30–7:00 p.m. at all six interchanges. The plotted 

and graphed throughputs were deemed reasonably consistent between the simulated and 

field-measured results.

Calibration of Speed Flow Curves 

Speed flow curves were plotted from 3:30–7:00 p.m. at all six interchanges. The plotted and 

graphed throughputs were deemed consistent with traffic engineering principles (and consistent 

with each other) for both simulated and field-measured results.

Calibration of Travel Time 

Travel time calibration was performed from 3:30–7:00 p.m. at all six interchanges. The plotted 

and graphed throughputs were deemed reasonably consistent between the simulated and 

field-measured results. Peak travel time occurred at approximately 5:45 p.m. 

Reasonableness of Animation 

The simulation network is all of the freeway networks with connecting ramps and local roads 

where traffic demands are generated. The simulation calibration and validation have confirmed 

the reasonableness of simulation volumes at key areas (selected locations before and after 

merging areas) and travel time of key segments (especially those the merging segments). This 

section focuses on checking the reasonableness of simulation behavior at key locations of the 

network, including car following behavior at basic segments, merging behavior at on-ramps, and 

merging behavior at lane drops. Figure 52 through figure 54 demonstrate simulation behavior of 

the above-mentioned three key areas. Animation checks at multiple key locations do not reveal 

abnormal behavior, and thus confirm the reasonableness of the simulation. 

Source: FHWA.

Figure 52. Screenshot. Merging area simulation example one (bird’s eye view).
113 

Source: FHWA.

Figure 53. Screenshot. Merging area simulation example two (zoomed-in view).

Source: FHWA.

Figure 54. Screenshot. Lane drop simulation (zoomed-in view).

I–270 MD VISSIM 

Background Information 

The network contains the following characteristics:

• Hourly volume: 12,300 veh/h. 

• 10 percent trucks in the traffic stream.

• Speed limit: 55 mi/h.

• Number of mainline lanes: four. 

• Shoulder width: approximately 12 ft on both sides. 

• Number of ramps: 1.1 per mile. 
114 

Overview

The section of roadway selected for analysis is located in Montgomery County, MD, and spans 

approximately 5.5 mi in the freeway’s southbound direction between Shady Grove Road and the 

I–270 west spur to I–495. The facility spans two collector-distributor (C-D) lanes separated out 

from the four GP lanes by a Jersey barrier and has shoulder widths varying anywhere from 2 to

10 ft. The GP lane closest to the median operates as an HOV lane during the morning rush, from 

6:30–9:30 am Monday–Friday, and the whole cross section (C-D and GP lanes) carries over 

125,000 vehicles per day in the I–270 southbound direction alone. The C-D lane ends at the 

southern end of this section of the I–270 corridor, just south of Montrose Parkway. In this 5.5-mi 

section, there are three slip ramps from the C-D lanes to the GP lanes, and two slip ramps from 

the GP lanes to the C-D lanes. Within the C-D lanes, there are five merge locations, two diverge 

locations, and one weaving condition, excluding those generated by the slip ramps. The posted 

speed limit for both the C-D and GP lanes is 55 mi/h. 

© 2017 Google®. 

Figure 55. Map. I–270 southbound starting at Shady Grove (a.m. peak). 
115 

Noteworthy for the I–270 corridor in this section of roadway is the lack of ITS. There are no 

overhead gantries with lane assignment, VSL signs, and only one dynamic message sign located 

at the southern end of the corridor. Bridge structures along the entire stretch of I–270 are a 

significant constraint to potential widening, as bridge piers interfere with the potential of shifting 

lanes or widening without significant structural costs. Additionally, this section of the I–270 

corridor experiences significant delays due to capacity constraints just south of this corridor 

along the I–270 spur and further at the American Legion Bridge along I–495 going into Virginia. 

The I–270 corridor ranks in the top locations around the State for recurring and non-recurring 

congestion, with a travel time index ranging anywhere between 1.87 and 2.49 between Shady 

Grove Road and Montrose Parkway during the morning rush. Subsequent data and information 

were obtained in a memo from the Maryland Department of Transportation State Highway 

Administration (Whitman, Requardt & Associates 2015). Because the calibration outcomes were 

relatively recent and favorable, the team did not perform additional calibration of this network. 

Throughput Calibration

The Maryland Department of Transportation State Highway Administration’s Data Services 

Engineering Division developed 2015 peak hour traffic volumes for the study area. They found 

the a.m. and p.m. peak hours were 7:00–8:00 a.m. and 5:00–6:00 p.m., respectively, as shown in 

table 34 and table 35. The traffic demand is balanced throughout the entire network for both a.m. 

and p.m. peaks.

Table 34. 2015 a.m. peak hour traffic volumes for I–270. 

I–270 

Southbound

GP

Count 

Volume

(veh/h) 

GP

Vissim

Volume

(veh/h) 

GP

Difference

(Percent)

HOV 

Lane 

Count 

Volume

(veh/h) 

HOV 

Lane 

Vissim

Volume

(veh/h) 

HOV 

Lane 

Difference

(Percent) 

C-D 

Lanes 

Count 

Volume

(veh/h) 

C-D 

Lanes 

Vissim

Volume

(veh/h) 

C-D 

Lanes 

Difference

(Percent) 

North of Shady 

Grove 4,506 4,111 −9 1,187 1,265 7 2,409 2,942 −14 

South of MD 124 6,864 6,930 1 — — — — — — 

South of 

Middlebrook 

Road 

6,917 6,737 −3 — — — — — — 

North of MD 118 3,944 4,665 18 — — — — — — 

North of MD 121 3,408 3,800 12 — — — — — — 

—No data. 
116 

Table 35. 2015 p.m. peak hour traffic volume for I–270. 

I–270 

Southbound

GP 

Count 

Volume

(veh/h)

GP 

Vissim 

Volume

(veh/h)

GP 

Difference

(Percent)

HOV 

Lane 

Count 

Volume

(veh/h)

HOV 

Lane 

Vissim 

Volume

(veh/h)

HOV 

Lane 

Difference

(Percent)

C-D 

Lanes 

Count 

Volume

(veh/h)

C-D 

Lanes 

Vissim 

Volume

(veh/h)

C-D 

Lanes 

Difference

(Percent)

North of Shady 

Grove 4,392 4,789 9 1,075 1,026 −5 4,029 4,148 3 

South of MD 124 4,078 4,713 16 1,195 1,280 7 2,577 2,448 −5 

South of 

Middlebrook 

Road 

5,330 6,115 15 1,281 1,190 −7 — — — 

North of MD 118 4,343 4,409 2 — — — — — — 

North of MD 121 3,603 3,787 5 — — — — — — 

—No data.

Calibration of Additional Measures 

Signal timing data are provided by the Maryland Department of Transportation State Highway 

Administration’s Data Services Engineering Division for signalized intersections within the 

corridor to ensure that the Vissim models include accurate existing signal timings. The Vissim 

model ring barrier controller files were verified and/or modified to match the signal data. Travel 

time and RITIS data are provided by the Maryland Department of Transportation State Highway 

Administration’s Data Services Engineering Division for segments along the I–270 GP, HOV, 

and C-D lanes. The data are shown in table 36 through table 39. 
117 

Table 36. 2015 a.m. travel time calibration results for I–270: I–270 northbound from the 

I–495 interchange. 

I–270 Northbound Segment 

Length (mi) Field(s) Vissim(s) Difference(s)(Percent)

To MD 187 1.8 109.7 109.0 −0.7 

To I–270 split 0.6 35.8 37.5 1.8 

To Montrose Road 1.8 97.9 100.1 2.2 

To MD 189 1.0 55.3 57.6 2.3 

To MD 28 1.0 52.6 55.1 2.5 

To Shady Grove Road 1.9 104.7 108.4 3.7 

To I–370 0.9 51.1 53.0 1.9 

To MD 117 1.5 81.6 85.5 3.9 

To MD 124 0.6 33.4 34.5 1.1 

To Middlebrook Road 2.5 137.9 140.9 3.0 

To MD 118 1.1 62.2 64.8 2.6 

To MD 27 0.9 50.9 51.8 1.0 

To MD 121 2.4 132.4 135.3 2.8 

To MD 109 4.1 216.1 234.5 18.3 

To MD 80 3.7 198.7 213.8 15.2 

To MD 85 5.3 283.2 309.0 25.7 

To I–70 1.4 80.0 79.9 −0.1 

I–270 total (min) 32.5 29.7 31.2 1.5 

I–270 total percent difference — — — 4.9 

—No data. 
118 

Table 37. 2015 a.m. travel time calibration results for I–270: I–270 southbound from I–70. 

I–270 Southbound Segment 

Length (mi) Field(s) Vissim(s) Difference(s)(Percent)

To MD 85 1.7 97.4 97.0 −0.4 

To MD 80 5.4 484.0 414.5 −69.5 

To MD 109 3.7 397.7 390.6 −7.1 

To MD 121 3.6 281.4 273.2 −8.2 

To MD 27 2.5 356.5 296.2 −60.3 

To MD 118 1.1 172.0 223.8 51.8 

To Middlebrook Road 1.1 120.5 132.6 12.1 

To MD 124 2.2 495.5 535.2 39.7 

To MD 117 0.9 67.0 77.0 10.0 

To I–370 1.0 240.0 160.7 −79.3 

To Shady Grove Road 1.5 277.5 133.7 −143.8 

To MD 28 1.9 541.5 492.8 −48.7 

To MD 189 1.0 129.5 260.0 130.5 

To Montrose Road 1.0 185.0 394.5 209.5 

To I–270 split 1.9 142.0 217.0 75.0 

To MD 187 0.4 29.3 30.0 0.7 

To I–495 interchange 1.9 122.4 131.8 9.4 

I–270 total (min) 32.8 69.0 71.0 2.0 

I–270 total percent difference — — — 2.9 

—No data. 

Table 38. 2015 a.m. travel time calibration results for I–270: I–270 spur northbound from 

Cabin John Parkway. 

I–270 Spur Northbound

Segment 

Length 

(mi) 

Field(s) Vissim(s) Difference(s)

(Percent)

To MD 190 0.5 25.6 32.2 6.6 

To I–495 1.1 64.1 66.7 2.6 

To Democracy Boulevard 1.4 81.6 91.2 9.6 

To I–270 split 0.9 49.9 51.0 1.2 

To I–70 30.0 1,638.1 1,724.3 86.2 

I–270 spur total (min) 33.9 31.0 32.8 1.8 

I–270 spur total percent difference — — — 5.7 

—No data. 
119 

Table 39. 2015 a.m. travel time calibration results for I–270: I–270 spur southbound from 

I–70. 

I–270 Spur Southbound

Segment 

Length 

(mi) 

Field(s) Vissim(s) Difference(s)

(Percent)

To I–270 split 30.3 3,987.5 4,098.7 111.2 

To Democracy Boulevard 0.7 86.4 88.4 2.0 

To I–495 1.3 132.1 183.1 51.0 

To MD 190 1.3 77.4 92.2 14.9 

To Cabin John Parkway 0.6 33.9 35.0 1.1 

I–270 spur total (min) 34.2 72.0 75.0 3.0 

I–270 spur total percent difference — — — 4.2 

—No data. 

Reasonableness of Animation 

To check the reasonableness of the simulation animation and consistency with the field 

measurements, the animation results are compared with the field-measured speed data 

downloaded from the RITIS database (RITIS 2018). The time period between October 1, 2017–

November 15, 2017, was selected for the speed data because traffic is not much affected by 

occasional trips and bad weather conditions in this period. Moreover, only Tuesdays, 

Wednesdays, and Thursdays are selected for the data collection since traffic volumes are less 

likely to be affected by leisurely trips on these days. Figure 56 and figure 57 show the average 

speed heat map from 6:00–10:00 a.m. and from 4:00–8:00 p.m. for the selected days, 

respectively. In these heat maps, the horizontal and vertical axes represent the location and time, 

respectively. As shown in the figures, three locations are already linked to the heat maps. With 

this, the team can measure the scale of the location axes and thus link any other locations along 

the simulated corridor to the heat maps. The team focuses on the peak hours that are bordered by 

the green block in the figures (i.e., from 7:00–8:00 a.m. in figure 56 and from 5:00–6:00 p.m. in 

figure 57). 

The team split the peak hour period into six 10-min time intervals and then compared the 

simulation animation results with the field-measured speed heat maps. In this report, the team 

selected four locations for each a.m. and p.m. peak hours as benchmarks. Figure 58 and figure 59

zoom in to the freeway geometry associated with the base dataset to the peak hour periods and 

tag the four selected benchmark locations. For comparison purposes, the team classified vehicle 

colors in the simulation based on their speeds. 

Figure 60 through figure 66 are examples of the comparison results. The comparison results 

indicate that the simulation animations match the field-measured speed heat map for most of the 

space-time points. The team found a few inconsistencies between the animation and the 

field-measured speeds (e.g., figure 67). However, most of these inconsistencies are related to 

microscopic traffic oscillations and do not necessarily indicate serious inconsistencies with the 

macroscopic field-measured speeds. In addition to these comparisons, the team checked the 

simulation animation carefully to identify the existence of any stuck vehicles. There were no

stuck vehicles on the I–270 freeway; however, the team found one minor problem on an 
120 

on-ramp, as shown in figure 67. Since this issue was located on an on-ramp and not on the 

mainline, it was not expected to significantly impact the simulation validity and results.

Original map: © 2017 Google® (see acknowledgements).

Figure 56. Illustration. Field-measured speed heat map from 7:00–8:00 a.m. 

Original map: © 2017 Google® (see acknowledgements).

Figure 57. Illustration. Field-measured speed heat map from 5:00–6:00 p.m. 
121 

Original map: © 2017 Google® (see acknowledgements).

Figure 58. Illustration. Field-measured speed heat map for p.m. peak hour. 

Original map: © 2017 Google® (see acknowledgements).

Figure 59. Illustration. Field-measured speed heat map for a.m. peak hour. 
122 

Original map: © 2017 Google® (see acknowledgements).

Figure 60. Diagram. Animation check at 7:10 a.m. 

Original map: © 2017 Google® (see acknowledgements).

Figure 61. Diagram. Animation check at 7:20 a.m. 
123 

Original map: © 2017 Google® (see acknowledgements).

Figure 62. Diagram. Animation check at 7:30 a.m. 

Original map: © 2017 Google® (see acknowledgements).

Figure 63. Diagram. Animation check at 7:40 a.m. 
124 

Original map: © 2017 Google® (see acknowledgements).

Figure 64. Diagram. Animation check at 5:10 p.m. 

Original map: © 2017 Google® (see acknowledgements).

Figure 65. Diagram. Animation check at 5:20 p.m. 
125 

Original map: © 2017 Google® (see acknowledgements).

Figure 66. Diagram. Animation check at 5:40 p.m. 

Source: FHWA.

Figure 67. Illustration. A stuck vehicle on an on-ramp.
126 

I–95 PA VISSIM

Background Information

There are presently a number of transportation improvement projects in progress along I–95 in 

Pennsylvania. While there are five active projects that will reconstruct, widen, and improve I–95 

in Pennsylvania, these projects do not include sector C, which covers the study corridor. 

PennDOT’s website gives detailed information regarding each of these projects 

(Pennsylvania Department of Transportation 2018). 

Overview

This section provides an overview of I–95 in Pennsylvania, including traffic information, and 

describes ongoing projects and plans for the I–95 corridor. The I–95 corridor is a vital 

north-south transportation corridor in Pennsylvania serving the airport, the stadiums, and 

Philadelphia, PA. The corridor carries an average of 102,000 vehicles, including 13,000 trucks 

every day (Pennsylvania Department of Transportation 2018). The corridor is divided into four 

sectors by PennDOT, including sector A, B, C, and D. The study corridor selected as part of this 

simulation effort covers sector C, from the Delaware State line to the Schuylkill River 

(figure 68). Figure 69 shows ADT volumes on the I–95 corridor on a typical (non-event) day in 

Pennsylvania. While the ADT ranges from 60,000 to 160,000 vehicles, sector C, the focus of this 

study, has approximately 120,000 vehicles. Figure 70 indicates average weekday speed profiles 

from 5:00 a.m.–8 p.m. on a typical (non-event) day along the corridor and highlights some of the 

existing bottlenecks. For this study corridor, the US 322 interchange and north appears to be the 

main bottleneck, especially during the p.m. peak where average speeds can drop below 30 mi/h.
127 

© 2019 Kittelson & Associates, Inc.

Figure 68. Map. I–95 corridor in Pennsylvania. 
128 

© 2019 Kittleson & Associates, Inc.

A. Busiest interchanges on I–95 in Pennsylvania during a typical, non-event day.

© 2019 Kittleson & Associates, Inc.

B. ADT on the I–95 corridor in Pennsylvania. 

Figure 69. Illustrations. How traffic flows on the I–95 corridor in Pennsylvania. 
129 

© 2019 Kittleson & Associates, Inc.

A. I–95 layout of counties. 

© 2019 Kittleson & Associates, Inc.

B. I–95 southbound (right-to-left) congestion profile, 5:00 a.m.–8 p.m. (top-to-bottom). 

© 2019 Kittleson & Associates, Inc.

C. I–95 northbound (left-to-right) congestion profile, 5:00 a.m.–8 p.m. (top-to-bottom). 

Figure 70. Illustrations. I–95 weekday congested locations. 
130 

Throughput Calibration

Since the volumes are from 2011, an update is needed for the volumes based on the growth rate 

along I–95 within the study area. Table 40 and table 41 show the 2009 and 2015 ADT results on 

the corridor (the latest ADT results are from 2015). Based on the results shown below, applying 

a global growth rate to bring the 2011 volumes to 2017 is needed to maintain the O-D routing 

structure in the Vissim model.

Table 40. I–95 PA throughput calibration plan—Philadelphia County, PA. 

Location 2009 

ADT

2015 ADT 6-Year Growth Factor

I–95 just northeast of Delaware State line 124,000 134,000 1.081 

I–95 by Philadelphia Airport 71,000 72,000 1.014 

I–95 west of Schuylkill River 96,000 102,000 1.063 

I–95 east of Schuyklill River (right after the 

study area) 77,000 107,000 1.390 

Table 41. I–95 PA throughput calibration plan—Delaware County, PA. 

Location 2009 

ADT

2015 

ADT

6-Year Growth 

Factor 

I–95 west of Chichester Avenue 95,000 97,000 1.021 

I–95 east of Chichester Avenue 124,000 100,000 0.806 

I–95 west of Conchester Highway (US 322) 119,000 135,000 1.134 

I–95 west of Edgmont Avenue (PA Rt 352) 125,000 127,000 1.016 

I–95 east of Darby Creek 134,000 121,000 0.903 

I–95 east of PA Rt 420 131,000 127,000 0.969 

I–95 west of Philadelphia Airport 131,000 134,000 1.023 

In order to obtain throughput in the simulation model, permanent oversaturation is required at the 

bottleneck locations for the analysis period. The visual inspection of the base model indicated 

that these bottleneck locations already experience very long queues and capacity breakdowns 

under the base volume scenario. To ensure permanent oversaturated conditions for the analysis 

period, base traffic volumes were increased artificially by 5 percent. It should be noted that 

5 percent was arbitrarily selected by the team. 

In order to estimate throughput, data collection points were placed just downstream of the 

bottleneck locations (figure 71 shows screenshots from the model). Ten simulation runs were 

performed to collect results. The results were obtained from the peak hour following a 1-h 

warmup period. Since field data are not available to compare simulation throughput values 

against field results, theoretical recommended capacity along with previous studies found in the 

literature were used for comparison. Table 42 shows the locations where throughput data were

collected and provides throughput results.
131 

Table 42. I–95 PA throughput calibration summary. 

Bottleneck Location Vissim Screenshot Number

of Lanes 

Vehicle 

Throughput 

(veh/h) 

HV 

Proportion 

(Percent) 

PCPHPL

Downstream of I–476 

southbound on-ramp 

(right merge)

Figure 71-A. Downstream of I–476 

southbound on-ramp (right merge). Three 5,084 6.3 1,817 

Downstream of I–476 

southbound off-ramp 

(right exit)

Figure 71-B. Downstream of I–476 

southbound off-ramp (right exit). Two 3,368 5.9 1,799 

Downstream of 

US 322 northbound 

on-ramp (left merge)

Figure 71-C. Downstream of US 322 

northbound on-ramp (left merge). Three 5,057 6.8 1,817 

HV = heavy vehicles; PCPHPL = passenger cars per hour per lane.

Source: FHWA

A. Downstream of I–476 southbound 

on-ramp (right merge).

Source: FHWA

B. Downstream of I–476 southbound 

off-ramp (right exit).

Source: FHWA

C. Downstream of US 322 northbound 

on-ramp (left merge).

Figure 71. Screenshots. Vissim screenshots of I–95 PA. 
132 

Results indicated throughput values that were slightly above 1,800 passenger cars per hour per 

lane and were consistent for each bottleneck location. While 1,800 passenger cars per hour per 

lane is lower than what is recommended in the HCM as theoretical capacity, several field studies 

found in the literature showed that HCM values tend to be higher, in particular at locations where 

there is heavy weaving with short merge/diverge sections. 

Calibration of Additional Measures 

Field speeds are based on March 2017 and October 2017 HERE archived historical probe data 

(Here 2019) from every Tuesday, Wednesday, and Thursday. Vissim results are based on the 

peak hour following a 1-h warmup time (so the team ran the models for 2 h). Vehicle inputs 

(volumes) were kept the same for the warmup time and the analysis time. A total of 10 

simulation runs were performed in Vissim. Driving behavior was adjusted at certain locations to 

more accurately model merge/diverge behavior, in particular at the bottleneck locations. The 

adjusted calibration parameters at key segments are described below: 

• At Conchester Highway (US 322) and I–95, due to the short left-hand on-ramp that 

includes heavy merging, the default parameters did not properly reflect field conditions in 

which ramp vehicles were “zippering” into the left lane, causing major congestion to 

mainline I–95. To more accurately model this behavior, the safety distance reduction 

factor within the lane change parameters in the Wiedemann 99 car following model 

(PTV Group 2019) was reduced from 0.60 to 0.30. In addition, the overall capacity 

within the segment was reduced (due to heavy weaving and an unusual left entrance) by 

increasing headway time and from 0.9 to 1.45 s and the following variation from 13.12 to 

25.00. 

• The southbound I–476 on-ramp is a right-hand add-lane ramp that is upstream of an 

off-ramp. This area experiences significant weaving due to the add lane being at capacity. 

As such, vehicles wishing to exit need to merge into shorter gaps, while vehicles in the 

add lane attempt to move to the left lanes and a more free-flow condition. This causes 

significant friction to mainline I–95. In order to replicate this condition in the model, the 

safety distance reduction factor was reduced from 0.60 to 0.10. Furthermore, to reduce 

overall capacity within the segment, the headway time was increased from 0.9 to 1.50 s 

and the following variation was increased from 13.12 to 25.00. 

Overall, simulation speeds are generally consistent with the field data. Major bottlenecks 

observed in the field are reflected in the simulation, as can be seen in the speed comparison in 

table 43 and table 44. 
133 

Table 43. 2017 calibration of speeds and travel times on I–95 PA—northbound direction. 

Segment

(Northbound)

Field 

TT 

(min)

Vissim

TT 

(min)

Field 

Speed 

(mi/h) 

Vissim

Speed 

(mi/h) 

Speed 

Difference

(Percent) 

Delaware State line (TMC 103+04114) 

to Rt 452 off-ramp; TMC 103P04115 3.1 3.0 30.2 30.8 −1.7 

Rt 452 off-ramp to exit 3 

(Highland Avenue); TMC 103P04116 2.4 3.2 27.4 21.3 22.4 

Exit 3 (Highland Avenue) to Barry 

Bridge on-ramp; TMC 103+04118 2.3 2.6 40.7 37.2 8.6 

Barry Bridge on-ramp to 

Edgmont Avenue off-ramp; 

TMC 103P04119 

1.1 1.0 53.2 52.7 0.8 

Edgmont Avenue off-ramp to I–476 

off-ramp; TMC 103P04121 1.3 1.4 52.9 51.9 1.8 

I–476 off-ramp to Stewart Avenue 

off-ramp; TMC 103P04122 1.4 1.4 54.3 55.3 −1.9 

Stewart Avenue off-ramp to 

Wanamaker Ave off-ramp; 

TMC 103P04123 

1.1 1.1 60.6 55.7 8.1 

Wanamaker Avenue to PA 291 

off-ramp; TMC 10304124 1.5 1.7 62.5 55.7 11.0 

PA 291 off-ramp to I–95 at 

Bartram Avenue; TMC 103P04125 1.0 1.1 63.9 56.1 12.2 

Total northbound 15.1 16.4 44.6 41.0 8.0 

TT = travel time.

Table 44. 2017 calibration of speeds and travel times on I–95 PA—southbound direction.

Segment

(Southbound)

Field 

TT 

(min)

Vissim

TT 

(min)

Field 

Speed 

(mi/h) 

Vissim 

Speed 

(mi/h) 

Speed 

Difference

(Percent) 

I–476 on-ramp to Edgmont Avenue 

on-ramp (TMC 103N04119 end) 2.0 1.3 33.4 52.0 −55.6 

Edgmont Avenue on-ramp to US 322 

on-ramp (103-04116 start) 2.5 1.8 39.0 53.8 −38.1 

US 322 on-ramp to Highland Avenue 

on-ramp (TMC 103-04115 start) 1.3 1.1 43.4 51.1 −17.7 

Highland Ave on-ramp to 

Chichester Avenue on-ramp 

(TMC 103-04113 start)

1.7 1.70 58.7 58.0 1.3 

TMC 103-4125 to I–95 at 

Bartram Avenue (TMC 103N04125) 1.5 1.3 43.2 50.7 −17.2 

I–95 at Bartram Avenue to I–95 

(TMC 103N04124 End) 1.5 1.3 41.0 46.6 −13.7 
134 

Segment

(Southbound)

Field 

TT 

(min)

Vissim

TT 

(min)

Field 

Speed 

(mi/h) 

Vissim 

Speed 

(mi/h) 

Speed 

Difference

(Percent) 

From TMC 103N04124 end to 

Wanamaker Avenue on-ramp 

(103-04122 start)

2.2 2.0 40.0 44.7 −11.8 

Wanamaker Avenue on-ramp to 

Stewart Avenue on-ramp 

(TMC 103-04121 start)

2.5 3.2 29.7 22.8 23.4 

Stewart Avenue on-ramp to I–476 

on-ramp (TMC103-04120 start) 3.0 2.8 25.0 26.8 23.4 

Total southbound 18.1 16.4 37.5 41.4 −10.3 

TT = travel time.

The initial I–95 model was from 2011 and was calibrated to GEH volume (which the team did 

not follow for this project) and corridor travel times. Table 45 through table 48 give the travel 

time calibration results from the 2011 study. 

Table 45. Morning travel time calibration measurements—northbound. 

Measurement

Delaware 

State Line 

to US 322 

Left Entry 

(Exit 3) 

US 322 Left 

Entry 

(Exit 3) to 

Barry 

Bridge 

On-Ramp 

(Exit 4)

Barry 

Bridge 

On-Ramp 

(Exit 4) to 

I–476 

Off-Ramp 

(Exit 7)

I–476 

Off-Ramp 

(Exit 7) to 

Bartram

Avenue

On-Ramp 

(Exit 14) 

Total—

Delaware 

State Line to 

Bartram

Avenue

On-Ramp 

(Exit 14) 

Field (min) 3.24 2.45 1.81 7.54 15.03 

Model (min) 3.10 2.37 2.20 8.78 16.45 

Difference 

(percent) 4.3 3.2 22.0 16.5 9.5 

Table 46. Morning travel time calibration measurements—southbound.

Measurement

Enterprise Avenue

Off-Ramp (Exit 15) 

to Past Airport 

Frontage Exit 

(Exit 13) 

Airport 

Frontage 

Exit 

(Exit 13) to 

I–476 

On-Ramp 

(Exit 7) 

I–476 

On-Ramp 

(Exit 7) to 

Delaware 

State Line

Total—Enterprise 

Avenue Off-Ramp 

(Exit 15) to 

Delaware State 

Line

Field (min) 1.75 6.28 6.01 14.04 

Model (min) 2.05 6.96 6.58 15.58 

Difference 

(percent) 17.3 10.7 9.4 11.0 
135 

Table 47. Evening travel time calibration measurements—northbound. 

Measurement

Delaware 

State Line 

to US 322 

Left Entry 

(Exit 3)

US 322 Left 

Entry 

(Exit 3) to 

Barry 

Bridge 

On-Ramp 

(Exit 4) 

Barry 

Bridge 

On-Ramp 

(Exit 4) to 

I–476 

Off-Ramp 

(Exit 7)

I–476 

Off-Ramp 

(Exit 7) to 

Bartram 

Avenue

On-Ramp 

(Exit 14) 

Total—

Delaware 

State Line to 

Bartram 

Avenue

On-Ramp 

(Exit 14) 

Field (min) 4.85 3.07 2.45 7.66 18.04 

Model (min) 4.23 2.73 1.93 8.49 17.38 

Difference 

(percent) 12.8 11.3 21.2 10.8 3.7 

Table 48. Evening travel time calibration measurements—southbound.

Measurement

Enterprise Avenue

Off-Ramp (Exit 15) 

to Past Airport 

Frontage Exit 

(Exit 13) 

Airport 

Frontage 

Exit 

(Exit 13) to 

I–476 

On-Ramp 

(Exit 7) 

I–476 

On-Ramp 

(Exit 7) to 

Delaware 

State Line

Total—Enterprise 

Avenue Off-Ramp 

(Exit 15) to 

Delaware State 

Line

Field (min) 2.10 10.79 6.23 19.12 

Model (min) 2.14 12.11 6.61 20.86 

Difference 

(percent) 1.4 12.3 6.2 9.1 

Reasonableness of Animation 

Check 1: during the p.m. peak hour, both probe data and Google® MapsTM typical traffic data 

indicated heavy southbound congestion originating from the I–476 interchange. This was 

consistent in the Vissim model where queues extending beyond the Stewart Avenue interchange 

as shown in figure 72. Vehicles in Vissim were color-coded by their speed to easily identify 

congested segments, and figure 73 displays defined speed intervals for each color. 

Check 2: in the northbound direction, the main bottleneck along the study corridor during the 

p.m. peak is the US 322 interchange where queues originating from this location extend beyond 

Chichester Avenue. Similar queuing and bottleneck conditions also occurred in the simulation 

model, as demonstrated in figure 74 and figure 75. 
136 

Original map: © 2017 Google® (see acknowledgements).

Figure 72. Map. Typical Tuesday traffic at 5:00 p.m. for I–95 southbound. 

© 2019 Kittleson & Associates, Inc.

Figure 73. Screenshot. Vissim screenshot during the p.m. peak hour for I–95 southbound. 
137 

Original map: © 2017 Google® (see acknowledgements).

Figure 74. Map. Typical Tuesday traffic at 5:00 p.m. for I–95 northbound. 

© 2019 Kittleson & Associates, Inc.

Figure 75. Screenshot. Vissim screenshot during the p.m. peak hour for I–95 northbound. 
138 

I–35 KS VISSIM

Background Information 

This facility is approximately 10 mi long and it consists of several interchanges. The bottlenecks 

at the southbound direction are located at Southwest Boulevard (merge), Shawnee Mission 

Parkway (weave), and 67th Street (merge). The last two bottlenecks interact with each other, and 

it appears that queue from 67th Street propagates upstream to Shawnee Mission Parkway and 

even further upstream (spillback reaches the Metcalf Avenue interchange). At the southbound 

direction, KDOT is operating ramp metering at 7th Street, Southwest Boulevard, 18th Street, and 

67th Street. 

In the northbound direction, the facility is experiencing recurrent congestion at the 87th Street

interchange (merge). In that direction, ramp meters operate at the 7th Street on-ramp and at 

Johnson Drive. Figure 26 is a map of the facility showing the congestion areas and the locations 

of the ramp meters. Only two of the six ramp meters were installed at bottleneck locations. Ramp 

metering was installed in the summer of 2017, and the algorithm that is currently used is the 

CARMA algorithm, which is a system-wide algorithm based on speeds. KDOT has decided to 

switch to another system-wide algorithm that uses occupancies as thresholds for metering rate 

determination. However, given that the metered ramps are not consecutive, it is impossible to 

leverage the full capabilities of a system-wide ramp metering algorithm.

Overview

The team obtained permission from KDOT to use their I–35 Vissim model. KDOT provided the 

University of Kansas with the Vissim simulation network of the I–35 corridor, and the 

University of Kansas team performed the calibration task. The facility is equipped with radar 

sensors. Field data at those sensor locations along the freeway and the on-ramps are available to 

the researchers through Kansas City Scout (Kansas DOT and Missouri DOT 2018). 

Modeling the Facility

The geometric modeling of the facility in Vissim consists of creating links and connectors. All 

links in the Vissim model are modeled to have a 12-ft width and a number of lanes that 

correspond to the field. A background map was used to draw the links of I–35 southbound from 

Cambridge Drive to 75th Street, including on-ramps and off-ramps. Traffic signals on the 

adjacent arterial streets were removed from the simulation model because the signal timing and 

the volume distribution on the arterial was not known. Figure 76 is a schematic of the Vissim 

model. 
139 

© 2018 Alexandra Kondyli.

Figure 76. Screenshot. Vissim model of the I–35 KS corridor.

Data Inputs

Traffic volume and speed data at the mainline, on-ramp, and off-ramp were obtained from the 

Kansas City Scout Portal. The team selected a day without incidents or adverse weather

throughout the corridor for the model calibration. The selected calibration day was April 22, 

2016. The simulation period is the afternoon peak period from 3:15–6:15 p.m., and 25 min were 

added in the beginning of the simulation period for initialization. The Vissim model provided by 

KDOT was used to obtain the percentage of passenger cars, heavy goods vehicles, and large 

goods vehicles. For the desired speed, the mainline detectors’ speed readings obtained from 
140 

Kansas City Scout Portal were reviewed and a mean speed of 64 mi/h was identified. The desired 

speed distribution follows an S-shaped curve.

Model Calibration 

The speed distribution from the simulation has to match roughly the speed distribution of 

real-life data with an admissible error of 10 percent. The Vissim model is calibrated by changing 

the driver behavior parameters in the links and connectors. 

The calibration was conducted by changing the values of multiple car following and lane 

changing parameters throughout the software. The calibration was done manually, by changing 

all relevant parameters on a one-at-a-time basis. The calibration day needs to start in 

uncongested conditions and end in uncongested conditions to verify that the calibrated model is 

functioning properly and that the modelling includes vehicle route decisions. To compare the 

simulation model to the calibration day, detectors were established in the Vissim model in the 

same locations that they exist in real life. After that, an initial simulation running using one 

random seed was conducted to compare the calibration day speed profile to the simulation model 

speed profile. Through the simulation animation, unrealistic driver behaviors were identified and 

were resolved with the help of the calibration. To achieve a speed profile similar to the 

calibration day, different sets of driver behavior parameters in links were developed and tested. 

This process was done on a trial and error basis depending on how well the simulation model 

represents the real world on each link. The process of running one seed and adjusting the 

parameters was executed until a similar speed profile was achieved. Then, the simulation model 

ran multiple times with different seeds. The average speed profile of those runs was compared to

the calibration day speed profile. 

The team adapted the connectors’ lane change distance from Leyn and Vortisch (2015) for the 

simulation trials. According to Leyn and Vortisch (2015), lane distance for an off-ramp diverge 

should be at least 2,100 ft. The suggested value was used in the initial runs, and then it was 

adjusted for the next test runs so that the vehicle maneuverability looked realistic and the speed 

profile of the simulation roughly matched the calibration day speed profile.

The car following and lane changing parameters were adjusted to produce similar speed profiles 

to those of the calibration day. Table 49 and table 50 show the selected calibration values of the 

parameters. The Vissim model is calibrated by matching the speed profile obtained from Kansas 

City Scout to the calibrated model profile shown in figure 77 and figure 78. Based on the data, 

there are two active bottlenecks along the corridor. The first one is located at the interchange 

with 67th Street, and the second one is located at Southwest Boulevard. The simulation results 

also suggest that the calibration was able to replicate the active bottlenecks; however, the extent 

of congestion at the most downstream bottleneck is not replicated accurately. At the locations 

where the calibration was successful, the speed difference between field and simulated data was

within 10 percent. 

The team constructed speed-flow graphs of the calibrated and field-measured network to 

evaluate the fit of the calibration process. Figure 79 shows the speed-flow graph at selected 

locations. 
141 

Table 49. Calibrated Wiedemann 99 model parameters.

Parameter Default Freeway Diverge Merge Weave 

CC0 4.92 5.60 7.50 7.80 9.00 

CC1 0.90 1.05 1.15 1.30 1.45 

CC2 13.12 13.10 12.00 12.00 12.60 

CC3 −8.00 −8.00 −8.00 −8.00 −8.00 

CC4 −0.35 −0.30 −0.35 −0.35 −0.35 

CC5 0.35 0.30 0.35 0.35 0.35 

CC6 11.44 11.44 11.44 11.44 11.44 

CC7 0.82 0.82 0.82 0.82 0.82 

CC8 11.48 10.48 10.48 12.48 12.48 

CC9 4.92 4.92 4.92 4.92 4.92 

CC0 = standstill distance; CC1 = spacing time; CC2 = following variation, maximum drift; CC3 = threshold for 

entering following; CC4 = negative following threshold; CC5 = positive following threshold; CC6 = speed 

dependency of oscillation; CC7 = oscillation acceleration; CC8 = standstill acceleration; CC9 = acceleration at 

50 mph. 
142 

Table 50. Calibrated lane changing parameters.

General Behavior

Default 

Free Lane 

Selection

Freeway 

Free Lane 

Selection

Diverge 

Free Lane 

Selection

Merge 

Free Lane 

Selection

Weave 

Free Lane 

Selection

Maximum deceleration – 

subject vehicle (ft/s2) 

−13.12 −13.12 −13.12 −13.12 −13.12 

Maximum deceleration – 

trailing vehicle (ft/s2) 

−9.84 −9.84 −9.84 −9.84 −12.00 

−1 ft/s2 per distance – own 

(ft)

100 300 200 300 400 

−1 ft/s2 per distance – 

trailing vehicle (ft)

100 200 200 200 400 

Accepted deceleration – 

own (ft/s2) 

−3.28 −3.00 −3.00 −4.50 −4.50 

Accepted deceleration – 

trailing vehicle (ft/s2

) 

−3.28 −2.25 −3.00 −3.00 −4.50 

Waiting before diffusion(s) 60 60 60 60 60 

Minimum headway 

(front/rear)

1.64 1.64 1.64 1.64 1.64 

Safety distance reduction 

factor

0.60 0.60 0.7 0.45 0.4 

Maximum deceleration for 

cooperative braking (ft/s2) 

−9.84 −9.84 −16.00 −18.00 −18.00 

Advanced merging Activated Activated Activated Activated Activated

Cooperative lane change Activated Activated Activated Activated Activated

Maximum speed difference 1.84 16.00 16.00 16.00 16.00 

Maximum collision time 10.00 10.00 10.00 10.00 10.00 
143 

© 2018 Alexandra Kondyli.

Figure 77. Map. Kansas City Scout speed profile.
144 

© 2018 Alexandra Kondyli.

Figure 78. Map. Calibrated Vissim model speed profile.
145 

© 2019 Alexandra Kondyli.

A. Main 7th Street

© 2019 Alexandra Kondyli.

B. Main Southwest Boulevard upstream. 

© 2019 Alexandra Kondyli.

C. Main Southwest Boulevard. 

© 2019 Alexandra Kondyli.

D. Main 18th Street. 

© 2019 Alexandra Kondyli.

E. Main Lamar Avenue. 

© 2019 Alexandra Kondyli.

F. Main Antioch Road merge.
146 

© 2019 Alexandra Kondyli.

G. Main Johnson Drive. 

© 2019 Alexandra Kondyli.

H. Main SMP. 

© 2019 Alexandra Kondyli.

I. Main 67th Street. 

© 2019 Alexandra Kondyli.

J. Main 67th Street merge.

Figure 79. Charts. Speed-flow graphs along the I–35 southbound corridor.

I–15 CA AIMSUN

Background Information 

The network contains the following characteristics: 

• Total length: 20 mi. 

• Starting and ending points: SR 52 at its southern end to SR 78 at its northern end. 

• Number of lanes: 8–10. 

• Speed limit: 65 mi/h.
147 

Overview

The calibration approach applied to the San Diego, CA, I–15 ICM Aimsun online model is based 

off of the approaches laid out by FHWA within the Traffic Analysis Toolbox Volume III and 

Volume IV: Guidelines for Applying CORSIM Microsimulation Modeling Software 

(Dowling et al. 2004; Holm et al. 2007). 

Source: FHWA. 

Figure 80. Diagram. Flowchart describing the calibration approach 

(Dowling, Skabardonis, and Alexiadis 2004). 

The data collection plan and its appendices detail the data points to be collected and the sources 

for each data point. The team used these data to calibrate and validate the base 24-h model. To 

replicate the various ITS that are currently part of the corridor, the model team created 

application programming interfaces (APIs) to emulate the functionality of the systems, including 

ramp metering. To ensure the system was properly calibrated for this purpose, tests on a single 

on-ramp at Carmel Mountain Road were performed by making sure the correct number of 

vehicles per green light were entering and that the system was properly activating and 

deactivating as well as stepping through the rate table. The prescribed check is as follows: 
148 

1. Warm up the network by running a relatively low demand for several hours to ensure 

network stabilization at a value other than 0. 

2. Gradually increasing the mainline flow showed that the ramp metering follows the trend 

of the traffic volume and the rate is increased, thus reducing the input volume rate. 

3. Decreasing the mainline flow showed the opposite trend of the previous step. The rate 

decreased until meeting the shutoff threshold, after which the ramp metering turned off. 

4. Finally, traffic increased once more to show that the meter turned back on. 

Throughout the process, the ramp meter functioned properly. The ramp meter turned on when 

reaching the threshold value (8,250 veh/h) for the associated mainline flow detector. Once the 

minimum release rate was reached, the flows were decreased and the on-ramp again responded 

accordingly by increasing the rate of discharge. Finally, when the mainline flow reached the 

threshold value for the shutdown (6,625 veh/h), the on-ramp turned off metering as expected.

After this, the team needed to verify the entire system, which was more of a general check by 

means of visually inspecting the network and did not involve stepping through all the rates for 

every location. The verification was as follows:

1. It is necessary to see the on-ramps moving through the rates as traffic increases in the 

peak periods of demand on the network. 

2. The team checked the previous step for both a.m. and p.m., as different rate tables were 

used. It was important to verify that the system works in both peak periods. As the 

demand increased over the peak period, it was necessary that the systems adjusted the 

rates accordingly. 

Driver Behavior Calibration 

Calibration is the adjustment of the various local and global parameters available within a 

software package to ensure that the model is able to reproduce the local driver behavior, traffic 

conditions, and performance. Aimsun comes with default parameters, but those parameters 

provide a starting point and will rarely replicate the local behavior. Therefore, comparing against 

field-observed data, the analyst can go step by step through a calibration process to achieve this 

goal. While preparing to undertake the calibration, it is important that the analyst identify within 

Aimsun which parameters require no further adjustment and which should be reviewed in order 

to match local conditions. The adjustable set of parameters can then be divided into two classes: 

those that affect capacity and those that affect route choice.

Calibration for Capacity

For each vehicle type, the length and other characteristics were adjusted to meet North American 

standards (especially vehicle length to represent larger cars, as the defaults were closer to 

European values). 

For the freeway capacity calibration, the main driver behavior parameter that influences capacity 

is the reaction time. By using an iterative process of comparing the calculated real-world 

capacities to the estimated model capacities as well as the flow/speed relation diagram, this 

global parameter was adjusted to 0.85 s. By comparing the correlation between speed drops and 

the development of congestion and queues, the team adjusted the speed acceptance and the 
149 

sensitivity factor to better represent real-world conditions and establish a reasonable 

representation of the correct capacity.

For surface street capacity calibration, the local and global parameters associated with 

intersection approaches and intersection movements were adjusted using an iterative process. By 

adjusting the reaction time at a stop (the time it takes a vehicle to react from a stopped position) 

and the reaction time at a signal (the time it takes a vehicle to react at a signal), the real-world 

and model intersection capacities were matched. Based on the analysis, the average approach 

capacity or saturation flow rate for signalized locations was approximately 1,495 veh/h/l. Prior to 

changing the parameters, the simulated saturation flows were significantly higher than those 

observed. 

Calibration for Traffic Volumes and Route Choice

Traffic volumes and flows were the principal measures of effectiveness (MOEs) used for the 

calibration of traffic volumes and route choice in the model. For the freeway sections, the 

volumes were compared at on-ramps, off-ramps, GP sections, and express lane sections. For the 

arterial sections, the volumes were compared at intersection approaches, turn movements, and 

mid-block sections. 

The C-logit route choice model was chosen to calculate costs and paths for each O-D pair, as it 

takes into account the degree of overlapping among the different routes to reduce the tendency of 

the logit model to produce route oscillations resulting in a flip-flop process. A route choice 

recalculation cycle of 5 min was set to dynamically update the cost of alternative paths, and a 

scale factor of 2.5 ensured the correct distribution of trips along freeways and arterials. The 

attractiveness weight was set to 1.5 to favor freeways and wider roads instead of local roads 

when experiencing the same cost. 

Throughput Calibration

• Type: 24-h mainline freeway counts; source: Caltrans Performance Measurement System 

(PeMS) online database (California Department of Transportation 2019). 

• Type: 24-h ramp counts; source: PeMS and ramp metering information system (RMIS) 

(San Diego Association of Governments 2008). 

• Type: 24-h express lane counts; source: PeMS (limited) and congestion pricing system 

(CPS) (San Diego Association of Governments 2008). 

• Type: 24-h arterial link counts; source: Sensys arterial travel time system (Wang et al. 

2010). 

• Type: 13-h turning movement counts; source: video counts. 

Calibration targets were introduced to provide more granularity and visibility to the model and to 

help better understand the volumes and speeds. This enhanced granularity and detail helps show 

how the model has been able to replicate the real-world conditions. The team defined new 

criteria to draw a reference between the modeled and observed locations where the hourly 

volume was less than 2,000 veh/h. This volume was raised due to concerns of the levels of traffic 

observed on the local arterials. Showing the validation at all levels results in more confidence in 

the model. 
150 

To perform this analysis and provide more insight to the model, the various counts below 

2,000 veh/h are summarized using two sets of guidelines. The first criterion shows counts within 

15 percent of the observed count, in comparison to the following targets: 

• 75 percent of flows between 750–20,00 veh/h with differences under 15 percent. 

• 70 percent of flows between 0–750 veh/h with differences under 20 percent or under 

150 veh/h. 

The second criterion is the GEH.

(1)

 GEH is a statistic often used in transportation modeling to 

show the representation between the modeled and observed counts/flows and to avoid some of 

the problems when only using absolute difference. The formula for the GEH is as follows: 

(12) 

Where: 

M = modeled volume in veh/h. 

C = observed volume in veh/h. 

From documentation on GEH provided by the UK’s Highway Agency and by Wisconsin 

Department of Transportation, GEHs with a value of 5.0 or lower are a representation of a very 

good fit. A GEH between 5.0 and 10.0 is a satisfactory fit and requires further investigation. A 

GEH greater than 10.0 is indicative of a potential error or a bad fit. Values greater than 10 are 

usually unavoidable in complex models but should be limited as much as possible. The following 

lists the criteria guidelines that Aimsun used to show the validation of the model, as previously 

agreed to with the San Diego Association of Governments: 

• 85 percent GEH less than 5.0 for flows greater than 2,000 veh/h. 

• 95 percent GEH less than 10.0 for flows greater than 2,000 veh/h. 

• 70 percent GEH less than 5.0 for flows less than 2,000 veh/h. 

• 95 percent GEH less than 10.0 for flows less than 2,000 veh/h. 

Volumes and flows were able to meet the requirements and fell within the GEH < 10 criteria. For 

some outlying periods or shoulder hours, the count validation fell short of meeting the criteria 

(e.g., 9–10 a.m. flows between 750–2,000 veh/h within 15 percent was at 43 percent). This is 

because: 

• 750–2,000 veh/h flow count samples were a much smaller range when compared to flow 

counts less than 750 veh/h. 

• In a shoulder hour, it can be very tricky to always meet the calibration targets when the 

full period is as long as 4 or 5 h. In the live system, there are no shoulder hours in the 

1

The GEH formula gets its name from Geoffrey E. Havers, who invented it in the 1970s while working as a 

transportation planner in London, England.
151 

model, as the model only runs for the next hour regardless if it is peak, off-peak, or in 

between. For 2016, the following criteria shown in table 51 were set.

Table 51. Calibration criterial and acceptance targets for 2016. 

Calibration Criteria and Measures Calibration Acceptance Targets

Traffic flows within 15 percent of observed 

volumes for links with peak-period volumes greater 

than 2,000 veh/h 

For 85 percent of cases for links with 

peak-period volumes greater than 

2,000 veh/h 

Sum of all link flows Within 5 percent of sum of all link counts 

Visual audits individual link speeds: visually 

acceptable speed-flow relationship 

To analyst’s satisfaction

Visual audits bottlenecks: visually acceptable 

queuing 

To analyst’s satisfaction

The summary of link count reasonableness assessment results for a typical, no-incident day 

include: 

• 91 of the 93 links (97 percent) met the 15 percent comparison criterion for the a.m. peak 

period—criterion one is met for the a.m. peak.

• 91 of the 96 links (94 percent) meet the 15 percent comparison criterion for the p.m. peak 

period—criterion one is met for the p.m. peak.

• 69 of the 70 links (98 percent) meet the 15 percent comparison criterion for the midday 

peak period—criterion one is met for the midday peak.

• The sum of all model link flows across all periods is 6,881,464, while the sum of 

observed link counts is 6,879,777. These volume sums are within 5 percent and thus 

criterion two is met for the three combined periods. 

• The sum of all model link flows in the a.m. peak period is 2,407,128, while the sum of 

observed link counts is 2,407,567. These volume sums are within 5 percent and thus 

criterion two is met for the a.m. peak period. 

• The sum of all model link flows in the p.m. peak period is 2,625,769, while the sum of 

observed link counts is 2,613,164. These volume sums are within 5 percent and thus 

criterion two is met for the p.m. peak period. 

• The sum of all model link flows in the midday peak period is 1,848,567, while the sum of 

observed link counts is 1,859,046. These volume sums are within 5 percent and thus 

criterion two is met for the midday peak period. 

• For all the peak periods, none of the arterial counts meet the required 2,000 veh/h, thus 

there is no criterion to meet. Although there are differences between observed and 

modeled arterial volumes, these counts are all included with the model sums for each 

period and thus the general flow of traffic along freeways and arterials meets 

criterion two. 
152 

Calibration of Additional Measures

Ramp metering: 

• The team utilized the ITS API calibration.

• The team utilized 30-s data provided from the RMIS system and used it for the 

calibration of the ramp metering API within the Aimsun model.

Travel times: 

• If not from the Sensys database, the team manually collected travel times for the major 

corridors throughout the model. 

• Validation of the transit operations used and available transit vehicle travel times. The 

source of these data was the automatic vehicle location system and the RTMS. 

Field measurements for the travel times included a combination of field-collected travel times 

and travel times available through the Sensys database. The model was adjusted to meet the 

target of modeled travel times within 15 percent of observed travel times in more than 85 percent

of cases. Table 52 summarizes these results.

Table 52. Number of travel times run and percentage by period.

Period Number of Travel Times 

Runs Percentage

Morning peak period 5:00–10:00 a.m. {41 (62)} 

{pass travel times (total)} 66 

Midday peak period 10:00 a.m.–2:00 p.m. {34 (42)} 

{pass travel times (total)} 81 

Evening peak period 2:00–7:00 p.m. {39 (51)} 

{pass travel times (total)} 76 

Queue Data

Queue data were collected and used from a few sources. As queue data were not readily 

available, it is recognized that these data may not be fully accurate but were used as a base for 

the congestion in order to conduct the visual audit. 

Bottlenecks and Speed Contours

For each section of the I–15 corridor, the PeMS database provided 5-min aggregated speed data

(California Department of Transportation 2019). Although some stations were not providing 

observed data, aggregate PeMS data were used. To show the calibration of the I–15 mainline, 

speed contour diagrams were created from the PeMS data and the modeled data (average of 

10 seeds, although any future comparisons based on non-typical day simulations would be 

produced only for one random seed). 
153 

© 2019 Aimsun.

Figure 81. Illustration. Updated PeMS I–15 a.m. southbound speed contour.

© 2019 Aimsun.

Figure 82. Illustration. Updated Aimsun modeled I–15 a.m. southbound speed contour.
154 

The speed contours as well as the work done to match queues and bottlenecks served as the basis 

for the approval of the visual audit. Comparison of the real-world data and the modeled data 

showed a small contradiction because the data reported by the PeMS system were reduced 

significantly for the data collection period between February 1, 2012–May 1, 2012. It is the 

understanding of the group that the reduction was due to the construction within the express 

lanes and the upgrades to the advance traffic management system. Accordingly, the team worked 

with PeMS to better understand the process used to calculate speeds within PeMS and complete 

the comparison where higher levels of confidence were available.

Reasonableness of Animation 

Following the latest visual audits, the team showed that the congestion levels within the model 

improved greatly. In particular, the mainline and CPS appeared to have much more reasonable 

levels of traffic for the a.m. and p.m. peaks. Due to the lack of recent PeMS data, further 

improvements to the bottlenecks and queuing could be required. Following the comments from 

the second San Diego Association of Governments’ visual audit of bottlenecks and the review of 

the model conducted by the San Diego Association of Governments, a few questions were asked 

to the Freeway Service Patrol (FSP) drivers since they have the best knowledge of the network. 

The following are the a.m. questions asked, followed by the FSP response and the updated model 

solution or response: 

• Between approximately 7:30–9:00 a.m., does the southbound mainline experience 

congestion between Bernardo Center Drive to Mercy Road? 

o FSP response: queue exists from Mercy Road to Sabre Springs Defense Access Road. 

 Model shows heavy traffic between Mercy Road to Carmel Mountain Road, with 

low speeds under 15 mi/h. 

• Between approximately 7:45–8:30 a.m., does the southbound mainline experience 

congestion at Carroll Canyon Road? 

o FSP response: yes, slows to 10–15 mi/h due to Miramar Road congestion 

downstream. 

 Model represents this congestion at this time due to the merging movements at 

Carroll Canyon Road. 

• Between approximately 7:30–9:00 a.m., does the southbound mainline experience 

congestion at Miramar Way?

o FSP response: no. 

 In the model, this is generated by weaving movements before the I–15 mainline 

merge to SR 163. Some intermittent slow traffic is generated, but it is only minor 

and short-lived congestion. 

• Between approximately 7:00–9:00 a.m., does the southbound mainline experience 

congestion north of SR 78? 

o FSP response: no. 

 In the model, this area is not congested during this time either.
155 

The following are the responses to the p.m. FSP questions: 

• Between approximately 4:00–6:00 p.m., does the northbound mainline experience 

congestion around Carroll Canyon Road? 

o FSP response: yes, bumper-to-bumper. 

 The model does show congestion, but not exactly “bumper-to-bumper.” See the 

speed contour maps (figure 81 and figure 82). 

• Between approximately 4:30–6:00 p.m., does the northbound mainline experience 

congestion between Bernardo Center Drive and Lake Hodges? (this is from PeMS but not 

in the simulation)

o FSP response: yes, very heavy traffic. 

 The model does have congestion, but it is not too heavy. The model shows 

congestion is between the Via Rancho Parkway exit to Pomerado Road. Once the 

model is online and the PeMS detection is providing current 

not-pre-Thanksgiving data, the model will be fine-tuned. 

• Between approximately 4:30–6:00 p.m., does the northbound mainline experience 

congestion between Lake Hodges and SR 78? 

o FSP response: yes, from Valley Parkway to the SR 78 ramps. 

• If so, is it related to the northbound-to-westbound connection between I–15 and SR 78? If 

not, can other choke points be identified? 

o FSP response: yes, related to the northbound-to-westbound connector ramp.

 The updated model does have congestion along all connector ramps from I–15 to 

SR 78. 

• Between approximately 4:00–6:00 p.m., does the eastbound-to-southbound connector 

ramp between SR 78 and I–15 experience congestion?

o FSP response: yes, traffic on the connector is heavy but still flowing.

• If so, how far back on SR 78 is the queue? 

o FSP response: the queue goes back to the beginning of the off-ramp on SR 78. 

 The model has some traffic at this location, but is not currently able to represent 

the true level of congestion. As better live data become available, the model will 

be improved. It should be noted that the model at SR 78 is only calibrated for 

I–15 and not to the same level for SR 78. 

• Between approximately 4:00–6:00 p.m., does eastbound SR 56 experience congestion 

and a queue from the Ted Williams interchange?

o FSP response: yes, the queue goes back to the Rancho Peñasquitos interchange. 

 The model has some traffic, but it does not go back to the Rancho Peñasquitos 

interchange (approximately halfway). As better real-time data become available, 

this congestion will be improved. 

• Between approximately 4:00–6:00 p.m., does the northbound mainline experience 

congestion north of SR 78? 

o FSP response: no. 

 The model also shows no congestion north of SR 78 in northbound mainline I–15. 
156 

Original photo: © 2017 Google® (see acknowledgements).

Figure 83. Map. Location and geographic boundaries of the I–15 corridor

(Dion and Skabardonis 2015). 

I–394 MN TSIS-CORSIM

Background Information 

The I–394 TSIS-CORSIM model is for the length of I–394 in Minnesota between I–494 and 

I–94, as illustrated in figure 84 and figure 85. The I–394 network was calibrated by MnDOT. 

Because the calibration outcomes were relatively recent and reasonable, no additional calibration 

was performed by the researchers. 
157 

Original map: © 2017 Google® (see acknowledgements).

Figure 84. Map. Calibrated 8-mi stretch of I–394 eastbound between I–494 and I–94. 

© 2019 Minnesota Department of Transportation.

Figure 85. Map. Speed limits near Minneapolis.

Overview

Volume data is from September 30, 2014. This was a Tuesday. The a.m. model was calibrated 

according to volumes and speeds along the parameters set out in chapter 6 of the Advanced 

TSIS-CORSIM Manual found on the MnDOT website in early 2017 (Minnesota Department of 

Transportation 2017). To enhance the calibration, other checks have been added that are not 

listed in the manual. Proportions of inequality are used to check the fit of the simulated data to 

the real data for both volumes and speed. Volumes are also checked using the GEH statistic with 

a target of under 5 percent (less than 3 percent preferred) for the peak hour and under 3 percent

for the 3-h simulation. Results are also compared to the 2017 Congestion Report to verify that 

congestion is occurring in the correct areas and for the same time (by use of animation files) 

(Minnesota Department of Transportation 2017). 
158 

The team obtained spreadsheets2

 from MnDOT listing the balanced 2014 volumes, a calibration 

worksheet that checks the percentages (volumes of 10 percent, speed of 20 percent), and a 

graphical representation of the I–394 corridor. The graphical representation is complete with 

peak hour modeling results at both the segment and by lane level. MnDOT also provided speed 

limits for I–394 between TH 169 and I–94, and the 2014 Congestion Report (Minnesota 

Department of Transportation 2014). Proportions of inequality can be found in the CalibVolume 

or CalibSpeed tabs of the spreadsheet. GEH values can be found in the FREReport tab. Other 

information related to developing the TSIS-CORSIM input file is also in this Excel document.

Throughput Calibration

The throughput calibration sheet provides simulated volumes, which are transferred to the main 

calibration spreadsheet. Figure 86 and figure 87 illustrate the user interface of the calibration 

spread sheet. Although the throughput numbers in figure 86 are not legible in the context of this 

report, the team reviewed these numbers and plots to determine that throughputs from the 

microsimulation model were reasonably accurate. The same is true for the calibration of speeds 

and densities, as shown in figure 87 and figure 88. 

© 2019 Minnesota Department of Transportation

Figure 86. Screenshot. Calibration of I–394 MN volumes. 

Calibration of Additional Measures 

The team further reviewed the calibration of speed data and density data, as shown in figure 87

and figure 88. 

2

The spreadsheet documents are unpublished.
159 

© 2019 Minnesota Department of Transportation.

Figure 87. Screenshot. Calibration of I–394 MN speeds.

© 2019 Minnesota Department of Transportation.

Figure 88. Screenshot. Calibration of I–394 MN densities.

Reasonableness of Animation 

The review of animation will consider the consistency of animated and field-measured results 

(regarding congested segments and time periods) and the existence of any “stuck” vehicles.

Check 1: the MnDOT calibration spreadsheet showed level of service (LOS) F (forced or 

breakdown flow) during the “MOE hour” (i.e., 7:00–8:00 a.m., or time periods 5 through 8 out 
160 

of 12) between upstream node 5172 and downstream node 5008. This was mostly consistent with 

the animation shown in figure 89. Segment 5006–5008 seemed like it might be slightly better 

than LOS F, but the density (43 vehicles per mile per lane) was right on the LOS F threshold. 

Queuing began to form in time period 5 near downstream node 5006. The furthest upstream node 

(5172) did not experience congestion until time period 8 (i.e., the queue gradually became longer 

and stretched further upstream between time period 5 and time period 8). 

Source: FHWA.

Figure 89. Screenshot. Formation of queue spillback on I–394 MN. 

Check 2: a similar situation occurred between upstream node 5022 and downstream node 5048, 

except this time the queue seemed to extend well upstream of node 5022. This is shown in 

figure 90. 

Source: FHWA.

Figure 90. Screenshot. Queue spillback extent on I–394 MN. 

The original hypothesis was that a different version of TSIS-CORSIM may have been used to 

generate the performance measures used for calibration, but this was not the case. Instead, the 

reason for link 5020–5022 not showing up as LOS F has to do with the 500-ft lane drop. When 

the toolset calculates the link MOEs, it includes the lane drop as part of the number of lanes. As 

this lane drop is a little over half the length of the link, this is causing the link speed and density 

numbers to be better than anticipated. This also happens on links with lane additions, 

acceleration lanes, and deceleration lanes. This discovery gave more confidence that the model 

was calibrated well.
161 

ACKNOWLEDGEMENTS

The original maps depicted in figure 17 is the copyright property of Microsoft® BingTM. The 

map overlay illustrates the traffic volume and bottleneck location and was developed as a result 

of this research project. The overlays include orange, blue, and green lines showing high 

volumes, bottlenecks, and free flow conditions. 

The original map depicted in figure 18 is the copyright property of Microsoft® BingTM. The map 

overlay illustrates the traffic volume and was developed as a result of this research project. The 

overlays include green, yellow, and orange lines showing free flow conditions, moderate 

volumes, and high volumes, respectively. 

The original map depicted in figure 19 is the copyright property of Microsoft® BingTM. The map 

overlay illustrates the traffic speed and bottleneck location and was developed as a result of this 

research project. The overlays include orange, blue, and green lines showing high volumes, 

bottlenecks, and free flow conditions. 

The original map depicted in figure 20 is the copyright property of Microsoft® BingTM. The map 

overlay illustrates the traffic speed and bottleneck location and was developed as a result of this 

research project. The overlays include orange, blue, and green lines showing high volumes, 

bottlenecks, and free flow conditions. 

The original map depicted in figure 21 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay indicates the location of the 

US 322 left-side on-ramp, which is the location of a bottleneck, and was developed as a result of 

this research project.

The original map depicted in figure 22 is the copyright property of Microsoft® BingTM. The map 

overlay illustrates the traffic speed and bottleneck location and was developed as a result of this 

research project. The overlays include orange, blue, and green lines showing high volumes, 

bottlenecks, and free flow conditions. 

The original map depicted in figure 23 is the copyright property of Google® EarthTM and can be 

accessed from https://www.google.com/earth. The map overlay illustrates the traffic volume and 

was developed as a result of this project. Green shading on the facility indicates free flow 

conditions, and orange indicates congested conditions. A red square indicates the location of the 

southbound I–476 on-ramp in Vissim.

The original map depicted in figure 24 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay indicates the location of the 

US 322 left-side on-ramp, which is the location of a bottleneck, and was developed as a result of 

this research project.

The original map depicted in figure 25 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay indicates the location of the 

I–476 interchange, which is the location of a bottleneck, and was developed as a result of this 

research project.
162 

The original map depicted in figure 26 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map shows the congested areas and ramp 

locations of I–35 northbound and southbound. 

The original map depicted in figure 27 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map indicates the I–66 study corridor for this 

research project.

The original map depicted in figure 28 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay indicates the locations of six 

interchanges on the I–66 study corridor and the data collection instruments near each 

intersection. The overlay was developed as a result of this research project. 

The original map depicted in figure 41 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. A map overlay serves to indicate the segment of 

I–35 in Kansas City, KS, that is being called out in a satellite image. The overlay was developed 

as a result of this research project.

The original maps depicted in figure 50 are the copyright property of Google® MapsTM and can 

be accessed from https://www.google.com/maps. The overlay for each map uses red or blue lines 

to indicate the locations of INRIX traffic management centers on I–66. The overlay was 

developed as a result of this research project.

The original map depicted in figure 51 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay indicates the locations of 

zones 1 through 10. The overlay was developed as a result of this research project.

The original map depicted in figure 55 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. A map overlay serves to indicate the recurring 

congestion segments of I–270 in Maryland. The overlay was developed as a result of this 

research project.

The original map depicted in figure 56 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes a field-measured heat 

map illustrating speeds on the study section shown in the map during the 4-h morning period of 

6–10 am. The overlay was developed as a result of this research project.

The original map depicted in figure 57 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes a field-measured heat 

map illustrating speeds on the study section shown in the map during the 4-h evening period of 

4–8 p.m. The overlay was developed as a result of this research project.

The original map depicted in figure 58 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes a field-measured heat 

map illustrating speeds on the study section shown in the map during the evening peak hour with 

location benchmarks. The overlay was developed as a result of this research project.
163 

The original map depicted in figure 59 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes a field-measured heat 

map illustrating speeds on the study section shown in the map during the morning peak hour with 

location benchmarks. The overlay was developed as a result of this research project. 

The original map depicted in figure 60 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes a field-measured heat 

map illustrating speeds on the study section shown in the map during the afternoon peak period 

with location benchmarks. The map overlay includes simulation animations that match the 

field-measured speed heat map for most space-time points measured at 7:10 a.m. The overlay 

was developed as a result of this research project.

The original map depicted in figure 61 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes simulation animations 

that match the field-measured speed heat map for most space-time points measured at 7:20 a.m. 

The overlay was developed as a result of this research project.

The original map depicted in figure 62 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes simulation animations 

that match the field-measured speed heat map for most space-time points measured at 7:30 a.m. 

The overlay was developed as a result of this research project.

The original map depicted in figure 63 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes simulation animations 

that match the field-measured speed heat map for most space-time points measured at 7:40 a.m. 

The overlay was developed as a result of this research project.

The original map depicted in figure 64 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes simulation animations 

that match the field-measured speed heat map for most space-time points measured at 5:10 p.m. 

The overlay was developed as a result of this research project.

The original map depicted in figure 65 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes simulation animations 

that match the field-measured speed heat map for most space-time points measured at 5:20 p.m. 

The overlay was developed as a result of this research project.

The original map depicted in figure 66 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map overlay includes simulation animations 

that match the field-measured speed heat map for most space-time points measured at 5:40 p.m. 

The overlay was developed as a result of this research project.

The original map depicted in figure 72 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map shows heavy southbound congestion 

originating from the I–476 interchange. 
164 

The original map depicted in figure 74 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map shows queues originating at the US 322 

interchange and extending beyond Chichester Avenue. 

The original map depicted in figure 83 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. This map shows the upstream and downstream 

endpoints of the I–15 corridor. 

The original map depicted in figure 84 is the copyright property of Google® MapsTM and can be 

accessed from https://www.google.com/maps. The map shows the calibrated 8-mi stretch of 

I–394 eastbound between I–494 and I–94.
165 

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Recycled HRDO-20/05-20(WEB)E Recyclable