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FHWA Safety Program

SAFETY ANALYSIS NEEDS ASSESSMENT 

FOR TRANSPORTATION SYSTEMS 

MANAGEMENT AND OPERATIONS
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

 

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SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-19-041

2. Government Accession

No.

3. Recipient's Catalog No.

4. Title and Subtitle

Safety Analysis Needs Assessment for Transportation Systems Management and

Operations

5. Report Date

October 2019

6. Performing Organization Code

7.Author(s)

R.J. Porter, Michael Dunn, Ian Hamilton, Jeff Gooch, and Vikash Gayah

8. Performing Organization Report

No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

8300 Boone Blvd., Ste. 700

Vienna, VA 22182-2626

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00005 (VHB)

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Final Report, July 2017 – October

2019

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Jerry Roche, Federal Highway Administration Office of Safety.

16. Abstract

This report documents a safety analysis needs assessment for Transportation Systems Management and Operations (TSMO). It

includes the following key content: syntheses of strategy-specific safety performance knowledge and capabilities (Chapter 2), a

synthesis of research on interrelationships between measures of traffic operational performance and safety performance (Chapter

3), a synthesis of research relating weather and weather-related road conditions to safety performance (Chapter 4), a synthesis of

research, tools, and challenges in estimating safety performance effects of TSMO that result from changes in travel choices and

traffic demand patterns (Chapter 5), and TSMO-related safety performance analysis gaps, limitations, and needs (Chapter 6).

The literature contains various types of safety performance evaluations for some TSMO strategies. However, there are only a

limited number of published safety performance evaluations that are robust enough to inform future evaluations and/or the

development of quantitative safety performance predictions. There is a need for safety and operations staff from local, State, and

Federal agencies, as well as researchers and other stakeholders, to engage in regular dialog on safety performance evaluation

needs and priorities specific to TSMO strategies. The needs assessment also identified five categories of methodological needs

for analyzing the safety performance of TSMO: 1) “sub-annual” safety data collection and analysis, 2) safety performance

effects beyond the site level, 3) safety performance effects of operational conditions, 4) study design and statistical analysis, and

5) mechanistic approaches to safety performance analysis of TSMO. The report concludes with recommended next steps in

continuing to advance safety performance analysis capabilities of TSMO. Readers of this report will gain an increased

understanding of existing knowledge, analysis methods, and research needs in the context of quantifying the safety performance

effects of TSMO.

17. Key Words: 18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this

page) Unclassified

21. No. of

Pages 220

22. Price

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

ii
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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https://www.fhwa.dot.gov/publications/convtabl.cfm
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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TABLE OF CONTENTS

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

Sub-Annual Safety Data Collection and Analysis........................................................... 3

Safety Performance Effects Beyond the Site Level ....................................................... 4

Safety Performance Effects of Traffic Operational Conditions.................................... 4

Study Design and Statistical Analysis .............................................................................. 5

Mechanistic Approaches to Safety Performance Analysis of TSMO........................... 5

CHAPTER 1— OVERVIEW ............................................................................................. 7

Transportation Systems Management and Operations (TSMO)................................. 8

Safety Performance Analysis.......................................................................................... 10

Approaches Based on Analysis of Crash Data ................................................................................... 11

Approaches Based on Surrogate Measures of Safety........................................................................ 12

Approaches Based on Simulating Crash Occurrence and Severity ............................................... 13

Reliability of Safety Effectiveness Evaluations...................................................................................... 14

Statistical Significance ............................................................................................................................... 15

Correlation versus Causation ................................................................................................................ 17

CHAPTER 2 — SAFETY PERFORMANCE OF SPECIFIC TSMO STRATEGIES: 

CURRENT STATE OF PRACTICE, KNOWLEDGE, AND SKILLS..........................19

Identification of Safety Resources..................................................................................20

Individual Strategy Syntheses ........................................................................................22

Managed Lanes........................................................................................................................................... 22

Part-Time Shoulder Use.......................................................................................................................... 40

Reversible Lanes ........................................................................................................................................ 47

Dynamic Lane Use Control .................................................................................................................... 48

Dynamic Junction Control ...................................................................................................................... 49

Ramp Metering .......................................................................................................................................... 51

Variable Speed Limits ............................................................................................................................... 56

Traffic Signal Coordination ..................................................................................................................... 63

Adaptive Signal Control Technology .................................................................................................... 69

Transit Signal Priority............................................................................................................................... 73

Truck Signal Priority................................................................................................................................. 77

Queue Jump Lanes.................................................................................................................................... 77

Safety Warning Applications................................................................................................................... 79

Work Zone Management and Temporary Traffic Control Applications..................................... 90

Traffic Incident Management Strategies............................................................................................... 96

CHAPTER 3 — RELATIONSHIPS BETWEEN TRAFFIC OPERATIONAL 

PERFORMANCE AND SAFETY PERFORMANCE.....................................................99
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Introduction......................................................................................................................99

Annual Relationships .......................................................................................................99

Typical Data.............................................................................................................................................. 100

Methods..................................................................................................................................................... 100

Notable Results....................................................................................................................................... 104

Real-Time Relationships ...............................................................................................110

Typical Data.............................................................................................................................................. 111

Methodology ............................................................................................................................................ 111

Notable Results....................................................................................................................................... 114

Key Takeaways...............................................................................................................119

CHAPTER 4 — EFFECTS OF WEATHER AND WEATHER-RELATED ROAD 

CONDITIONS ON SAFETY PERFORMANCE .........................................................121

Introduction....................................................................................................................121

Data................................................................................................................................. 121

Weather Conditions.............................................................................................................................. 121

Data Integration ...................................................................................................................................... 125

Analysis Approaches......................................................................................................126

Findings to Date.............................................................................................................128

Effects of Weather on Safety Performance....................................................................................... 128

Assessments of Mitigation Strategies.................................................................................................. 132

Key Takeaways...............................................................................................................135

CHAPTER 5 — MODELING TRAVELER BEHAVIOR AND SAFETY 

INTERACTIONS ...........................................................................................................137

Introduction....................................................................................................................137

Previous Research .........................................................................................................139

Trip Generation....................................................................................................................................... 139

Alternatives: Route Shift, Temporal Shift, and Modal Shift............................................................ 140

Challenges to Quantifying Strategy Effectiveness ............................................................................ 143

Connections to Safety............................................................................................................................ 144

Taxonomy of Demand Management Strategies ........................................................146

Trip Generation....................................................................................................................................... 148

Route Shift................................................................................................................................................ 149

Temporal Shift ......................................................................................................................................... 149

Modal Shift................................................................................................................................................ 149

Modeling the Safety Effects of TSMO Strategies .......................................................150

Assumptions............................................................................................................................................. 150
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Methods to Model Trip Generation................................................................................................... 151

Methods to Model When a Trip is Made .......................................................................................... 151

Methods to Model Mode Choice ........................................................................................................ 152

Methods to Model Network Assignment.......................................................................................... 152

Limitations & Future Directions ..................................................................................153

Non-Linear Relationship between Travel Demand and TSMO.................................................... 153

Spillover Effects........................................................................................................................................ 154

Autonomous and Connected Technologies..................................................................................... 154

Need for Additional Empirical Safety Analyses and Modeling ...................................................... 154

CHAPTER 6 — SAFETY PERFORMANCE ANALYSIS NEEDS .............................155

Strategy-Specific Evaluation Needs.............................................................................155

HOV/HOT Lanes.................................................................................................................................... 155

Part-Time Shoulder Use........................................................................................................................ 156

Bus Facilities and Preferential Treatments........................................................................................ 156

Traffic Signal Technology and Timing Practices ............................................................................... 156

Ramp Metering ........................................................................................................................................ 157

Variable Speed Limits............................................................................................................................. 157

Dynamic Lane Assignment.................................................................................................................... 157

Strategy Combinations........................................................................................................................... 158

Methodological Needs...................................................................................................158

1. Need to Conduct “Sub-Annual” Safety Data Collection and Analysis.............................. 158

2. Need to Analyze Safety Performance Effects Beyond the Site Level ................................. 161

3. Need to Analyze Safety Performance Effects of Traffic Operational Conditions........... 163

4. Needs Related to Study Design and Statistical Analysis........................................................ 169

5. Need to Explore Mechanistic Approaches to Safety Analysis of TSMO........................... 169

CHAPTER 7 — SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS.....171

ACKNOWLEDGEMENTS............................................................................................177

REFERENCES ................................................................................................................179
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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LIST OF TABLES

Table 1. TSMO strategies covered in Chapter 2..........................................................20

Table 2. Summary of safety performance evaluation findings for HOV/HOT 

conversions.......................................................................................................................34

Table 3. Summary of safety performance evaluation findings for HOV/HOT design 

features. ............................................................................................................................35

Table 4. Summary of safety performance evaluation findings for exclusive truck 

facilities and truck restrictions.......................................................................................37

Table 5. Summary of safety performance evaluation findings for exclusive bus 

facilities. ............................................................................................................................39

Table 6. Summary of safety performance evaluation findings for part-time shoulder 

use......................................................................................................................................46

Table 7. Summary of safety performance evaluation findings for ramp metering. . 54

Table 8. Summary of safety performance evaluation findings for variable speed 

limits.................................................................................................................................. 60

Table 9. Summary of safety performance evaluation findings for traffic signal 

coordination. ....................................................................................................................67

Table 10. Summary of safety performance evaluation findings for adaptive signal 

control technology...........................................................................................................72

Table 11. Summary of safety performance evaluation findings for transit signal 

priority. .............................................................................................................................76

Table 12. Summary of safety performance evaluation findings for intersection 

warning applications........................................................................................................86

Table 13. Summary of safety performance evaluation findings for curve warning 

applications.......................................................................................................................88

Table 14. Summary of safety performance evaluation findings for queue warning 

applications.......................................................................................................................89

Table 15. Summary of safety performance evaluation findings for animal warning 

applications.......................................................................................................................89
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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Table 16. Summary of safety performance evaluation findings for work zone 

applications.......................................................................................................................94

Table 17. Summary of safety performance evaluation findings for traffic incident 

clearance strategies.........................................................................................................98

Table 18. Summary of rural model coefficients (standard errors) for the models 

developed by Lord et al.(175) ..........................................................................................106

Table 19. Summary of urban model coefficients (standard errors) for the models 

developed by Lord et al.(175) ..........................................................................................106

Table 20. Comparing freeway LOS with predicted crash rates for six-lane freeways 

in Colorado, adapted from Kononov et al.’s plot in figure 26.(180) ............................110

Table 21. Correlations between traffic factors and the probability of crash severity 

and type as found by Golob et al. (186) ..........................................................................118

Table 22. Adjustment factors for crashes and average driving speed under various 

road surface conditions, as synthesized by Strong et al.(199)......................................129

Table 23. Recommended transit elasticity values.(238) ...............................................142

Table 24. Ten new shorter-term paradigm safety strategies (less than three 

years).(248) ........................................................................................................................145

Table 25. Five new longer-term paradigm safety strategies (more than three 

years)(248) .........................................................................................................................146

Table 26. Example taxonomy for considering transportation demand effects of 

pricing strategies............................................................................................................147

Table 27. Example taxonomy for considering transportation demand effects of 

capacity optimization strategies..................................................................................148

Table 28. Summary of surrogate safety studies of TSMO strategies. .....................166
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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LIST OF FIGURES

Figure 1. Graphic. Framework for organizing TSMO strategies. ................................ 9

Figure 2. Graphic. Typical U.S. managed lane facilities and applications.(23) ............22

Figure 3. Photo. Separated express lanes with access points on I-495 in Fairfax 

County, VA.(24)..................................................................................................................23

Figure 4. Photo. Previous implementation of static part-time shoulder use on I-66 

in Virginia.(50).....................................................................................................................41

Figure 5. Photo. Bus-on-shoulder operations in Minneapolis-St. Paul, Minnesota.(50)

...........................................................................................................................................41

Figure 6. Graphic. Rendering of a dynamic merge control arrangement in a freeway 

setting.(62) ..........................................................................................................................49

Figure 7. Graphic. Basic ramp metering configuration.(65)..........................................51

Figure 8. Graphic. Rendering of variable speed limit sign gantry in Seattle, WA.(77)

...........................................................................................................................................56

Figure 9. Graphic. Time-space diagram of a coordinated timing plan for a corridor 

with four signals.(89) ..........................................................................................................63

Figure 10. Photo. Curve warning sign with flashing beacon.(128) ................................. 82

Figure 11. Photo. Portable dynamic end-of-queue warning sign in Wisconsin.(135) ..84

Figure 12. Photo. Animal-activated dynamic warning sign in Arizona.(138) ...............85

Figure 13. Equation. Proportional relationship between crash frequency and AADT 

in traditional SPFs. ........................................................................................................101

Figure 14. Equation. Standard functional form of a segment SPF...........................101

Figure 15. Equation. HSM CMF for high volume on freeway segments..................101

Figure 16. Equation. Proportional relationship between multiple-vehicle crash 

frequency and AADT for ramp segments in the HSM. .............................................102

Figure 17. Graphic. The Sigmoid-shaped SPF estimated for Colorado six-lane 

freeways.(179)....................................................................................................................102
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Figure 18. Equation. Predicted annual crash frequency for a freeway segment as a 

function of hourly flow. ................................................................................................. 103

Figure 19. Equation. Predicted annual crash frequency for a freeway segment as a 

function of hourly flow and density..............................................................................103

Figure 20. Equation. Predicted annual crash frequency for a freeway segment as a 

function of hourly flow and V/C ration. .......................................................................103

Figure 21. Graphic. High volume CMF for the freeway segment predictive 

methodology in the HSM. .............................................................................................105

Figure 22. Graphic. Predicted crash frequency per kilometer for one freeway lane 

as a function of traffic flow.(174) .....................................................................................107

Figure 23. Graphic. Predicted crash frequency per kilometer for one freeway lane 

as a function of vehicle density.(174)...............................................................................107

Figure 24. Graphic. Predicted crash frequency per kilometer for one freeway lane 

as a function of V/C ratio.(174) ........................................................................................108

Figure 25. Graphic. Six-lane freeway SPF for Colorado with zone labels based on 

critical density.(179)..........................................................................................................109

Figure 26. Graphic. Relating the six-lane freeway SPF to level of service.(179) ........109

Figure 27. Equation. Typical functional form of a binary logit prediction the 

probability of a crash occurring. ..................................................................................111

Figure 28. Graphic. Histogram of freeway crashes based on average vehicle speed 

across all three lanes at the loop detector immediately upstream of the crash 

location (Station F).(182) ................................................................................................. 113

Figure 29. Equation. Coefficient of variation of speed at loop detector I. ..............114

Figure 30. Graphic. Comparison of coefficient of variation of speed at the 

downstream detector for non-crash and crash events.(181)........................................114

Figure 31. Graphic. Comparison of occupancy at the upstream detector for noncrash and crash events.(181) ............................................................................................115

Figure 32. Graphic. Contour map showing relative odds of a crash as a function of 

changes in coefficient of variation in speed along a segment of highway.(183)..........117
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Figure 33. Graphic. Various sensor and data components of RWIS.(194) .................123

Figure 34. Graphic. Map showing location of crash and location of snowplow 

image.(195) ........................................................................................................................124

Figure 35. Photo. Snowplow image, taken 11 minutes after the crash occurred.(195)

.........................................................................................................................................124

Figure 36. Graphic. Rates of fatal crashes on snowy days and the first snowy day 

compared with dry days, by age group.(209) ................................................................. 131

Figure 37. Photo. Example of variable speed limit sign with additional variable 

message sign for warning drivers of weather-related road conditions in 

Washington.(229) ..............................................................................................................134

Figure 38. Equation. Example utility function for mode choice.(251).........................152
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

xii 

ACRONYMS

AADT annual average daily traffic

AASHTO American Association of State Highway and Transportation Officials

ASCT Adaptive Signal Control Technology

ATDM Active Transportation and Demand Management

AVL automated vehicle location

AWIS Automated Work Zone Information System

AWS advance warning signal

B/C benefit-cost

BRT bus rapid transit

CMF crash modification factor 

CVS coefficient of variation of speed

DCM Data Capture and Management

DIC Deviance Information Criterion

DOT Department of Transportation

DRAC deceleration rate to avoid a crash

DRCOG Denver Regional Council of Governments

DTA dynamic traffic assignment 

EB empirical Bayes

EDC Every Day Counts

EMS emergency management services

EOQ end-of-queue 

EOQWS end-of-queue warning system
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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FAA Federal Aviation Administration

FARS Fatal Accident Reporting System

FAST Fixing America’s Surface Transportation

FAST fixed automated spray technology

FHWA Federal Highway Administration

FI Fatal and injury crashes (includes KABC crashes on the KABCO scale) 

HCM Highway Capacity Manual 

HOT high-occupancy toll

HOV high-occupancy vehicle

HSM Highway Safety Manual 

ICWS intersection conflict warning systems

iMiT Incident Management Integration Tool

ITRD International Transport Research Documentation

ITS intelligent transportation systems

ITS JPO Intelligent Transportation Systems Joint Program Office

KA fatal and suspected serious injury crashes (KA on the KABCO scale)

KABCO all possible severity levels of a crash determined by the highest injury 

severity for any person involved in the crash:

K = fatal

A = suspected serious injury

B = suspected minor injury

C = Possible injury

O = No apparent injury

LOS level of service

LUCS lane use control signals

MAP-21 Moving Ahead for Progress in the 21st Century 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

xiv

MARS multivariate adaptive regression splines

MLPNN multilayer perceptron neural networks

MnDOT Minnesota Department of Transportation

MoDOT Missouri Department of Transportation

MPC model predictive control

MPO Metropolitan Planning Organization 

MV multiple-vehicle

NB negative binomial

NCHRP National Cooperative Highway Research Program

NOAA National Oceanic and Atmospheric Administration

NWS National Weather Service

PDO property damage only 

PET post-encroachment time

PRS portable rumble strip

PTSU part-time shoulder use

RSAPv3 Roadside Safety Analysis Program version 3

RTM regression to the mean

RWIS Road Weather Information System

Safety PM Final Rule National Performance Management Measures: Highway Safety 

Improvement Program Final Rule

SCATS Sydney Coordinated Adaptive Traffic System

SPF safety performance function

SSAM Surrogate Safety Assessment Model

SV single-vehicle
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TCRP Transit Cooperative Research Program

TIM traffic incident management

TkSP truck signal priority 

TLR truck lane restriction

TPF Transportation Pooled Fund 

TRB Transportation Research Board

TRID Transport Research International Database 

TSMO Transportation Systems Management and Operations 

TSP transit signal priority 

TTC time to collision

USDOT United States Department of Transportation

VAMS Value Added Meteorological Services

V/C volume-to-capacity

VMT vehicle miles traveled

VEWF Vehicle Entering When Flashing

VSL variable speed limits

V2I vehicle-to-infrastructure 

V2V vehicle-to-vehicle

WRT with respect to

WxDE Weather Data Environment
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

1 

EXECUTIVE SUMMARY

Moving Ahead for Progress in the 21st Century (MAP-21) defines Transportation Systems 

Management and Operations (TSMO) as an “integrated set of strategies to optimize the 

performance of existing infrastructure through the implementation of multimodal and 

intermodal cross-jurisdictional systems, services, and projects designed to preserve capacity and 

improve security, safety, and reliability of the transportation system” (MAP-21 §1103 (a) (30) 

(A)). TSMO offers agencies a wide range of potential strategies for addressing system- and 

project-level performance needs with cost-effective, tailored strategies. State and local agencies 

are increasingly recognizing TSMO as a core business area in support of maximizing the 

performance of their transportation infrastructure and making better use of resources. Some 

regions in the United States have found it useful to develop TSMO plans to define a common 

vision for TSMO in the region, develop performance objectives to guide the selection of TSMO 

strategies, and identify performance measures that enable the region to track progress towards 

their objectives. TSMO plans also identify potential projects, services, and implementation 

policies to reach the objectives.

Performance analysis helps agencies make sound decisions on which TSMO policies, services, 

and projects to pursue as part of performance-based planning and programming. A performance 

analysis of a TSMO strategy might quantify, for example, how the strategy would be expected 

to affect measures of travel time, travel time reliability, pollutants/air quality, and the number 

and severity of traffic crashes. The agency could monetize these expected effects and determine 

an overall benefit-cost ratio for the investment.

The ability for agencies to quantify the effects of TSMO strategies on the number and severity 

of traffic crashes is limited when compared to similar abilities for operational performance 

measures. This report presents a safety performance analysis needs assessment for TSMO. The 

purpose of this needs assessment was to characterize the current state of safety performance 

analysis practice, knowledge, and skills with respect to specific TSMO strategies and broader 

TSMO program areas, and then to identify gaps and associated research needs. Readers of this 

report will gain an increased understanding of existing knowledge, analysis methods, and 

research needs in the context of quantifying the safety performance effects of TSMO. This 

information will be valuable to staff at various Federal, State, and local transportation agencies, 

as well as universities and other organizations that are involved in safety performance research 

and working to advance performance-based practices in planning, design, operations, and 

maintenance. 

The objective of a safety performance analysis of a TSMO strategy (or combination of 

strategies) is to determine how implementing the strategy (or combination of strategies) will 

affect, or has affected, safety performance. The number, severity, and types of crashes 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

2 

characterize the safety performance of a highway segment, intersection, facility, corridor, 

subarea, or network. While safety and TSMO have always had visible interrelationships, there is 

incompatibility between many existing safety performance analysis tools and the characteristics 

of TSMO. For example, few TSMO strategies have robust crash modification factors (CMFs) 

and the American Association of State Highway and Transportation Officials (AASHTO) 

Highway Safety Manual (HSM) analysis methods do not currently incorporate daily, hourly, or 

sub-hourly variations in traffic characteristics and the road environment that are key factors in

fully assessing the safety performance impacts of TSMO. 

The needs assessment characterized the current state of TSMO-related safety performance 

analysis practice, knowledge, and skills through four sets of activities:

1) Syntheses of TSMO strategy-specific safety performance evaluations and analysis 

capabilities (Chapter 2). 

2) A synthesis of research on interrelationships between measures of traffic operational 

performance and safety performance (Chapter 3).

3) A synthesis of research relating weather and weather-related road conditions to safety 

performance (Chapter 4). 

4) A synthesis of research, tools, and challenges in estimating safety performance effects of 

TSMO that result from changes in travel choices and traffic demand patterns (Chapter 

5). 

The syntheses of strategy-specific safety performance evaluations and analysis capabilities 

addresses 15 strategies or sets of strategies:

1) Managed lanes

2) Part-time shoulder use

3) Reversible lanes

4) Dynamic lane use control

5) Dynamic junction control

6) Ramp metering

7) Variable speed limits

8) Traffic signal coordination

9) Adaptive signal control technology

10) Transit signal priority

11) Truck signal priority

12) Queue jump lanes

13) Safety warning applications

14) Work zone management and temporary traffic control applications

15) Traffic incident management

The strategy-specific syntheses show that the literature contains various types of safety 

performance evaluations for some TSMO strategies. However, there are only a limited number 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

3 

of published safety performance evaluations that may be robust enough to inform future 

evaluations or the development of quantitative safety performance predictions. There is a need 

for safety and operations staff from local, State, and Federal agencies, as well as researchers and 

other stakeholders, to engage in regular dialogue on safety performance evaluation needs and 

priorities specific to TSMO strategies. TRB activities, AASHTO meetings, and pooled-fund 

efforts are logical forums for such communications. The report presents an overview of 

potential near-term needs for consideration by these groups that cover HOV/HOT lanes, parttime shoulder use, bus facilities and preferential treatments, traffic signal technology and timing 

practices, ramp metering, variable speed limits, dynamic lane assignment, and strategy 

combinations. 

The syntheses of strategy-specific safety performance knowledge and capabilities documented in 

Chapter 2, as well as the syntheses of crosscutting topics contained in Chapters 3, 4, and 5, also 

resulted in insights on methodological (i.e., study design and analysis) issues and related lessons 

learned. These issues and lessons learned led to a set of methodological safety performance 

analysis needs for TSMO, which fell into five categories:

1) “Sub-annual” safety data collection and analysis. 

2) Safety performance effects beyond the site level.

3) Safety performance effects of traffic operational conditions.

4) Study design and statistical analysis.

5) Mechanistic approaches to safety performance analysis of TSMO. 

SUB-ANNUAL SAFETY DATA COLLECTION AND ANALYSIS

A significant number of TSMO strategies operate (or have the potential to operate) 

dynamically, based on changing traffic or environmental conditions. Most safety performance 

evaluations of TSMO strategies in the literature ignored these operational aspects, instead 

analyzing annual crash frequencies (i.e., crash frequencies across all days, all times) and using an 

annual average number of vehicles as a measure of exposure (i.e., numbers of vehicles across all 

days, all times). 

There is a need to distinguish between the following measures of crash frequency and exposure 

as part of safety performance evaluations of TSMO:

• Average crash frequency (all days, all times) and actual or average number of vehicles 

(all days, all times): most relevant to TSMO features and strategies that operate 

permanently (“on” 24/7).

• Average crash frequency (during specific statically defined days or times, such as 

daytime, weekday, weekday peak-period) and actual or average number of vehicles 

(during the same specific statically defined days or times, such as annual weekday 

volume, annual weekday peak-period volume): most relevant to TSMO features and 

strategies that operate on a set time-of-day schedule.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

4 

• Average crash frequency (during dynamically defined days or times) and actual or 

average number of vehicles (during dynamically defined days or times, such as during 

certain operational conditions or weather conditions): most relevant to TSMO features 

and strategies that operate dynamically.

SAFETY PERFORMANCE EFFECTS BEYOND THE SITE LEVEL

This methodological need deals with capturing safety performance effects of TSMO strategies 

beyond a site level to get a more complete picture of their total effects. Many of the TSMO 

strategies reviewed in Chapter 2 are expected to affect performance beyond an individual 

segment, intersection, or interchange. The needs assessment identified at least four different 

mechanisms by which this occurs: 

1. Application of the strategy itself extends beyond an individual intersection or segment 

and therefore has an effect on performance at that level (e.g., arterial signal 

coordination, part-time shoulder use, and managed lanes).

2. The location of crashes changes following the removal of a bottleneck.

3. The spatial boundary at which safety performance is affected by non-recurring 

congestion resulting from an incident as well as the spatial boundary at which safety 

performance is affected by incident management strategies changes with the 

characteristics of each incident.

4. Some TSMO strategies are expected to effect performance at a corridor, area, or 

system level because of their influence on traveler behavior (e.g., where, when, and how 

people make trips).

SAFETY PERFORMANCE EFFECTS OF TRAFFIC OPERATIONAL 

CONDITIONS

TSMO strategies affect traffic operational and safety performance. The safety performance 

effects of TSMO strategies will depend on the traffic operational conditions with a strategy in 

place and what would have been the traffic operational conditions without the strategy. There

is a need to develop methodologies and tools that incorporate traffic operational conditions 

into the safety performance analysis of TSMO. Depending on the strategy, examples of 

addressing this methodological need include: 

• Incorporating macroscopic measures of traffic operations throughout the year into an 

analysis of average annual crash frequency. This might include, for example, effects of 

annual average hourly flow, density, and V/C ratio during a defined hour on average 

annual crash frequency during that same hour (e.g., midnight – 1 a.m., 1 a.m. – 2 a.m., 2 

a.m. – 3 a.m.).

• Incorporating macroscopic measures of traffic operations during defined time intervals 

into an analysis of crash frequency or crash probability during those time periods and 

future time periods. This might include, for example, effects of average volume, average 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

5 

speed, standard deviation of volume, and standard deviation of speed on crash 

probability during the next 5, 10, 15, 20, 25, and 30 minutes from the time of the 

macroscopic traffic operations measurement.

Quantifying relationships between traffic operations and safety performance would also inform 

how to operate TSMO strategies from a safety performance perspective. This could include, for 

example, the most desirable operational conditions from a safety performance perspective 

under which to dynamically open (or close) a shoulder as a travel lane. This could also lead to 

methods to achieve desirable operational conditions from a safety performance perspective 

using dynamic lane assignment by increasing or decreasing the number of open travel lanes 

during different times of day.

Linking operational measures to safety performance is also related to the use of alternative 

measures of safety (i.e., safety surrogates). Numerous studies used surrogate safety measures 

to assess TSMO strategies. However, only a small portion attempted to validate the link 

between surrogate measures and the occurrence and/or severity of crashes.

STUDY DESIGN AND STATISTICAL ANALYSIS

A significant portion of published safety research on TSMO used naïve before-after or withwithout study designs and analyzed measures such as observed crash rates and observed crash 

frequencies. Methodological issues with these study designs and performance measures are well 

documented, including in the Highway Safety Manual, and they are continuing to be phased out 

as options for defensible safety analysis and evaluation. Study design and statistical analysis 

approaches within safety performance analysis research and practice continue to advance. Such 

advancements should be incorporated into future safety performance analyses of TSMO.

MECHANISTIC APPROACHES TO SAFETY PERFORMANCE ANALYSIS OF 

TSMO

This needs assessment did not identify any studies in the literature that used mechanistic 

approaches to analyze the safety performance of TSMO. The literature does, however, contain 

examples of such approaches in contexts that show potential for future application to TSMO. 

There is a need to advance research on the use and validation of mechanistic approaches to 

quantify the safety performance effects of TSMO.

Continuing to advance both strategy-specific knowledge and analysis capabilities related to 

safety performance of TSMO will provide agencies with new information for considering TSMO 

solutions for addressing system- and project-level needs within a performance-based decisionmaking framework. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

6 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

7 

CHAPTER 1— OVERVIEW

This report documents the results of a safety analysis needs assessment for Transportation 

Systems Management and Operations (TSMO). Conducting a needs assessment generally 

includes two types of activities: 

1. Characterizing the current state of practice, knowledge, and skills for the topic of 

interest.

2. Identifying gaps in the existing body of knowledge and corresponding needs, which then 

provide the foundation for future research activities and advancements in practice.

Readers of this report will gain an increased understanding of existing knowledge, analysis 

methods, and research needs in the context of quantifying the safety performance effects of 

TSMO. The primary intended audiences are those involved in developing transportation safetyrelated research needs and priorities. This audience spans staff at Federal, State, and local 

transportation agencies, private industry, and universities who develop research needs and 

priorities for their specific entities as well as for organizations and groups who fund shared 

research priorities, such as the American Association of State Highway and Transportation 

Officials (AASHTO) and the Transportation Pooled Fund (TPF) Program. This report will also 

be of interest to practitioners looking for a single reference that identifies and synthesizes safety 

information relevant to specific TSMO strategies (e.g., variable speed limits, adaptive signal 

control) as well as broader TSMO program areas (e.g., incident management, traffic 

management, demand management).

The remainder of this introductory chapter provides overviews of TSMO and safety 

performance analysis approaches. It also includes background information on the reliability of 

safety effectiveness evaluations, statistical significance, and “correlation versus causation” that 

will help readers put the findings of previous research in context. Chapters 2 through 5 

synthesize safety analysis information relevant to specific TSMO strategies and broader TSMO 

program areas. This information includes:

• Syntheses of strategy-specific safety performance knowledge and capabilities (Chapter 2).

• A synthesis of research on interrelationships between measures of traffic operational 

performance and safety performance (Chapter 3).

• A synthesis of research relating weather and weather-related road conditions to safety 

performance (Chapter 4). 

• A synthesis of research, tools, and challenges in estimating safety performance effects of 

TSMO that result from changes in travel choices and traffic demand patterns (Chapter 

5). 

Building on the findings of Chapter 2 through Chapter 5, Chapter 6 describes safety 

performance analysis gaps/limitations and needs. Chapter 7 provides a summary of the report 

along with key conclusions and recommendations.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

8 

TRANSPORTATION SYSTEMS MANAGEMENT AND OPERATIONS (TSMO)

Moving Ahead for Progress in the 21st Century (MAP-21) defines TSMO as an “integrated set of 

strategies to optimize the performance of existing infrastructure through the implementation of 

multimodal and intermodal, cross-jurisdictional systems, services, and projects designed to 

preserve capacity and improve security, safety, and reliability of the transportation system”

(MAP-21 §1103 (a) (30) (A)). The scope of TSMO strategies spans intelligent transportation 

systems (ITS), traffic operations and management, travel demand management, planning, and 

policy development. TSMO is undertaken from a systems perspective, which means that related 

TSMO strategies are coordinated with each other and across multiple jurisdictions, agencies, 

and modes. TSMO includes both efforts to operate the multimodal transportation system and 

activities to manage travel demand.(1) 

A single universally-accepted typology for the range of possible strategies that fall under the 

broad umbrella of TSMO does not currently exist. In addition, the growth of emerging mobility 

services (e.g. Shared Use Mobility) and new data sources for systems management and traveler 

information lead to new and continually-evolving TSMO applications. The Federal Highway 

Administration (FHWA) Office of Operations provides several resources with suggestions for 

how these TSMO strategies might be categorized.(2,3,4) This report adopts a framework from a 

recently completed FHWA Office of Operations task order on incorporating travel time 

reliability into transportation systems management. The framework, shown in figure 1, organizes 

TSMO strategies into three overarching categories: 1) incident/event management, 2) traffic 

management, and 3) demand management. Within each of these broader categories are more 

specific tactical program areas (e.g., road weather management, active traffic management, 

parking management). Each tactical program area then contains a list of specific actions in the 

form of implementing individual TSMO strategies to contribute to achieving a performance 

objective (e.g., implement variable speed limits, adopt ramp metering, deploy managed lanes). 

Within this framework, specific TSMO strategies can be implemented in different contexts. For 

instance, implementing variable speed limits supports six different tactical program areas: road 

weather management, traffic incident & emergency transportation operations, work zone 

management, planned special event management, freeway management, and active traffic 

management. Strategies can also be implemented in combination (e.g., part-time shoulder use

and dynamic lane use controls) in support of one or more tactical program areas.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

9 

Source: FHWA

Figure 1. Graphic. Framework for organizing TSMO strategies.

Link to text version of Figure 1
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

10

TSMO offers agencies a wide range of potential strategies for addressing network and projectlevel performance needs with cost-effective, tailored strategies. State and local agencies are 

increasingly recognizing TSMO as a core business area in support of maximizing the 

performance of their existing transportation infrastructure and making better use of resources. 

Some regions in the United States have found it useful to develop TSMO plans to define a 

common vision for TSMO in the region, develop performance objectives to guide the selection 

of TSMO strategies, and identify performance measures that will enable a region to track 

progress towards their objectives.(5) TSMO plans also identify potential TSMO projects, 

services, and implementation policies to reach the objectives. Building on these concepts, some 

State Departments of Transportation (DOTs) have expressed goals to shift the development of 

their Statewide Transportation Improvement Program to first funding TSMO solutions to 

address project needs when possible prior to funding road widening projects or other 

significant changes to the highway infrastructure. Increasingly, interest in TSMO strategies and 

their performance effects is spanning multiple business units within DOTs, including those units 

involved in the management and analysis of safety performance.

SAFETY PERFORMANCE ANALYSIS 

The number, severity, and type of crashes define the safety performance of a highway segment, 

intersection, facility, corridor, subarea, or network. As a step towards achieving safety 

performance-based planning, programming, project implementation, operations, and 

maintenance, FHWA published the National Performance Management Measures: Highway 

Safety Improvement Program Final Rule (Safety PM Final Rule) in the Federal Register on March 

15, 2016, with an effective date of April 14, 2016. The Safety PM Final Rule requires States and 

Metropolitan Planning Organizations (MPOs) to set annual safety performance targets for the 

following measures: 

• Number of fatalities.

• Rate of fatalities per 100 million vehicle miles traveled (VMT).

• Number of serious injuries.

• Rate of serious injuries per 100 million VMT.

• Number of non-motorized fatalities and number of non-motorized serious injuries

combined.

The Safety PM Final Rule emphasizes fatalities and serious injuries in defining safety 

performance. While the number of less severe crashes are often a part of a safety performance 

analysis and could provide some hints about the potential of future fatal and serious injury 

crashes to occur, a larger portion of their immediate costs to society is derived from their 

effects on property, travel time reliability, delay, and traffic management resources (e.g., traffic 

incident management and response). As much as possible, reference to safety performance 

effects of TSMO must clearly distinguish the types and severities of crashes that are affected. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

11

The amount of information available to quantify the safety performance effects of policies, 

programs, and project-level decisions has grown substantially over the last decade. AASHTO 

published the First Edition of the Highway Safety Manual (HSM) in 2010. Development of the 

Second Edition is underway. The HSM includes methods for quantifying safety performance to 

facilitate performance-based decision-making during planning, programming, project 

implementation, operations, and maintenance. Various stakeholder groups, including AASHTO, 

FHWA, State DOTs, and Transportation Research Board (TRB) standing technical committees, 

have cooperated in efforts to institutionalize the HSM and its associated analytical tools to 

support data-driven decisions, inform trade-off decisions, advance knowledge on factors 

influencing safety performance, and ultimately reduce fatalities and serious injuries.

The objective of a safety performance analysis of a TSMO strategy (or combination of 

strategies) is to determine how implementing the strategy (or combination of strategies) will 

affect, or has affected, safety performance. There are three general sets of approaches for 

analyzing effects on safety performance:

1. Approaches based on analysis of crash data.

2. Approaches based on alternative (i.e., surrogate) measures of safety.

3. Approaches based on simulating crash occurrence and severity.

Approaches Based on Analysis of Crash Data 

One way to determine how the implementation of a TSMO strategy affects safety performance 

is to analyze the frequency, type, and severity of crashes at locations (i.e., segments, 

intersections, facilities, corridors) before and after implementation of the strategy. Alternatively, 

one can compare the frequency, type, and severity of crashes at locations where the strategy is

present to similar types of locations without the strategy. FHWA’s A Guide to Developing Quality

Crash Modification Factors describes how to conduct before-after and cross-sectional studies to

determine the safety performance effects of strategies based on an analysis of crash data.(6)

Information derived from before-after and cross-sectional studies can also be used to develop

methods to forecast (i.e., predict) the frequency, type, and severity of crashes for existing or

new facilities; under current or forecasted traffic conditions; and for different design, 

operational, or management alternatives. 

Most safety performance analysis approaches based on an analysis of crash data, including those 

in the HSM, predict or estimate an average annual crash frequency assuming relatively static

conditions over the course of a year. There are significant limitations to these types of

approaches when analyzing the safety performance effects of TSMO. These limitations include 

the following:
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

12

• Some TSMO strategies are only operational during certain times of the day and have

operational characteristics that vary in real-time as a function of traffic and weather

conditions. Most methods based on an analysis of crash data are applied at the annual

level and do not incorporate daily, hourly, or sub-hourly variations in the operational

environment.

• TSMO strategies that are applied to support the attainment of traffic operational

objectives (e.g., improve travel time) have significant direct effects on traffic operational

performance metrics, such as speed, flow, and capacity. Most methods based on an

analysis of crash data incorporate average traffic volumes – specifically the annual

average daily traffic (AADT) – and do not consider safety performance effects of the

magnitudes of and variations in speed, flow, capacity, and density changes and

interactions.

• Most methods based on an analysis of crash data involve statistical analysis and require

an adequate number of locations with the strategies of interest to obtain statistically

significant results. Specific types and combinations of road design and TSMO strategies

are still, in some cases, relatively rare, making it difficult to quantify the anticipated safety

performance of these strategies for defined sets of conditions.

• Some TSMO strategies are intended to influence traveler behavior within a region and

across a transportation system, including trip timing, mode choice, route choice,

destination, or whether to make the trip. Although becoming more widely used through

dynamic traffic assignment methods, data and models to describe these behavior

changes as a function of time may not be readily integrable with existing safety

performance analysis approaches.

While not as common, there are examples of analyzing crash data at a “sub-annual” level. In 

some cases, the analysis period is as small as 5 minutes. Such examples include studies that are

focused on the relationship between safety performance and traffic metrics in real-time. These 

studies develop models for predicting the probability of a crash occurring for a given set of 

traffic conditions. Databases for model development are created by merging crash data,

roadway data, and high-resolution traffic operations data acquired from various detection 

systems. 

Approaches Based on Surrogate Measures of Safety 

Surrogate measures of safety such as speed, speed variation, traffic control compliance, 

deceleration rate, and lane change behavior can, in theory, be used to determine how the 

implementation of a TSMO strategy affects safety performance. The use of surrogate measures 

to estimate the safety performance effects of different strategies is identified as one possible 

study approach in FHWA’s A Guide to Developing Quality Crash Modification Factors.

(6) Tarko et al. 

pointed out that two conditions must be met before a surrogate measure of safety can be 

useful in a safety performance application:(9)

1. The surrogate measure should be based on an observable non-crash event that is

related to crashes.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

13

2. There should be a practical method for converting the changes or differences in noncrash events into corresponding changes or differences in crash frequency and/or crash

severity.

In other words, the key to the application of surrogate measures of safety is the availability of

reliable models or procedures to relate crash frequency (by crash type and severity), or

changes in crash frequency, to the surrogate measures, or changes in the surrogate measures.

Establishing links between surrogate measures and crashes has been a challenging area of

research. While the use of surrogate measures and related tools such as FHWA’s Surrogate 

Safety Assessment Model (SSAM) has become increasingly popular in research studies, they

have not gained traction and remain unproven in informing programmatic or project-level 

decision-making in practice. However, there are increasing opportunities for progress with the 

availability of emerging technologies and new data sources. National Cooperative Highway

Research Program (NCHRP) project 17-86, Estimating Effectiveness of Safety Treatments in the

Absence of Crash Data, is developing a procedural guide for using surrogate measures of safety. 

Readers of Chapter 2 (strategy-specific evaluations) should use caution in applying the results 

that come from studies applying surrogate measures of safety that have not been fully vetted 

and validated. Chapter 6 of this report includes needs related to the use of surrogate measures 

of safety in the context of TSMO safety evaluations. 

Approaches Based on Simulating Crash Occurrence and Severity 

The third and currently least common approach to analyzing safety performance is through 

simulating crashes based on models and mechanisms that characterize how drivers, vehicles, 

and the road environment interact. Transportation Research Circular E-C179: Theory, Explanation, 

and Prediction in Road Safety describes this process in three general steps:(11)

1. Identify the relevant mechanisms for the occurrence and severity of a specific crash

type.

2. Quantify the causal effects of different driver, vehicle, and road characteristics on each

mechanism.

3. Aggregate the frequencies of the mechanisms for the location of interest with defined or

assumed driver, vehicle, and road characteristics.

Transportation Research Circular E-C179 notes that a benefit of using such a mechanistic approach 

is that prior or new knowledge of the underlying mechanisms can guide study design, model 

development, analysis, and interpretation.

Two examples of approaches based on simulating crash occurrence and severity through a 

mechanistic approach include the conditional encroachment-crash-severity approach of the 

Roadside Safety Analysis Program version 3 (RSAPv3) and Wang et al.’s occurrence-mechanism 

approach for estimating rear-end crash probability. RSAPv3 uses encroachment models,
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

14

databases of vehicle trajectories, and models of crashes with roadside hardware and obstacles

to predict run-off-road crash occurrence and severity based on characteristics of the roadway

and roadside. Wang et al. focused on rear-end crashes at signalized intersections and developed

a model to predict their occurrence based on lead vehicle deceleration and following vehicle

response time.(12)

This needs assessment will summarize existing knowledge, analysis methods, and research

needs for quantifying the safety performance effects of TSMO using “annual” and “sub-annual” 

analyses of crash data, surrogate measures of safety, and mechanistic approaches to simulate 

crashes. 

The remainder of this chapter includes background information on three topics that will help 

the reader interpret the study results presented in Chapters 2 through 4: 1) reliability of safety 

effectiveness evaluations, 2) statistical significance, and 3) correlations vs. causation. 

Reliability of Safety Effectiveness Evaluations 

The syntheses in Chapter 2 (strategy-specific evaluations), Chapter 3 (interrelationships of 

traffic operational and safety performance), and Chapter 4 (weather effects on safety 

performance) will show that a majority of prior TSMO-related safety research is based on

analyses of the rate, number, type, and severity of crashes at locations of interest. A significant 

amount of this prior TSMO-related safety research also used more traditional study design, 

analysis, and evaluation methods that are more susceptible to bias and may result in less reliable 

findings. One set of common examples are before-after or with-without analyses of observed 

numbers of crashes, which are susceptible to regression-to-the-mean (RTM) bias and other 

biases due to confounding factors that are not addressed in the study design or analysis 

approach (e.g., temporal trends, inherent differences in sites with and without the treatment). 

Crash data are particularly susceptible to RTM bias. The random nature of crash occurrence 

results in the potential for an unusually high or unusually low number of crashes to occur at a 

site in any given year. Another set of examples common to the TSMO literature are beforeafter or with-without analyses of observed crash rates, which assume a linear relationship

between the number of crashes and traffic volume. Research studies have repeatedly shown 

that the relationship between the number of crashes and traffic volume is nonlinear. 

Comparisons of crash rates can therefore lead to incorrect conclusions on the effects of a 

TSMO strategy. Observed crash rates are also susceptible to RTM bias. 

More reliable methods are those that account for potential bias due to RTM, changes in traffic 

volume, the nonlinear relationship between number of crashes and traffic volume, and general 

temporal and other unknown or unobserved effects. One such technique is a before-after study 

utilizing an Empirical Bayes (EB) approach. The before-after study with EB approach is more 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

15

robust than a naïve before-after study because it uses what is known about the safety 

performance of other sites with similar characteristics to the study site (i.e., reference sites) in

addition to the crash history of the study site itself.(7) Through the use of reference sites, the EB 

method addresses bias due to RTM, accounts for changes in traffic volume, and accounts for

temporal effects. 

Readers of Chapters 2, 3, and 4 should use caution in applying the results that come from 

studies applying the traditional methods, such as the use of observed number crashes or 

observed crash rates. The results from these methods that do not properly account for 

potential sources of bias are less reliable and may result in less effective decisions. The reliability

of the results from these traditional methods are even lower if based on only a few study sites

and a few years of data. Chapter 6 of this report includes needs related to study design and 

analysis approaches in the context of TSMO safety evaluations. FHWA’s Reliability of Safety

Management Methods: Safety Effectiveness Evaluations provides additional information on more 

reliable methods and demonstrates through examples the value of more reliable methods in

safety effectiveness evaluation.(7)

Statistical Significance 

The content of the needs assessment regularly refers to standard errors, confidence levels, and

statistical significance in presenting the results of safety performance evaluations of TSMO

strategies. It is important to have some understanding of these concepts to gauge the potential 

effectiveness of the strategies and compare different TSMO strategies and different evaluation 

techniques. 

The standard error provides a measure of certainty for a given result (e.g., a measure of 

certainty in the magnitude of an estimated increase or decrease in crash frequency due to the 

implementation of a TSMO strategy). A small standard error (compared to the magnitude of 

the estimated increase or decrease) indicates a higher level of certainty in the result. A larger

standard error (compared to the magnitude of the estimated increase or decrease) indicates a

lower level of certainty in the result. 

The standard error can be used by an analyst to calculate the confidence interval, another

measure of certainty in an estimate. The confidence interval is a range of values between which

the true value of the estimated parameter is expected to fall. The wider this interval is, the less

certain the results may be. Additionally, confidence intervals can be wider or narrower given

the level of confidence one wants of the true value falling within that range. A 99-percent 

confidence interval is wider than a 95-percent confidence interval, which is in turn wider than a 

90-percent confidence interval. As an example, a 95-percent confidence interval is translated

for practical interpretation to indicate that the data shows a 95-percent chance that the true

value of the estimated parameter is within that interval.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

16

In a related manner, the confidence level associated with the statistical significance of a result 

provides an indication that the effect estimated from the sample of data in hand (e.g., an 

increase or decrease in crash frequency) will be present in other similar samples of data. The 

following types of statements are found throughout this document when reporting statistical 

significance and associated confidence levels: “The result was/was not statistically significant at a 

95 percent confidence level.” Such a statement generally means the following:

1. The result provides an estimate of the effect of a TSMO strategy on the number

and/or severity of crashes (e.g., results showed an average crash frequency reduction

of 29-percent (standard error of 5-percent)).

2. The study implemented null hypothesis significance testing, where the null hypothesis is

that the TSMO strategy has no effect on the safety performance measure in question.

3. The estimated p-value, the estimated probability that the researchers would have

observed the effect in their data (or a greater effect) under the null hypothesis is less

than or equal to 0.05 (for a 95-percent confidence level), 0.10 (for a 90-percent

confidence level), etc.

A body of literature exists regarding the use and misuse of statistical significance and p-values in 

various fields, related particularly to the ability (or inability) to independently reproduce 

research findings. Hauer demonstrated the misapplication of hypothesis testing in road safety 

literature using three case studies.(13) The American Statistical Association (ASA) released a 

“Statement on Statistical Significance and P-Values” with six principles underlying their proper 

use and interpretation:(14) 

1. “P-values can indicate how incompatible the data are with a specified statistical model.

2. P-values do not measure the probability that the studied hypothesis is true, or the

probability that the data were produced by random chance alone.

3. Scientific conclusions and business or policy decisions should not be based only on

whether a p-value passes a specific threshold.

4. Proper inference requires full reporting and transparency.

5. A p-value does not measure the size of an effect, or the importance of a result.

6. By itself, a p-value does not provide a good measure of evidence regarding a model or

hypothesis.”

The road safety community continues to advance approaches and research protocols that 

advance reliable study design, statistical analysis, and reporting practices. For the purposes of 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

17

the syntheses of specific TSMO strategies in Chapter 2, the following information is provided 

for key results if it could be determined from the papers:

• The estimate of the “effect” of the TSMO strategy on the number, type, and/or severity

of crashes along with the standard error of the estimate.

• Whether the result was statistically significant at a 95 percent confidence level.

• If the finding was not statistically significant at a 95 percent confidence level:

o The level of statistical significance if the confidence level is greater than or equal

to 80 percent (i.e., 90, 80).

o A statement that the finding is not statistically significant if the confidence level is

less than 80 percent.

Correlation versus Causation 

The syntheses in Chapter 2 through Chapter 4 present the findings of research that generally

sought to determine what change in safety performance (if any) was caused by some treatment

(e.g., TSMO strategy, weather condition, traffic condition). The question of determining causeand-effect in an observational study, which is the primary study type for safety performance 

research, is a broad, complex, and active area of research. Most readers may be familiar with a

more commonly used phrase that “correlation does not imply causation,” meaning that two 

events occurring together does not always mean there is a cause-and-effect relationship. As one 

hypothetical example, a sample of freeway facilities may experience fewer crashes when parttime shoulder use is operational. This outcome, however, does not automatically mean that 

part-time shoulder use is the cause of fewer crashes during the time periods it is operational. 

There could be other factors that are reducing the numbers of crashes that tend to be present 

at the same time part-time shoulder use is operational, and that are not accounted for in the 

study design (e.g., a certain vehicle mix, driver familiarity and mix, fewer entering and exiting

vehicles).

The syntheses in Chapter 2 through Chapter 4 do not directly address the assumption of 

causation for each set of findings. The discussions and summaries in these chapters do, 

however, touch on study characteristics that influence the chances that results are evidence of 

a cause-and-effect relationship. These characteristics include strength of the study design and 

analysis approach, strength of the association, and consistency of findings across multiple, welldesigned studies. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

18
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

19

CHAPTER 2 — SAFETY PERFORMANCE OF SPECIFIC TSMO 

STRATEGIES: CURRENT STATE OF PRACTICE, KNOWLEDGE, 

AND SKILLS

This chapter contains syntheses of TSMO strategy-specific safety performance evaluations and 

analysis capabilities. The syntheses are intended to serve multiple purposes. They characterize 

the current state of safety performance information for individual TSMO strategies, setting the 

stage for the identification of strategy-specific needs within a performance-based decisionmaking framework. They also provide a quick reference for practitioners interested in what 

safety performance research exists for specific strategies. A significant number of references in 

these syntheses are from research-oriented/academic journals that are often not readily 

available to the practitioner community. Summaries contained in this document attempt to 

make the key findings of these studies more readily accessible to readers. Such information and 

their source studies could be used to inform TSMO-related safety performance questions until 

TSMO gaps in more widely referenced resources, such as the HSM and FHWA Crash 

Modification Factors (CMF) Clearinghouse, are addressed.

The next section of this chapter describes the process used by the project team to 

systematically search for and identify relevant resources. This is then followed by syntheses for 

15 individual TSMO strategies (see table 1). Each synthesis contains two parts: 1) a general 

description of the strategy and 2) a summary of safety evaluations. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

20

Table 1. TSMO strategies covered in Chapter 2.

TSMO Strategy Page Number

Managed Lanes 22

Part-Time Shoulder Use 40

Reversible Lanes 47

Dynamic Lane Use Control 48

Dynamic Junction Control 49

Ramp Metering 51

Variable Speed Limits 56

Traffic Signal Coordination 63

Adaptive Signal Control Technology 69

Transit Signal Priority 73

Truck Signal Priority 77

Queue Jump Lanes 77

Safety Warning Applications 79

Work Zone Management and Temporary 

Traffic Control Applications

90

Traffic Incident Management Strategies 96

IDENTIFICATION OF SAFETY RESOURCES

To foster thoroughness and consistency, the project team established a systematic process to 

identify resources relevant to safety performance analyses of individual TSMO strategies. The 

project team used four primary search engines and databases: the ITS Benefits Database, the 

Transport Research International Database (TRID), the CMF Clearinghouse, and Google 

Scholar. Since TRID and Google Scholar contain information on a wide variety of topics not 

limited to safety, they were queried using the name of the strategy in question as well as the 

keywords “safety” and “crash.” 

The ITS Benefits Database was developed by the Intelligent Transportation Systems Joint 

Program Office (ITS JPO) of the United States Department of Transportation (USDOT). Its 

major objectives are to collect and present findings from evaluations of ITS deployments, 

specifically with respect to ITS effects on safety, mobility, efficiency, productivity, energy, 

environmental effects, and customer satisfaction.(15) Each result from a search presents a 

headline, such as the following result of a search for “variable speed limit” and “safety”: 

“Variable Speed Limits (VSL) cut crash rates by more than half during low visibility on I-77 in 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

21

Virginia.”(16) Each result also includes a concise summary of the context for the stated benefit 

and sometimes includes a link to the original source of the information.

The TRID database was released in 2011 as a partnership between TRB and the International 

Transport Research Documentation (ITRD) database.(17) It is maintained by TRB with 

sponsorship by State DOTs, various USDOT administrations, and other parties. It contains 

nearly 1.2 million references to books, technical reports, conference proceedings, and peerreviewed journal articles related to transportation research.(18)

The FHWA CMF Clearinghouse is a central repository for CMFs. A CMF is an estimate of the 

change in crashes that is expected to occur following the implementation of a chosen 

countermeasure or strategy.(19) For example, the FHWA CMF Clearinghouse contains a CMF of 

0.79 (with a standard error of 0.05) for “install adaptive traffic signal control” for all 

intersection crash types and crash severities (CMF ID 6858). To illustrate the application of the 

CMF, assume an average of 12.3 crashes per year (of all types and severities) at a given 

intersection without adaptive signal control. If adaptive signal control was implemented at the 

intersection, the average number of crashes (of all types and severities) expected to occur after 

implementation would be 12.3 * 0.79 = 9.72 crashes per year. The standard error can be used 

to estimate a confidence interval for the CMF. In this case, the 95-percent confidence interval 

for the “install adaptive traffic signal control” CMF would be 0.69 to 0.89. This range is 

calculated as 0.79 +/- 2*0.05. 

The CMF Clearinghouse is sponsored by the FHWA Office of Safety, and only includes CMFs 

produced from crash-based studies. A strategy may have multiple CMFs corresponding to 

specific crash types, road types, or crash severities. In addition to the information about the 

CMF itself, each entry contains the abstract of the study from which it was derived and a link to 

the paper or report if it is publicly available.(20)

Google Scholar is a search engine for scholarly literature that covers a wide array of subjects 

and formats. It includes peer-reviewed journals, conference proceedings, books, technical 

reports, and academic reports such as theses and dissertations. Given that Google Scholar is a 

general search engine, and not transportation-focused, even the most specific query will often 

provide tens of thousands of results. However, the results are ranked according to an index 

that takes into account the full text of each document, where it was published, and how 

frequently and recently it has been cited in other works.(21)

For the purposes of data collection 

for this needs assessment, the project team considered the first 10 pages of Google Scholar 

results.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

22

INDIVIDUAL STRATEGY SYNTHESES 

Managed Lanes 

General Description of Strategy: Managed lanes are highway facilities or sets of lanes on 

highway facilities where operational strategies are proactively implemented and managed in 

response to changing traffic conditions.(22) The spectrum of strategies that fall within the

definition of managed lanes includes high-occupancy vehicle (HOV) lanes, high-occupancy toll

(HOT) lanes, dynamic shoulder lanes, express lanes, and truck lanes. There are three forms of

lane management – pricing, vehicle eligibility restrictions, and access control. These facilities

range in complexity depending on the type of active management that is implemented (see 

figure 2).

Source: FHWA

Figure 2. Graphic. Typical U.S. managed lane facilities and applications.(23)

Managed lanes are implemented for a variety of reasons; however, the underlying goal is 

generally to reduce congestion and improve traffic flow and travel times. Their design and 

management can be flexible, allowing them to accommodate changes in demand patterns and 

operational needs over time. Some managed lanes that implement vehicle eligibility strategies, 

or a combination of vehicle eligibility and access control strategies, seek to reduce private 

automobile demand by providing transit preferential treatment (in the case of transitways or 

busways) or incentive travelers to increase vehicle occupancy (in the case of HOV lanes). 

Other managed lanes that implement vehicle eligibility/restrictions (e.g., truck 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

23

facilities/restrictions) seek to improve vehicle flow and/or safety performance by separating 

vehicles with different characteristics. Managed lanes that implement pricing strategies seek to 

improve vehicle flow for a subset of vehicles by attracting some drivers from the generalpurpose lanes to the managed lanes and charging them for the higher-quality service. 

The road geometric considerations for implementing managed lanes are significant, largely 

because managed lanes have generally been implemented on existing urban facilities (though 

recently, managed lanes have increasingly been built as added capacity due to the popularity of 

priced lanes). In some cases, managed lanes are added by reducing the widths of other roadway 

elements, as opposed to overall roadway widening. This can have implications for lane width, 

shoulder width or provision of shoulders, and the type of buffer used. The type of separation 

between the managed lanes and general-purpose lanes is a key design decision and influences 

the extent and complexity of other design decisions, which include cross section, alignment, and 

access type and location. The separation ranges from not physically separated (e.g., designated 

using pavement markings) to fully separated facilities that may only be accessed from the 

general-purpose lanes at specific locations. Figure 3 shows an example of a fully separated 

managed lanes facility. 

 Source: FHWA

Figure 3. Photo. Separated express lanes with access points on I-495 in Fairfax 

County, VA.(24)

Safety Performance Evaluations: Researchers conducted a number of safety assessments of 

managed lanes during early stages of experience with the strategy.(25,26,27,28,29) Some of these early

studies characterized general overall changes in safety performance resulting from 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

24

implementation of managed lanes projects.(25,26,29) The early studies also provided initial insights

to some factors influencing safety performance of facilities with managed lanes, including

patterns of traffic and congestion, speed differentials between managed lanes and generalpurpose lanes, access design, and widths of buffers between the managed lanes and generalpurpose lanes. While these early studies had limitations with respect to sample sizes and

analysis approaches, they set the stage for more recent research efforts. 

HOV Implementation

Bauer et al. explored the issue of limited urban right-of-way in the context of the crosssectional reallocation of widths.(30) The study was an observational before-after evaluation using

the empirical Bayes (EB) method. It examined the safety performance of various directional 

urban freeway segments that had undergone four-to-five lane or five-to-six lane conversions 

through narrowing existing lanes and/or converting shoulders to full or partial travel lanes. The 

study sites comprised nearly 50 miles of urban freeway on four different routes in southern 

California, and, in most cases, the conversions were performed to implement managed lanes. 

The results showed that four-to-five lane conversions resulted in an increase in overall crash

frequency (all types and severities) of 10.96-percent (standard error of 2.88-percent). Fatal and

injury (KABC) crash frequency increased by 10.59-percent (standard error of 4.56-percent). 

These results were statistically significant at a 95-percent confidence level. The five-to-six lane 

conversions resulted in crash frequency increases of 3- to 7-percent for various crash severities

(with standard errors ranging from of 4.56- to 7.22-percent), but these results were not

statistically significant due to a limited sample size. The researchers noted the possibility that 

increasing the capacity at the treatment sites may cause bottlenecks to migrate downstream of

the treatment sites, potentially leading to an increase in crashes at the treatment site.

Cooner & Ranft explored the implementation of retrofitted buffer-separated HOV lanes in

Dallas, Texas, which were implemented by decreasing lane widths from 12 feet to 11 feet and 

converting the inside shoulder to a lane.(31) The study examined crash data from 1990 to 2000, 

and the HOV lanes were implemented in the mid-1990s. Results showed that injury crash rates 

increased by 56-percent on I-35E and 41-percent on I-635 following introduction of the HOV 

lanes. Fatal and suspected serious injury crash rates decreased following construction, while 

suspected minor injury and possible injury crash rates increased. The standard errors and 

confidence intervals of these results were not reported by the authors. Additionally, a

substantial increase in crash frequency was found in the general-purpose lane adjacent to the 

HOV lane when compared to the before condition, with unsubstantial changes in the crash rate 

for the other general-purpose lanes. The study considered “Lane 1” to be the left-most lane in

the before condition and the left-most general-purpose lane (adjacent to the HOV lane) in the 

after condition. I-35E showed a 153-percent increase in the number of crashes in Lane 1 and I635 showed a 188-percent increase in the number of crashes in Lane 1 following the 

implementation of the HOV lane. Once again, standard errors and confidence intervals for
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

25

these results were not reported. The increase in crash frequencies was primarily attributed to

speed differentials between the HOV and general-purpose lanes. 

HOV to HOT Conversions

Cao et al. conducted a before-after study of converting HOV lanes to HOT lanes on I-394 in 

Minnesota.(32) The authors used the before-after with EB study design and analysis approach 

with four years of before data and two years of after data. Results showed a 5.3-percent 

reduction in total crash frequency because of the conversion (standard error of 3.99-percent). 

The result was not statistically significant at a 95-percent confidence level. It was significant at

an 80-percent confidence level. 

Abuzwidah & Abdel-Aty conducted a similar study on 16 miles of I-95 in Miami, Florida which

found that the HOV-to-HOT conversion did not significantly change the overall crash

frequency. The researchers conducted a before-after with EB analysis of the whole roadway

section, as well as separate analyses of the HOT lanes only and the general-purpose lanes only. 

The number of fatal and injury crashes (of all types) on the whole roadway segment decreased 

by 4-percent (standard error of 10-percent). The results were not statistically significant. The 

analysis of the HOT lanes showed that the number of fatal and injury crashes decreased by 28-

percent (standard error of 12-percent). The number of lane change-related and rear-end 

crashes decreased by 38- and 39-percent (with standard errors of 7- and 10-percent). These 

results were all significant at a 95-percent confidence level. The analysis of the general-purpose 

lanes showed that rear-end and lane change-related crashes increased by 25- and 27-percent, 

and these results were significant at a 95-percent confidence level (with standard errors of 10- 

and 8-percent). Fatal and injury crashes in the general-purpose lanes increased by 8-percent 

(with a standard error of 5-percent). The change was not statistically significant at the 95-

percent confidence level. It was significant at the 80-percent confidence level. The crash

frequency reductions in the HOT lanes were attributed to lower traffic volume and more 

uniform flow compared to the general-purpose lanes and the previous HOV lanes.(33)

HOV/HOT Design Features

Jang et al. analyzed safety performance using crash rates and other descriptive statistics on 

extended stretches of eight freeway facilities with HOV lanes: four stretches with continuous

access and four stretches with limited access.(34) The continuous access HOV lanes operated as 

HOV lanes during peak hours (5-9am, 3-7pm) and operated as regular lanes outside of the peak 

periods. The study considered only the crashes during the peak periods. The continuous access 

facilities were all located in the Bay Area in northern California, while the limited access

facilities were all in Los Angeles in southern California. This may limit the comparability of the 

results. While the analytical methods used in the study had some weaknesses, results from the 

analysis are still informative: 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

26

• In limited access HOV lanes, there were larger proportions of rear-end crashes and

smaller proportions of sideswipe crashes compared to continuous access lanes.

• Of all crashes that occurred along directional freeway segments with an HOV lane, a

larger proportion occurred in the HOV lane and leftmost general-purpose lane for

freeways with limited access HOV lanes than for freeways with continuous access

lanes.

• Fatal and injury crash rates and no apparent injury crash rates were higher in the

limited access HOV lanes than the continuous access HOV lanes. In the case of the

leftmost general-purpose lane adjacent to the HOV lane, the fatal and injury crash rates

were higher, and the no apparent injury crash rates were lower in lanes adjacent to

continuous access HOV lanes.

In a follow-on study, Jang & Chan used three separate statistical analysis techniques to validate 

their previous results comparing limited- and continuous-access HOV lanes.(35) The techniques 

used were empirical cumulative density functions, Kolmogorov-Smirnov Tests, and Poisson 

Distributed Samples comparison of means. The results showed that the statistical tests 

supported the previous findings that distinguished safety performance characteristics of 

continuous-access HOV facilities versus limited-access facilities. The study also examined crash 

rates versus distance to access points on the limited access HOV segments. The distance was 

defined as the mid-point of the segment to the last transition area upstream or first transition 

area downstream. The results showed crash rates to be higher closer to transition areas, with 

crash rates decreasing and leveling out as the distance increases. 

In another study, Jang et al. estimated four different count regression models for 1) number of 

no apparent injury crashes in the HOV lane, 2) number of fatal and injury crashes in the HOV 

lane, 3) number of no apparent injury crashes in the leftmost general-purpose lane, and 4) 

number of fatal and injury crashes in the leftmost general-purpose lane.(36) The authors 

estimated these four models using three years of crash, traffic, and roadway data from 

approximately 150 miles of freeways in Southern California with buffer-separated HOV lanes. 

The models estimated crash frequency as a function of HOV lane width, left lane width, lanespecific AADTs, left shoulder width, and buffer width. The model results showed that the 

numbers of no apparent injury and fatal and injury crashes in the HOV lane were associated 

with HOV lane width, average daily traffic volume in the HOV lane, left shoulder width, and 

buffer width. HOV lanes that were 11 ft or wider had fewer crashes than HOV lanes less than 

11 ft. The number of crashes in the HOV lane decreased as the left shoulder width increased. 

The authors found that both the number of no apparent injury and fatal and injury crashes in 

the HOV lane increased by 7-percent (with a standard error of 7-percent) for every foot 

increase in buffer width but this finding was not statistically significant.

The study also found that the total numbers of no apparent injury and fatal and injury crashes in 

the left general-purpose lane were positively associated with the average daily traffic in the left 

lane and the results were significant at a 95-percent level. The number of crashes in the left lane 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

27

decreased by 11-percent (with a standard error of 10-percent) for every foot increase in buffer 

width. This result was not statistically significant. Specification of average daily traffic volume in

the models deviated from generally accepted approaches to SPF development.

Fitzpatrick & Avelar investigated the relationship between crash frequencies and flush bufferseparated managed lane dimensions.(37) Their dataset included approximately 190 miles of nonweaving managed lane segments in California and Texas with a single managed lane that was

operational 24/7 and anywhere between three to five general-purpose lanes.(37) Based on the 

models, the researchers concluded that, for each additional foot of managed lane envelope (i.e., 

the sum of left shoulder width, lane width, and buffer width): the total number of crashes 

reduced by two to three percent (standard error of 0.5-percent), and the number of fatal and 

injury crashes decreased by 4.4 percent (standard error of 1.3-percent). Both results were 

significant at a 99-percent confidence level. Additional exploratory analysis indicated that buffers 

of three feet or less appeared to be associated with more crashes than buffers of four to six 

feet. The authors also concluded that changes in left shoulder width are not as influential on 

safety performance as changes in lane and buffer widths within the managed lane envelope.

Srinivasan et al. developed a crash prediction method for Florida DOT for freeways with HOV

or HOT lanes, using data from California, Florida, and Texas.(38) Models were developed

separately for six-, eight-, ten-, and twelve-lane freeways. The HOV lane sites included one 

managed lane in each direction, while the HOT lane sites included two managed lanes in each

direction. Ten-lane freeways with a two- to three-foot buffer were associated with a 12.4-

percent decrease in the number of fatal and injury crashes (with a standard error of 7.5-

percent) when compared to facilities with a painted stripe (no buffer) or a zero- to two-foot

buffer. This result was not statistically significant at a 95-percent confidence level (it was

significant at a 90-percent confidence level). Additionally, facilities with only a painted stripe (no

buffer) had more total crashes (all types and severities) than those with a zero- to two-foot or

two- to three-foot buffer. The effect of buffer type was not found to be statistically significant 

for six-, eight-, or twelve-lane freeways. Sample size issues may have contributed to the lack of

statistically significant results for the other models. The models for HOT lanes indicate that

facilities with a one-foot buffer have more crashes than those with a three-foot buffer, which in 

turn have more crashes than those with 20 feet of separation between the managed and 

general-purpose lanes. However, the separation width values were unique to each site, and thus

caution should be used in transferring these results, as other site-specific confounding factors 

could be present. 

Exclusive Truck Facilities and Truck Restrictions

Examining potential lane management strategies that separate types of traffic, Lord et al.

conducted an exploratory safety analysis of data from approximately eight miles of freeway

sections in New Jersey where the inner and outer lanes were divided and the inner lanes

restricted truck traffic (i.e., were for passenger cars only).(39) Results indicated that lanes

without trucks had fewer crashes and lower crash rates than lanes with both passenger cars
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

28

and trucks. Trucks were involved in 40-percent of crashes in the outer lanes, while making up 

30-percent of the traffic stream. The authors concluded that additional work was needed to

explore the contributing factors leading to truck-related crashes in the outer lanes with both

passenger cars and trucks.

Das et al. analyzed the effects of truck lane restrictions (TLRs) on safety performance at 16 

sites in the Dallas-Fort Worth region of Texas. Using a before-after EB evaluation, the study 

found that TLRs indicated safety performance improvements in terms of fatal and injury crashes 

and fatal and injury crashes involving large trucks.(40) However, the results of the before-after EB 

analysis showed that the crash frequency reductions for overall fatal and injury crashes and fatal 

and injury truck-involved crashes were only statistically significant at a 95-percent confidence 

level for 2 out of the 16 study sites (one additional site was significant at an 80-percent 

confidence level, and several sites showed statistically significant increases in crash frequency).

Fontaine et al. examined the safety effects of Virginia’s TLR policy, which prohibits trucks from 

traveling in the left-most lane of interstates with three or more lanes by direction when the 

speed limit is 65 mph or higher; along all of I-81; along all interstates in the Northern Virginia 

District of VDOT; and in the left lane of two-lane directional interstate segments when a 

truck’s speed is below the posted speed limit.(41) The study involved crash frequency analyses of 

23 sites with three directional lanes and 20 sites with two directional lanes. A before-after EB 

study approach was used on the sites with three directional lanes, but due to a lack of 

comparison sites the two-directional-lane sites were examined using a naïve analysis approach. 

Only one year of “after” data was available for the analysis.

For sites with three lanes in each direction, the researchers identified a bifurcation in the 

results at 10,000 vehicles per day per lane. Results for overall crashes (all severities and vehicle 

types) showed that lower-volume sites experienced a 10-percent decrease in crash frequency 

(with a standard error of 3-percent), and higher-volume sites experienced a 12-percent 

increase in crash frequency (with a standard error of 1.5-percent). Analysis of fatal and injury 

crashes showed that lower-volume sites experienced a 17-percent reduction in fatal and injury 

crash frequency for all vehicle types (with a standard error of 4.6-percent), while higher-volume 

sites experienced a 14-percent increase in fatal and injury crash frequency for all vehicle types 

(with a standard error of 2.6-percent). Finally, the researchers analyzed truck-involved crashes. 

These results showed that lower-volume sites experienced a 29-percent decrease in truckinvolved crash frequency for all crash severities (with a standard error of 5.1-percent), while 

higher-volume sites experienced a truck-involved crash frequency increase of 37-percent for all 

crash severities (with a standard error of 4.1-percent). Truck-involved fatal and injury crash 

frequency decreased by 34-percent (with a standard error of 4.1-percent) at lower-volume 

sites and increased by 27-percent (with a standard error 6.6-percent) at higher-volume sites. 

These results were all statistically significant at a 95-percent confidence level.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

29

The researchers performed a similar analysis for sites with two lanes in each direction. Results 

showed that overall crashes (all severities and vehicle types) experienced a 23-percent decrease 

in crash frequency (with a standard error of 10.2-percent). Analysis of fatal and injury crashes 

showed a 31-percent reduction in fatal and injury crash frequency for all vehicle types (with a 

standard error of 15.3-percent). The results for all truck-involved crashes showed a 7-percent 

decrease in truck-involved crash frequency (with a standard error of 21.9-percent). There was 

a 53-percent reduction in truck-involved fatal and injury crash frequency (with a standard error 

of 21.4-percent). These results were all statistically significant at a 95-percent confidence level 

except for the decrease in all truck-involved crashes, which was not statistically significant.

Since the original study of Virginia TLRs was limited to one year of “after” data, Fontaine et al. 

performed a follow-on study to further examine the safety performance effects.(42) Once again, 

the researchers examined sites separately based on number of directional lanes (two vs. three 

or more), using a before-after EB approach for sites with three or more directional lanes and a 

naïve approach for sites with two directional lanes. In this study, only higher volume three-lane 

or greater sites (greater than 10,000 vehicles per lane per day) were examined. The results for 

three directional lane sites showed that overall truck-involved crash frequency increased by 23-

percent (with a standard error of 5.6-percent). This result was statistically significant at a 95-

percent confidence level. Truck-involved fatal and injury crashes on three directional lane roads 

increased by 4-percent (with a standard error of 7.1-percent), but this result was not 

statistically significant. For two directional lane sites, the naïve before-after crash frequency 

analysis showed no statistically significant change.

Fontaine studied another TLR strategy in Virginia that prevents trucks traveling more than 15-

mph below the posted speed limit from using the left lane on four-lane freeways.(43) The 

strategy is generally implemented in mountainous terrain, and 19 study sites were identified in 

Virginia. The paper did not report the posted speed of the 19 sites. The study was unable to 

use the EB method due to a lack of appropriate reference sites, so a naïve before-after study of 

crash frequency was performed instead, using four years of before data and two years of after 

data. The results showed that the frequency of fatal and injury crashes for all vehicle types

declined by 32-percent (standard error of 11.7-percent). This result was statistically significant 

at a 95-percent confidence interval. However, the changes in the frequency of truck-involved 

crashes and truck-involved fatal and injury crashes were not statistically significant. The 

confidence intervals for the truck-involved crash analyses were wide due to the small sample 

size of crashes at the study sites. 

The implementation of managed lanes, particularly bus- or truck-only lanes and HOV or HOT 

lanes, can result in speed differentials between the managed lanes and the general-purpose 

lanes. Tao performed a safety surrogate analysis using Vissim microsimulation software and the 

FHWA Surrogate Safety Assessment Model (SSAM) to analyze various managed-lane scenarios 

in Montreal, Canada.(44) The scenarios included an exclusive bus/taxi arterial lane and a freeway 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

30

HOV and bus-on-shoulder configuration. The surrogate safety measures used in the study were 

estimated conflict angle, time to collision (TTC) and post-encroachment time (PET). TTC is the 

time required for two vehicles to collide if they were to continue on the same path without 

braking. PET is the time from the moment a vehicle departs a defined point to the moment the 

next vehicle arrives at that point. The study found that increasing the length of merge lanes and 

weaving sections (road segments that allow for vehicles to both merge into and out of a 

managed lane) can reduce the number of conflicts associated with high speed differentials and 

could be more effective than using physical separation methods for managed lanes.(44)

Adelakum performed a survey- and simulation-based study of the effects of potential interstate 

truck lane configurations on efficiency and safety in Tennessee.(45) The different scenarios 

included a) trucks must use the left lane, b) trucks must use the left two lanes, c) optional truck 

only lane (either concrete barrier or painted buffer divided), and d) trucks must use right two 

lanes (no-change scenario). The microsimulation portion, performed in Vissim, examined the 

rate of lane change maneuvers as a surrogate measure of safety. It showed that the smallest

lane change rates (79,648 lane changes per hour for cars; 482 for sport utility trucks; and 2,825 

for tractor-trailer vehicles) occurred under the optional, separated exclusive truck lane 

scenario. Furthermore, the highest lane change rates occurred under scenario (d) where trucks 

must use the right two lanes (137,975 lane changes per hour for cars; 3,259 for sport utility

trucks; and 28,533 for tractor-trailer vehicles). The survey portion of the study showed that, of 

the 500 truck drivers surveyed, the majority preferred moving trucks to the left lanes to avoid 

conflicts with vehicles merging onto or off the roadway.

El-Tantawy et al. also used surrogate safety measures to evaluate the safety performance of 

truck lane restrictions but cautioned that there isn’t an empirical link between crash 

occurrence and conflicts.(46) The study simulated an urban freeway segment in Toronto, Canada, 

and collected data to identify lane changing, merging, and rear-end conflicts. Four different 

scenarios were tested: no restriction, restricting trucks from the left-most lane, restricting 

trucks from the left-most two lanes, and dedicated left-most lane for trucks only. Each scenario 

was tested at three different levels of truck penetration (4-, 15-, and 30-percent). The 

simulation results indicate that truck lane restriction strategies reduced the simulated number 

of conflicts when truck percentage was at least 15-percent. Restricting trucks from the leftmost lane did not substantially affect lane changing conflict frequency. Restricting trucks from 

the left-most two lanes decreased truck-related lane changing conflicts by 26- to 53-percent, 

depending on truck percentage. The provision of a truck-only lane (in the left-most lane) 

decreased truck-related lane changing conflicts by 17- to 21-percent. These results were 

significant at a 95-percent confidence level (standard errors were not reported by the authors). 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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Exclusive Bus Facilities

Duduta et al. explored the safety effects of BRT design features using data from nine BRT 

systems around the world.(47) The study incorporated crash analysis, road safety audits, and 

interviews with transit officials. Due to differences in crash reporting standards between

countries, the researchers were unable to effectively compare results, but instead analyzed each

city as a separate case study. Three cities presented adequate data for negative binomial crash 

frequency modeling: Mexico City and Guadalajara, Mexico; and Porto Alegre, Brazil. Difference 

of means tests were used to perform before-after analysis for the other cities where statistical 

models could not be developed (Bogota, Colombia; Delhi, India; Curitiba and Belo Horizonte, 

Brazil). Over 90-percent of crashes on BRT corridors did not occur in bus lanes and did not 

involve buses. Counterflow bus lanes were significantly correlated with higher overall crash

rates. Additionally, center-lane bus systems tended to be associated with lower crash rates than 

curbside systems. The researchers identified notable tradeoffs between safety, capacity, and

pedestrian accessibility in the design of BRT systems and facilities. They pointed out that 

reduction of crashes and crash severity would require measures that would affect operational 

efficiency: slowing down bus operations and not allowing buses to merge in and out of express 

lanes at stations.

Goh et al. performed a safety surrogate study of bus lane effects for a 1.6 km corridor in 

Melbourne, Australia using microsimulation techniques.(48) The study compared a base case of 

mixed traffic (no change) with two bus lane configurations: (a) a curbside lane reallocated for

buses only and (b) a new additional curbside lane for buses only. TTC and deceleration rate to

avoid a crash (DRAC) were used as the surrogate measures of safety performance. The

researchers emphasized the fact that the study was limited to a single corridor and that results

are likely to be influenced by geometric and operational characteristics. The results indicated

that vehicle conflicts at intersection approaches and bus stops were lower in both bus lane 

configurations than in the base case (conflict reductions varied between 9.4 and 42.5 depending 

on traffic volume). For mixed traffic, in scenario (a) where traffic volumes were over 900

vehicles per hour (vph), vehicle conflicts increased on the corridor by 50.7 conflicts (1200 vhp), 

195.2 conflicts (1500 vph), and 503.7 conflicts (1800 vph); and in scenario (b) vehicle conflicts 

reduced by 36.3 conflicts (1200 vhp), 54.2 conflicts (1500 vph), and 199.3 conflicts (1800 vph).

These reductions, expressed as TTC, were all statistically significant at a 95-percent confidence 

level (standard errors were not reported by the authors). The differences between the two

scenarios became negligible at volumes lower than 900vph. 

Summary 

The managed lane synthesis effort uncovered safety performance research in four general areas: 

1) HOV/HOT conversions, 2) HOV/HOT design features, 3) exclusive truck facilities and truck

restrictions, and 4) exclusive bus facilities. Table 2, table 3, table 4, and table 5 summarize these
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studies. Of these categories, HOV/HOT lanes have been the most extensively researched from 

a safety performance perspective and there are potentially implementable findings with respect 

to the quantification of safety effects associated adding an HOV/HOT lane to an existing 

roadway by narrowing lane and shoulder widths and with tradeoffs associated with elements of 

the managed lane envelop (i.e., the left shoulder width, lane width, and buffer width). The 

changes in crash type and severity with different separation strategies, specifically within the 

managed lane and adjacent general-purpose lanes, have been researched but are not fully 

understood. Similarly, safety performance near managed lane access points has not been fully 

researched. Work is ongoing to fill some of these gaps and produce a single source of 

information for policymakers and practitioners interested in evaluating safety performance of 

HOV/HOT lanes. At the time of this report, NCHRP project 17-89A, HOV/HOT Freeway Crash 

Prediction Method for the Highway Safety Manual, was initiated.(49) The objectives of NCHRP 17-

89A, are to “1) develop a predictive methodology that can be used to estimate crash frequency 

and severity for freeway facilities, and associated ramps, with HOV or HOT lanes, and 2) 

provide a tool for highway agencies to quantify safety performance in planning, project 

development, and operation of freeways with HOV or HOT lanes.”

Exclusive truck facilities, truck restrictions, and exclusive bus facilities have been researched to 

a much lesser extent from a safety performance perspective, with safety research on exclusive 

bus facilities occurring outside of the United States. Research on truck facilities and truck 

restrictions uncovered some potentially informative results using both crash- and surrogatebased approaches, but there is a need to repeat these types of studies with more robust 

datasets and analysis approaches.

A selected number of the studies in this section are included in the CMF Clearinghouse. A 

search of the CMF Clearinghouse for managed lanes-related CMFs results in the following:

• Three- and four-star CMFs for “four to five lane conversion” and “five to six lane

conversion” (without widening the roadway, and in the context of managed lanes) based

on the work of Bauer et al (CMF IDs 4 – 9).(30)

• Two- and three-star CMFs for “widen managed lane envelopes” based on the work of

Fitzpatrick and Avelar (CMF IDs 9398 – 9400).(37)

• Two-star CMFs for converting continuous access HOV lanes to limited-access based on

Jang et al (CMF IDs 2114 and 2115).

(34)

• Two- and three-star CMFs for converting HOV lanes to HOT lanes based on the results

of Cao et al. and Abuzwidah & Abdel-Aty (CMF IDs 2988 – 2993, 8801 – 8818).(32,33)

• A range of one-star to four-star CMFs for truck lane restrictions based on the work of

Das et al. and Fontaine et al. (CMF IDs 9377 – 9380, 2720 – 2722, 2225 – 2228).(40,41)

The highest-rated CMFs are those by Fontaine et al. showing:

o Truck lane restrictions leading to reductions in the frequency of total (all types,

severities), fatal-plus-injury (all types), truck-related (all severities), and truckrelated fatal-plus-injury crashes when volumes are less than 10,000 vehicles per

day per lane.
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o Truck lane restrictions leading to increases in the frequency of these same crash

types when volumes are greater than 10,000 vehicles per day per lane.
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Table 2. Summary of safety performance evaluation findings for HOV/HOT 

conversions. 

Study Location Site type Method Findings

Bauer et al., 

2004(30)

Los Angeles, 

CA; San Diego, 

CA

Urban freeway 

with lane and 

shoulder width 

conversion to 

accommodate 

HOV lane

Before-after with 

EB 

Four-to-five lane conversion:

• Overall crash frequency

increase of 10.96% (SE

2.88%, significant at

95%)

• Fatal and injury crash

frequency increase of

10.59% (SE 4.56%,

significant at 95%)

Other conversions:

• Five-to-six lane

conversions did not

generate statistically

significant change in

crashes

Cooner & 

Ranft, 2006(31)

Dallas, TX Urban freeway 

with retrofitted 

HOV lanes

Naïve beforeafter

• Retrofitted bufferseparated HOV lanes

resulted in increases in

fatal and injury crash

rates, particularly for

non-incapacitating and

possible injury crashes

in the HOV lane and left

GP lane.

• Injury crash frequency

increase of 41% - 56%

following introduction

of HOV lanes

Cao et al., 

2012(32)

Minneapolis, 

MN

Urban freeway 

with HOV-toHOT 

conversion

Before-after with 

EB 

Overall crash frequency 

decrease of 5.3% for 

conversion of HOV to HOT 

(S.D. 3.99%, not significant 

at 95%)

Abuzwidah & 

Abdel-Aty, 

2016(33)

Miami, FL Urban freeway 

with HOV-toHOT 

conversion

Before-after with 

EB 

• Fatal and injury crash

frequency decrease of

4% (S.E. 10%, not

significant)

• Overall HOT lane crash

frequency decrease of

28% (S.E. 12%, significant

at 95%)

• Overall general-purpose

lane crash frequency

increase of 8% (S.E. 5%,

not significant at 95%)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

35

Table 3. Summary of safety performance evaluation findings for HOV/HOT design 

features.

Study Location Site type Method Findings

Jang et al., 

2009(34)

Contra Costa, 

CA; Alameda, 

CA; Santa 

Clara, CA; 

Angeles, CA

Urban freeway 

with limited- or 

continuousaccess HOV 

lane

Statistical 

hypothesis 

testing; crash

concentration 

location analysis

• Limited access HOV lanes

have larger proportions of

rear-end crashes and

smaller proportions of

sideswipe crashes

compared to continuous

access lanes.

• Larger proportion of

crashes occurred in the

HOV lane and leftmost

GP lane for freeways with

limited access HOV lanes

than for freeways with

continuous access lanes.

• Fatal and injury crash

rates and no apparent

injury crash rates were

higher in the limited

access HOV lanes than

the continuous access

HOV lanes.

Jang et al., 

2011(36)

Los Angeles, 

CA; Orange, 

CA

Urban freeway 

with HOV lane

Negative 

binomial 

regression

For each additional foot of 

buffer: 

• HOV crash frequency

increase of 7% (SE 7%,

not significant)

• Left GP lane crash

frequency decrease of

11% (SE 10%, not

significant).

Fitzpatrick & 

Avelar, 

2016(37)

Los Angeles, 

CA; Dallas, TX; 

Houston, TX

Urban freeway Poisson and 

negative 

binomial 

regression

For each foot of managed lane 

envelope width: 

• Decrease of 2-3% in

overall crash frequency

(S.E. 0.5%, significant at

99%)

• Decrease of 4.4% in fatal

and injury crash frequency

(S.E. 1.3%, significant at

99%)

Changes in left shoulder 

width are not as influential as 

changes in lane and buffer 

widths within the managed 

lane envelope.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

36

Study Location Site type Method Findings

Srinivasan et 

al., 2015(38)

CA; TX; WA; 

FL

Urban freeway 

with HOV or 

HOT lanes 

Negative 

binomial 

regression

• Increasing buffer width for

HOV and HOT lanes on

10-lane freeways resulted

in 12.4% decrease in fatal

and injury crashes and

overall crashes (SE 7.5%,

significant at 90%).

• Effect of buffer width not

significant for 6-, 8-, or

12-lane freeways.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

37

Table 4. Summary of safety performance evaluation findings for exclusive truck 

facilities and truck restrictions.

Study Location Site type Method Findings

Lord et al., 

2014(39)

Woodbridge 

Township, NJ

Urban freeway 

with Truck 

Lane 

Restrictions

Naïve crash 

analysis

• Lanes without trucks

had fewer crashes and

lower crash rates

• In mixed-traffic lanes,

trucks were 30% of the

traffic stream but

involved in 40% of

crashes

Das et al., 

2018(40)

Dallas-Ft. 

Worth, TX

Urban freeway 

with Truck 

Lane 

Restrictions

Before-after with 

EB 

TLRs produced slight 

decrease in fatal and injury 

truck-involved crashes. 

Results were only significant 

at 95% for 2 out of 16 study 

sites.

Fontaine et 

al., 2007(41)

VA Urban and rural 

freeway with 

Truck Lane 

Restrictions

Before-after with 

EB and naïve 

before-after

Truck-involved fatal and 

injury crash frequency:

• 3+ lane interstates with

volume >10,000

veh/day/lane increase of

27% (SE 6.6%, significant

at 95%)

• 3+ lane interstates with

lower volume decrease

of 34% (SE 4.1%,

significant at 95%)

• 2-lane interstates

decrease of 53% (SE

21.4%, significant at

95%)

Fontaine et 

al., 2009(42)

VA Urban and rural 

freeway with 

Truck Lane 

Restrictions

Before-after with 

EB and naïve 

before-after

Truck-involved crash 

frequency: 

• 3+ lane interstates with

volume > 10,000

veh/day/lane overall

increase of 23% (SE

5.6%, significant at 95%),

fatal and injury increase

of 4% (SE 7.1%, not

significant)

2-lane interstates did not

show significant effects.

Fontaine, 

2008(43)

VA Rural freeways 

with Truck 

Lane 

Restrictions

Naïve beforeafter

• Decrease of 32% in fatal

and injury crash

frequency (S.E. 11.7%,

significant at 95%)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

38

Study Location Site type Method Findings

• Change in truckinvolved crash frequency

not significant.

Tao, 2015(44) Montreal, 

Canada

Urban arterial 

and freeway

Simulation-based 

surrogate study

Increasing the length of 

merge lanes can alleviate 

safety effects of high speed 

differentials in managed lane 

configurations 

Adelakum, 

2008(45)

Knoxville, TN Urban freeway Simulation-based 

surrogate study

Lowest lane change rates 

occurred under scenario 

with separated truck-only 

lane

El-Tantawy et 

al., 2009(46)

Toronto, 

Canada

Urban freeway Simulation-based 

surrogate study

All results significant at 95%:

• Truck Lane Restrictions

result in safety benefits

when trucks make up at

least 15% of traffic

stream

• Restricting trucks from

left lane did not affect

overall conflict

frequency

• Decrease in overall

conflicts of 26-53%

when restricting trucks

from left two lanes

• Decrease in overall

conflicts of 17-21% with

provision of truck-only

lane
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

39

Table 5. Summary of safety performance evaluation findings for exclusive bus 

facilities.

Study Location Site type Method Findings

Duduta et al., 

2012(47)

Mexico City & 

Guadalajara, 

Mexico; Porto 

Alegre, Brazil

Arterial BRT 

corridor

Negative binomial 

regression and 

statistical 

hypothesis testing 

of differences in 

means

• 90% of crashes on BRT

corridors did not occur

in bus lanes or involve

buses

• Counterflow bus lanes

were correlated with

higher overall crash

rates

• Center-lane bus systems

tended to provide more

safety benefits than

curbside systems

Goh et al., 

2013(48)

Melbourne, 

Australia

Arterial with 

bus lane

Simulation-based 

surrogate study

• Presence of bus lanes

decreased vehicle

conflicts at intersections

and bus stops (significant

at 95% for volumes

greater than 900 vph)

• At low volumes, bus

lanes did not significantly

affect conflict

occurrence
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

40

Part-Time Shoulder Use 

General Description of Strategy: This strategy allows the shoulder to be used as a travel lane 

to provide additional capacity when needed. It preserves the shoulder for typical shoulder uses 

(e.g., refuge and/or recovery area) during most of the day. The converted shoulder, vehicle-use

options, operating options, and speed control characterize different part-time shoulder use 

alternatives, as listed below:(50)

• Converted Shoulder:

o Right shoulder.

o Left shoulder.

• Vehicle-Use Options:

o Open shoulder to transit vehicles only.

o Open shoulder as an HOV lane that permits carpools and transit vehicles to use

it.

o Open shoulder as a HOT lane that allows drivers to pay a toll to use it if their

vehicle does not meet HOV requirements.

o Open shoulder to all vehicles except trucks.

o Open shoulder to all vehicles.

o Open shoulder to slow-moving trucks in rural mountainous areas.

• Operating Options:

o Dynamically open shoulder when defined congestion thresholds are reached (an

ATM strategy referred to as “Dynamic Shoulder Use”).

o Statically open shoulder during specified peak periods (i.e., set time of day).

• Speed Control Options:

o Same speed limit as other lanes (at posted speed limits).

o Same speed as other lanes (at a reduced speed relative to normal posted speed

limits).

o Lower speed limit than other lanes.

Figure 4 and figure 5 depict two of these alternative forms of part-time shoulder use.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

41

 Source: FHWA

Figure 4. Photo. Previous implementation of static part-time shoulder use on I-66 

in Virginia.(50)

 

 Source: FHWA

Figure 5. Photo. Bus-on-shoulder operations in Minneapolis-St. Paul, Minnesota.(50)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

42

The strategy will have an effect at the freeway facility level, spanning multiple freeway segments 

and interchange ramps. It is possible that performance effects of part-time shoulder use may 

extend beyond the location where it is implemented because:

• The additional peak-period capacity may attract some drivers from parallel roadways.

• The removal of a bottleneck through part-time shoulder use could create another

bottleneck downstream of the part-time shoulder use location, which could in turn

affect performance at that location.

When the shoulder is in use, the strategy changes several geometric characteristics that may

affect performance, including remaining shoulder widths, lateral clearance to roadside objects, 

shoulder cross-slopes, stopping sight distances on curves, and design characteristics at entrance 

and exit ramp locations. 

Safety Performance Evaluations: FHWA’s Use of Freeway Shoulders for Travel – Guide for 

Planning, Evaluating, and Designing Part-Time Shoulder Use as a Traffic Management Strategy (PartTime Shoulder Use Guide) provided an overview of empirical studies and tools that quantify the 

safety performance effects of part-time shoulder use, noting that research in this area is

limited.(50) The overview first summarized an analysis of data before and after implementation of

priced dynamic part-time shoulder use on I-35W in Minneapolis. A before-after EB analysis of

the data indicated that expected crash frequencies had increased by 28.4-percent for all crash 

types and severities after implementation of the part-time shoulder use strategy (standard error 

and confidence level were not reported). However, the Minnesota Department of

Transportation (MnDOT) observed that a significant bottleneck upstream of the site had been 

removed at the same time part-time shoulder use was implemented. Traditional SPFs were not

able to adequately capture the effect of the bottleneck removal on expected crash frequency of 

this downstream, part-time shoulder use site. MnDOT followed up with an analysis using the 

Second Strategic Highway Research Program (SHRP2) L07 models. These models relate 

expected crash frequency to traffic density. MnDOT used these models to predict what would 

have been the expected crash frequency without part-time shoulder use at the location of

interest but with the upstream bottleneck removal. This follow-on analysis estimated that

removal of the upstream bottleneck resulted in a 22-percent increase in expected crash

frequency at the location with part-time shoulder use. Therefore, the increase in expected

crash frequency related to implementation of priced dynamic part-time shoulder use was 6.4 

percent when taking the removal of the upstream bottleneck into consideration. 

Lee et al. developed a model of expected crash frequency using data from a 6.5 mile-long

segment of I-66 in Virginia where, during peak hours, the left (i.e., inside) lanes served as HOV

lanes and the right shoulder served as an additional travel lane.(51) The authors explored the use 

of negative binomial regression models to capture the effects of the HOV and part-time 

shoulder use operations on daily crash frequencies. The use of three years of crash data from 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

43

only one location and the high level of crash count disaggregation (i.e., by day) led to modeling 

challenges, and almost none of the resulting independent variables were statistically significant, 

limiting the contribution of this study. 

A subsequent study by Dutta et al. further explored the effects of Active Traffic Management 

on I-66 in Virginia, which included part-time shoulder use as well as other traffic management 

strategies such as variable speed limits and lane-use control. The safety evaluation analysis used 

the before-after EB method, showing that locations with part-time shoulder use reduced the 

number of crashes (all types and severities) by 25- to 40-percent (with standard errors from 

6.5- to 11.3-percent). These results were significant at a 95-percent confidence level, while the 

other Active Traffic Management strategies did not result in any significant changes to crash 

frequency.(52)

Kononov et al. identified an inconsistency between the notion that decreasing congestion by 

implementing part-time shoulder use will result in some degree of improved safety performance 

and the idea that crash rates increase as the number of lanes increase. The researchers used 

neural networks to develop corridor-specific SPFs and explore the relationship between traffic 

characteristics (volume, speed, etc.) and crash data on a mountainous freeway in Colorado with 

volumes not exceeding 1,900 vphpl(53) The researchers found that there was a 63.2-percent 

decrease in the overall crash rate (all types and severities) following the introduction of parttime shoulder use. However, the study noted that this estimate is optimistic since the SPFs 

assumed the availability of full shoulders, which may be compromised under the part-time 

shoulder use scenario. The research team assumed an increase of 25-percent in the crash rate 

for segments with limited shoulder width compared to full shoulders, and modified the 

predicted crash frequency reduction to 53.6-percent. Standard errors and confidence levels 

were not reported by the authors. The study noted that the safety performance benefits were 

highly dependent on the number of lanes present and the level of congestion. 

Guerrieri & Mauro examined part-time shoulder use on 128 km of freeway in Italy from both a 

capacity and safety perspective. The safety portion of the study adapted freeway predictive 

methods from the HSM, calibrated with three years of crash data, to estimate what was 

expected without part-time shoulder use versus what was observed with it. Results showed 

substantial increases in capacity (up to 35-percent), but no significant variations in safety 

performance.(54)Standard errors and confidence levels were not reported by the authors.

In addition to these studies, the FHWA Part-Time Shoulder Use Guide also demonstrated an 

adaptation of the HSM freeway and interchange predictive methods for estimating the safety 

effects of part-time shoulder use. However, the guide noted various significant limitations in the 

HSM approach, including:(50)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

44

• Failure to capture changes to ramp-freeway junctions when part-time shoulder use is in

operation.

• Cannot capture effects of “remaining” shoulder widths (during part-time shoulder use)

less than four feet.

• Failure to capture potential increase in crash frequency downstream of the part-time

shoulder use location if part-time shoulder use alleviates a bottleneck.

• Failure to address changes in barrier offset when part-time shoulder use is in

operation.

• Assumed shoulder is in use 24 hours per day.

• Failure to address potential change in volume and crash frequency resulting from the

additional capacity attracting drivers from adjacent, alternative routes.

The limitations are significant enough to question the applicability of the HSM Predictive 

Method for Freeways and Interchanges to evaluate part-time shoulder use. 

The FHWA Part-Time Shoulder Use Guide also identified two studies from Europe where 

results indicated possible safety benefits of part-time shoulder use, but data and analysis 

approaches were limited.(50) The FHWA Part-Time Shoulder Use Guide authors also noted that 

“In Europe, part-time shoulder use is almost always accompanied by the construction of 

turnouts and a higher level of ATM than is typical in the U.S., including dynamic speed

limits/lane assignment, full CCTV monitoring, and aggressive incident management.” (50)

Aron et al. analyzed the safety effects of part-time shoulder use as an auxiliary lane between a

motorway entrance and exit ramp near Paris. The study used a before-after with comparison 

group study design.(55) The authors divided both the treatment and comparison sites into “subsites” based on average speed and occupancy, recognizing the likely chance that these 

operational measures would affect safety performance. They observed a reduction in crash

frequency at the location with part-time shoulder use and an increase in crash frequency 

downstream of the location, but none of these findings were statistically significant because of 

small sample sizes (standard errors and confidence levels were not reported by the authors).(55)

Summary

Safety research on part-time shoulder use focused primarily on the overall effect that 

implementation has on safety performance. Results vary significantly (see table 6), with previous

studies illustrating that this is likely because safety performance effects are dependent on the 

number of lanes present, traffic volumes, the level of congestion, time-dependent

characteristics, and any changes that may have occurred upstream of the part-time shoulder use 

location. No previous studies looked at the specific safety performance effects of the geometric

considerations, including remaining shoulder widths, lateral clearance to roadside objects, 

shoulder cross-slopes, stopping sight distances on curves, and design characteristics at entrance 

and exit ramp locations. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

45

Searches for “part-time shoulder use,” “shoulder lane use,” and “hard shoulder” in the CMF 

Clearinghouse do not result in any relevant CMFs. At the time of this report, NCHRP project 

17-89, Safety of Part-Time Shoulder Use on Freeways, was initiated.(56) The objectives of NCHRP

17-89 are to “1) develop quantitative tools for practitioners to use to evaluate safety

performance of freeways with part-time shoulder use as a function of temporal, operational,

and other conditions when the shoulder is open and closed to traffic, and (2) use the tools to

determine the safety performance of part-time shoulder use, in order to better inform State

DOT decision making on their application.”(56)

Also, at the time of this report, NCHRP project 15-59, Horizontal Sightline Offset Design Criteria, 

Exceptions, and Mitigation Strategies, was an ongoing project expected to include an evaluation of 

safety and operational performance of horizontal sightline offsets.(57) The project was also 

expected to include an analysis of the trade-offs and risks associated with state-of-the-practice 

mitigation treatments when horizontal sightline offset criteria and guidance are not met. The 

products of this project are expected to include recommendations for updates to AASHTO’s A

Policy on Geometric Design of Highways and Streets (Green Book) that will address horizontal 

sightline offset design criteria and guidance for assessing curved roadway alignments adjacent to 

barriers or other types of impediments that may affect a driver’s line of sight.(57) While parttime shoulder use is not included in the online project description, the analyses and results may 

provide insights to sight distance effects when part-time shoulder use is implemented.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

46

Table 6. Summary of safety performance evaluation findings for part-time shoulder 

use. 

Study Location Site type Method Findings

Documented 

in Jenior et al., 

2016(50)

Minneapolis, 

MN

Urban freeway Before-after with 

EB 

Overall crash frequencies 

increase 28.4%, but 

correcting for the removal of 

an upstream bottleneck 

indicated crash frequencies 

increase likely closer to 6.4%

due to PTSU

Lee et al., 

2010(51)

Northern VA Urban freeway Negative binomial 

regression

No significant safety 

performance effects

Dutta et al., 

2018(52)

Northern VA Urban freeway Before-after with 

EB 

Overall crash frequency 

decrease of 25-40% (SE 6.5-

11.3%, significant at 95%)

Kononov et 

al., 2012(53)

Denver, CO Urban and 

mountainous 

rural freeway

Neural network 

development of 

SPFs

Overall crash frequency 

decrease of 63.2% which the 

researchers modified to 

53.6% in an attempt to 

account for the reduced 

shoulder width due to PTSU

Guerrieri & 

Mauro, 

2016(54)

Northern Italy Urban freeway HSM predictive 

methods to 

compare what 

was expected with 

observed

No significant safety 

performance effects

Aron et al., 

2013(55)

Paris, France Urban freeway Before-after with 

comparison group

No significant safety 

performance effects
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

47

Reversible Lanes 

General Description of Strategy: Reversible lanes are a strategy that consists of the reversal 

of travel lanes to allocate capacity based on the directional traffic demand, thereby allowing

directional capacity to better match traffic demand throughout the day. In an Active 

Transportation and Demand Management (ATDM) approach, the lane directionality is updated 

dynamically in response to real-time traffic conditions or in advance of anticipated traffic 

conditions.(58) Dynamic lane reversal is implemented to improve vehicle flow along freeways and

arterials by providing capacity to the travel direction that most needs it at any given time.

The performance effects of dynamic lane reversal are likely primarily at the freeway or arterial

facility level where it is implemented. However, it is possible that effects may extend beyond

the facility level to the corridor level because the additional, directional peak-period capacity

may attract drivers from parallel roadways. Dynamic lane reversal affects travel time reliability

because it enables the capacity of specific directions to change in response to traffic conditions, 

even if the traffic conditions are unexpected. 

Changing the travel direction of one or more lanes as part of dynamic lane reversal changes the 

freeway cross-section characteristics, including number of directional through lanes at any given 

time and the type/amount of separation between opposing directions of travel. 

Safety Performance Evaluations: There are few known safety performance evaluations of 

dynamic lane reversal. Dey et al. performed a cursory evaluation of a 2.7-mile reversible lane 

operation on the Connecticut Avenue arterial in Washington, D.C.(59) The study did not include 

a before-after crash analysis. Instead, it compared crash data from Connecticut Avenue with 

crash data from two comparison sites that did not have reversible lanes. The comparison sites 

exhibited similar traffic characteristics and patterns to the study site. Additionally, the

evaluation examined the percent of Connecticut Avenue crashes that occurred during 

reversible lane operations. Over the six-year study period, the results showed that Connecticut 

Avenue had a higher overall crash rate (for all times of day, normalized by volume), as well as a

higher percentage of crashes occurring during the hours of reversible lane operation (standard 

errors and confidence levels were not reported). 44.3-percent of the weekday crashes on 

Connecticut Avenue occurred during reversible lane operations. Additionally, Connecticut

Avenue showed a higher proportion of head-on and sideswipe crashes. 

Searches for “reverse,” “reversal,” and “reversible” in the CMF Clearinghouse do not return

any relevant results.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

48

Dynamic Lane Use Control 

General Description of Strategy: Dynamic lane use control involves dynamically closing or 

opening individual travel lanes as needed and providing advanced warning of the lane closure(s)

or lane configurations (typically through overhead dynamic lane control signs). It is generally

used as one element of an overall freeway traffic management strategy.(58) Dynamic lane use 

control is part of a larger group of other dynamic lane assignment strategies covered in this 

report (such as reversible lanes and dynamic junction control). These strategies are generally

implemented to improve vehicle flow along freeways and arterials by providing capacity where

it is most needed. Dynamic lane use control, sometimes implemented in combination with

variable speed limits, is also implemented to support safety performance, particularly along

stretches of freeway where shoulder widths are reduced because the same system can be used

to warn drivers of closed lanes (e.g., during an incident).

Dynamic lane use control has the potential to improve travel time reliability, especially when

used in combination with other dynamic lane assignment strategies. This is because it enables

lane arrangements to change in response to traffic conditions, even if the traffic conditions are 

unexpected. 

Safety Performance Evaluations: There are few known safety performance evaluations of 

dynamic lane use control outside of those for part-time shoulder use (covered in a previous

section). The analysis by Dutta et al. of the I-66 Active Traffic Management System in Virginia

included evaluation of the safety performance of lane use control signals (LUCS).(52) The study 

used 21 weeks of before data and 13 weeks of after data to perform a before-after EB analysis 

and found no statistically significant change in crash frequency for road segments with LUCS. 

A search of “lane use” and “lane control” on the CMF Clearinghouse did not return any 

relevant results.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

49

Dynamic Junction Control 

General Description of Strategy: Dynamic junction control (also known as dynamic merge 

control) is a strategy that dynamically allocates lane access based on downstream conditions. It

is often used at interchanges with high traffic volumes and relative traffic demands between 

mainline and ramps that change throughout the day.(60,61) Dynamic junction control is 

implemented to improve vehicle flow at freeway entrance and exit ramp locations by providing

capacity to vehicles entering the freeway. Figure 6 depicts an example of the signage required to 

operate a dynamic merge system.

 Source: FHWA

Figure 6. Graphic. Rendering of a dynamic merge control arrangement in a freeway 

setting.(62)

FHWA’s overview of Active Traffic Management approaches in Guidance for the Use of Dynamic 

Lane Merging Strategies includes examples of what dynamic junction control might look like at 

entrance and exit ramp locations.(63) For exit ramp locations, dynamic junction control could

consist of assigning lanes dynamically either for through movements, shared through-exit ramp

movements, or exit-only movements. For entrance ramp locations, it could involve a dynamic 

lane reduction on the freeway mainline upstream of a high-volume entrance ramp. It could also 

involve extended use of a shoulder lane as an acceleration lane to turn a one-lane entrance 

ramp to a two-lane entrance ramp.

The performance effects of dynamic junction control are likely concentrated at ramp-freeway 

junctions of individual interchanges. It is possible that different lane assignment options at the 

ramp-freeway junctions could have effects upstream and downstream of those locations. This 

strategy could be applied at one or more interchanges on a congested freeway facility. Dynamic 

junction control has a strong link to travel time reliability, because it enables the capacity of 

specific movements at freeway entrance and exit ramp locations to change in response to traffic 

conditions, even if the traffic conditions are unexpected.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

50

Dynamic junction control is also commonly deployed in temporary applications, especially in

the case of work zone lane closures. Many work zone-specific systems are designed to alternate 

dynamically between early merging (most effective in low congestion conditions) and late, or

zipper, merging (most effective in congested conditions).(63)

Changing lane access at interchanges as part of dynamic junction control changes the freeway 

cross section geometric characteristics, including number of through lanes on the freeway

mainline as well as the cross-section design characteristics of the entrance and exit ramp 

terminals. 

Safety Performance Evaluations: There are no known safety performance evaluations of 

dynamic junction control. Searches of “junction” and “merge” on the CMF Clearinghouse did 

not return any relevant results. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

51

Ramp Metering 

General Description of Strategy: Ramp meters are traffic signals installed on freeway onramps to control the rate at which vehicles enter the flow of traffic on the freeway mainline.(64)

The rate at which the ramp meters release vehicles onto the freeway depends on the mainline 

traffic volume and speed. Figure 7 below shows the basic operation of a ramp meter.

 Source: FHWA

Figure 7. Graphic. Basic ramp metering configuration.(65)

The primary reason to implement ramp metering is to improve vehicle flow by breaking up

platoons of vehicles entering the freeway from an entrance ramp and by managing the entrance 

demand to a level that seeks to keep the freeway operating under capacity. However, ramp

metering is also implemented based on the assumption that the strategy can reduce mergerelated crashes. 

Implementing ramp metering does not require changes to roadway geometrics, but there are

some design and operational considerations for determining the location of the ramp meter. 

These include the distance from the crossroad to the ramp meter for queue storage and the 

distance from the ramp meter to the freeway entrance terminal to provide for vehicle

acceleration. 

Safety Performance Evaluations: MnDOT evaluated the effectiveness of ramp meters using 

data from four selected corridors in the Twin Cities Metropolitan region.(66) They collected data

during five-week time periods when ramp meters were operating and then during a subsequent 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

52

phase when ramp meters were deactivated. The results showed that the frequency of observed 

peak-period crashes (all types and severities combined) increased by 26-percent during the 

deactivation phase while controlling for seasonal variation using data from equivalent periods 

during the previous two years.

(66) Exploration of crash types showed significant increases in 

rear-end (15-percent), side-swipe (200-percent), and run-off-road (60-percent) crashes, which 

are the most typical types of ramp-related crashes. An additional crash severity analysis showed 

a significant 33-percent increase in the frequency of no apparent injury crashes when ramp 

metering was deactivated, but no significant changes in the frequency of fatal or injury crashes 

(likely due to small sample sizes of these crashes). The standard errors and confidence levels 

for these findings were not reported by the authors.

Other cities have conducted similar studies to evaluate new ramp metering systems. While 

these studies generally focus on operational metrics and suffer from small crash data sample 

sizes and limited analysis, they can still provide information to inform future exploration of the 

safety effects of ramp metering. The Kansas and Missouri Departments of Transportation jointly 

operate a transportation program for Kansas City, which conducted an evaluation of a ramp 

metering pilot program. One year after the installation of the ramp meters, the results showed 

a 64-percent decrease in the frequency of all crashes, and an 81-percent decrease in the 

frequency of merge-related crashes.(67) A press release from the Colorado DOT reported that

rear-end and side-swipe crashes were reduced by about 50-percent due to the implementation 

of a ramp metering pilot program in Denver (the analysis period was not reported).

(68)

However, neither study indicated any analysis of crash severity, and all of the analysis results 

were based on naïve comparisons of observed crash frequencies. Additionally, standard errors 

and confidence levels were not reported by the authors for either study.

Liu & Wang conducted a before-after safety evaluation of 19 metered on-ramps in Northern 

California.(69) The researchers collected data for three years before and three years after the 

ramp meters were implemented and compared before crash rates to after crash rates for the 

segments 0.1 miles upstream of the merge points to 0.1 miles downstream of the merge points. 

The study found a 36-percent reduction in the total crash rate (all types and severities 

combined) on the study sections (standard error and confidence level were not reported by 

the authors). 

Drakopoulos et al. evaluated the incremental effectiveness of six new ramp meters (in addition 

to six previously existing meters) on US 45 in Wisconsin.(70) The study used six months of 

before data and six months of after data and revealed a 16-percent reduction in crash frequency 

and a 13-percent decrease in crash rate following the implementation of the additional ramp 

meters (standard errors and confidence levels were not reported by the authors). However, 

the study noted that during the installation of the ramp meters, additional geometric 

improvements and resurfacing operations were conducted that may have also affected the 

safety performance of the roadway.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

53

Aydos & O’Brien analyzed 25 of 84 total ramp metering sites in Auckland, New Zealand that 

were operated through the Sydney Coordinated Adaptive Traffic System (SCATS) Ramp 

Metering System. The authors selected these sites based on data availability and to limit the 

effect of construction operations on the findings. A naïve before-after study of the crash data 

concluded that there was a 22.1-percent reduction in crashes (with a standard error of 4.3-

percent) following the implementation of ramp metering.(71) This result was statistically

significant at a 99-percent confidence level.

A variety of studies have attempted to assess the safety effects of ramp metering using 

simulation techniques and surrogate measures of safety. Lee et al. studied a section of I-880 in 

California as well as a hypothetical on-ramp network by incorporating a log-linear real-time 

crash prediction model into PARAMICS microsimulation software. The model estimated crash

potential based on surrogate safety measures, including speed variation, speed difference 

between upstream and downstream points, and volume difference between upstream and 

downstream points. The model was calibrated using one year of crash data. The results 

indicated that ramp metering reduced crash potential (for all types and severities) by 5- to 37-

percent, with the most notable results occurring in congested conditions.(72) The standard error 

was 2- to 3-percent, and the change was statistically significant at a 95-percent confidence level.

Abdel-Aty et al. and Gayah also used PARAMICS to investigate ramp metering in Orlando, 

Florida. Abdel-Aty et al.’s study focused on the congested traffic regime, using a previously

developed crash probability model that considered speed variance, loop detector occupancy, 

and volume variance.(73) The model found that metering more ramps compounds the benefit of 

reduced crash probability. Gayah’s study used neural networks to convert the simulation 

results into rear-end and lane-change crash probability values based on geometric and loopdetector data. The neural network application showed that ramp metering can reduce the 

crash probability for rear-end and lane-change crashes, particularly under congested conditions. 

Furthermore, the performance of the ramp metering algorithm depends on the congestion

condition. However, ramp metering showed the potential for inducing slight crash migration in 

some scenarios.(74) This means that implementation of ramp metering may reduce crash

probability at the immediate location but result in an increased crash probability at an upstream 

or downstream location. Gayah found that, in most cases, the migration effect was negligible. In

a subset of sites, the increase at upstream and downstream locations was significant, which

indicates that careful site-specific analysis should be performed prior to introducing ramp 

metering. 

Summary

The synthesis of ramp metering safety research discussed several safety evaluations based on an 

analysis of crash data. All indicated improvements in safety performance (see table 7), but all 

used what is generally thought to be the least reliable type of observational study design: a naïve

before-after study comparing observed crash frequencies or rates. There is a need for a more
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

54

robust and defensible safety performance evaluation to build on these previous results. 

Additionally, further guidance is needed regarding the spatial limits of the study area when 

analyzing crashes in the context of ramp metering. Ramp metering is likely to only affect crashes 

within a certain influence area surrounding the treatment site, as opposed to all crashes on the 

freeway mainline. A search for “ramp meter” in the CMF Clearinghouse results in only one 

CMF: a three-star CMF of 0.64 for all crash types and severities combined based on the Liu & 

Wang study (CMF ID 5436).

(69)

Safety evaluations based on surrogate measures of safety had 

similar results to the naïve before-after studies. One surrogate-based study showed the 

importance of crash analysis at upstream and downstream locations prior to finalizing selection 

of ramp metering because changes in the surrogate measures at these locations pointed to an 

increase in crash frequency at the upstream and downstream locations.

Table 7. Summary of safety performance evaluation findings for ramp metering. 

Study Location Site type Method Findings

Cambridge 

Systematics, 

Inc., 2001(66)

Minneapolis-St. 

Paul, MN

Urban freeway Naïve before-after When ramp meters were 

deactivated:

• Peak-period overall crash

frequency increased 26%

• Increase in rear-end (15%),

sideswipe (200%), and runoff-road (60%) crash

frequencies

• 33% increase in no apparent

injury crash frequencies, no

significant change in fatal or

injury crash frequencies

Kansas City 

SCOUT, 

2011(67)

Kansas City, 

KS/MO

Urban freeway Naïve before-after • 64% decrease in overall

crash frequency

• 81% decrease in mergerelated crash frequency

Liu & Wang, 

2013(69)

Northern CA Urban freeway Naïve before-after 36% reduction in overall crash 

rate

Drakopoulos 

et al., 2004(70)

Milwaukee, WI Urban freeway Naïve before-after 13% reduction in overall crash 

rate (other geometric 

improvements confounded 

findings) 

Aydos & 

O’Brien, 

2014(71)

Auckland, New 

Zealand

Urban freeway Naïve before-after 22.1% reduction in overall crash 

frequency (SE 4.3%, significant at 

99%) 

Lee et al., 

2006(72)

Hayward, CA Urban freeway Simulation-based 

surrogate study

• Ramp metering reduced

crash potential 5-37% (SE 2-

3%, significant at 95%)

• Most notable results

occurred in congested

conditions
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

55

Study Location Site type Method Findings

Abdel-Aty et 

al., 2007(73)

Orlando, FL Urban freeway Simulation-based 

surrogate study

Metering more ramps increases 

the potential safety benefits by 

decreasing estimated crash 

probability

Gayah, 

2006(74)

Orlando, FL Urban freeway Simulation-based 

surrogate study

• Ramp metering can reduce

the probability of rear-end

and lane-change crashes,

particularly under congested

conditions

• Different ramp metering

algorithms perform better

under different congestion

conditions

• Ramp metering showed

potential for crash migration

in some scenarios
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

56

Variable Speed Limits 

General Description of Strategy: VSLs are a strategy that allows speed limits at a location to 

change based on prevailing road, traffic, and weather conditions. Variable speed limits

harmonize operating speeds and improve driver expectation by providing information in 

advance on slowdowns and potential lane closures. Some publications have suggested that this 

strategy offers considerable promise in restoring the credibility of speed limits.(75,76) VSL systems 

utilize information on traffic speed, occupancy, volumes, weather, and road surface conditions 

to determine appropriate speeds and display them for drivers.(75) Figure 8 shows an example of 

VSL signage, though there are numerous types of VSL signs depending on different system or 

site characteristics. VSL may be implemented as either a regulatory or an advisory system (or 

both).

 Source: FHWA

Figure 8. Graphic. Rendering of variable speed limit sign gantry in Seattle, WA.(77)

The primary reason to implement this strategy is to improve safety performance by decreasing 

the frequency of crashes associated with traveling at speeds that are higher than the speed

appropriate for the prevailing conditions. The strategy also seeks to improve safety 

performance and traffic flow by reducing speed variance (i.e., improving speed harmonization) 

and reducing the probability for secondary crashes. VSLs are also implemented to mitigate 

adverse weather conditions or to slow faster-moving traffic as it approaches a queue or 

bottleneck

Safety Performance Evaluations: In May 2008, the Missouri Department of Transportation 

(MoDOT) installed signs with VSL on the I-270/I-255 loop around St. Louis with the objectives

of improving traffic flow, preventing traffic flow breakdown, reducing congestion and delay, and

improving safety performance. During rush hours and traffic incidents, an automated system set 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

57

speed limits between 40 mph and 60 mph to support speed harmonization within and across 

lanes. More specifically, MoDOT sought to lower speed limits upstream of congestion to 

reduce the closing speed of incoming traffic.

Bham et al. examined the effect of this MoDOT VSL application on changes in travel time, travel 

time reliability, capacity, number of crashes, and crash severity.(78) The team conducted 

descriptive comparisons of observed crash counts, a naïve before-after study, and a before-after

EB study. The analyses used one year of before data and one year of after data. The descriptive 

comparisons looked at safety performance by time of day. Results were mixed, showing 

reductions in the observed number of morning peak crashes and slight increases in the number 

of evening peak crashes. The naïve before-after analysis and EB analysis included all crashes 

from the before and after periods (not just peak period crashes). The before-after EB study

found that the VSL system contributed to an estimated 8.4-percent overall reduction in crash 

frequency for all types and severities (with a standard error of 3.8-percent).(78) This result was 

statistically significant at a 95-percent confidence level.

Pu et al. evaluated a VSL deployment on a seven-mile section of I-5 in Washington, which 

displayed automated speed limits based on real-time traffic conditions.(79) The study used 3.5 

years of before data and 2.5 years of after data to perform a before-after EB analysis. Results 

showed an average crash frequency reduction of 29-percent (with a standard error of 5-

percent) for all crash types and severities. This result was significant at a 95-percent confidence 

level. Additional analysis showed that VSL implementation reduced property damage only

crashes (O crashes) by 25-percent (standard error of 7-percent), possible injury crashes (C 

crashes) by 28-percent (standard error of 10-percent), and crashes with fatalities, suspected 

serious injuries, and suspected minor injuries (KAB crashes) by 19-percent (standard error of 

24-percent). All of these crash severity results were significant at a 95-percent confidence level

except for the decrease in KAB crashes.

Siddiqui et al. investigated safety effects of an advisory VSL system deployed along the Oregon 

(OR)-217 freeway in Portland, OR based on five months of after data.

(80) This system was part 

of an OR-217 active traffic management project, which was intended to address safety 

performance and congestion along the corridor. Analysis of crash data and surrogate safety 

measures showed a decrease in total crash frequency, total crash rate, and the rear-end crash 

rate, despite an increase in VMT on the corridor. A naïve crash analysis showed a decrease of 

14.8-percent in crash rate for total crashes (all types and severities) and a decrease of 10.5-

percent in crash rate for rear-end crashes. Standard errors and confidence levels were not 

reported by the authors. Due to the recent deployment of the system, only five months of after 

data was available, which is why surrogate safety measures, in this case mean speed and speed 

variability, were explored and used. The system was found to generally reduce mean speed and 

speed variability within the same lane and between the median and outside lanes.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

58

De Pauw et al. used a before-after EB study to assess the safety performance effects of a 

variable speed limit system on 60 km of freeways in Flanders, Belgium using crash data from 

1999 to 2011.(81) The analysis showed that there was an 18-percent reduction in the number of 

injury crashes (KABC) following implementation of VSL (with a standard error of 6.6-percent). 

This result was statistically significant at a 95-percent confidence level. Other results, such as a 

decrease in fatal and suspected serious injury crashes and a decrease in rear-end crashes, were 

not statistically significant at a 95-percent confidence level. 

Kuhn et al. evaluated a VSL pilot project in Texas which sought to examine the effectiveness of 

VSL for application in construction work zones, adverse weather conditions, and urban 

congestion. The study used six months of before data and one year of after data to conduct a 

naïve before-after analysis. Results showed a 31-percent decrease in overall crash frequency (all 

types and severities) after the implementation of the VSL pilot program, with associated 

decreases in crash severity, particularly in the proportion of fatal and incapacitating injury 

crashes. However, the authors stated that no statistical significance tests were conducted due 

to the short duration of the analysis period. (82)

Wyoming DOT installed four different weather based VSL systems on a combined 147 miles of 

I-80 with the goal of reducing the potential for crashes under adverse weather or road surface

conditions. Buddemeyer et al. performed a preliminary crash analysis on the Elk Mountain

corridor of I-80.(83) . The analysis used five years of before data and one year of after data and

results showed a 30-percent decrease in crash rates (standard error and confidence level were

not reported by the authors). Gaweesh et al. used negative binomial and multivariate adaptive

regression splines (MARS) models along with before-after EB methodology to analyze six years

of before data and five years of after data on another section of I-80 in Wyoming. The results

showed that the implementation of VSL led to reductions in crash frequency for fatal and injury

crashes (KABC crashes) that were significant at a 95-percent confidence level (standard errors

were not reported by the authors). Decreases in no apparent injury crashes (O crashes) were

not statistically significant at a 95-percent confidence level.(84)

Some studies have used simulation methods and surrogate measures of safety in an attempt to 

quantify how the implementation of variable speed limits effect safety performance. Abdel-Aty 

et al. combined previously-developed crash prediction modeling techniques with 

microsimulation in PARAMICS to explore the effectiveness of VSL as a strategy for improving 

safety on interstate roadways.(85) The crash prediction model considered two separate speed 

regimes, with the threshold speed being 37.5 miles per hour. The high-speed regime used 

average occupancy and flow as inputs, and the low-speed regime considered average volume, 

occupancy, and coefficient of variation of speed. The study found that changing the speed limit 

by large amounts (such as 15 miles-per-hour) through a combination of gradual speed change 

over time (five miles-per-hour change every 10 minutes) and abrupt speed change in space (no 

transition zone with graduated speeds) produced the greatest reduction in crash potential at 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

59

the study location. Lee et al. performed a similar analysis using PARAMICS and a log-linear 

crash prediction model on a 4.7-km section of freeway in Toronto, Canada.(86) The study 

investigated the effect of VSL activation threshold, duration, and control strategy on crash 

potential and found that implementing VSL can lead to reductions in the overall crash potential 

by 5- to 17-percent (standard errors and confidence levels were not reported by the authors). 

The surrogate safety measures examined included speed variation at a point, difference in speed 

variation between upstream and downstream locations, and the covariance of the volume 

difference between upstream and downstream locations on adjacent lanes (a measure of lane 

changing behavior). The researchers observed higher reductions in crash potential based on 

these surrogates for lower VSL activation threshold values (where the activation threshold is 

based on crash potential). Mid-length VSL intervention duration (five-to-ten minutes) was found 

to balance reduced crash potential and travel time, while longer and shorter durations 

significantly increased travel times while not providing significantly better safety performance. 

Habtemichael & de Picado Santos assessed the effect of VSL implementation on vehicle conflicts 

on a 12-kilometer freeway segment in Lisbon, Portugal.

(87) The segment was simulated in Vissim

and then processed through SSAM to identify vehicle conflicts, using TTC and PET as the 

primary surrogate safety measures. The study notably simulated VSL effects under a variety of 

driver compliance levels, ranging from 25-percent to 100-percent. Results showed that VSL 

provided safety performance benefits based on the surrogate measures under all traffic 

conditions, but the improvement was highest under congested conditions. Additionally, the 

safety benefits did not come at the expense of operational efficiency. The magnitude of the 

benefits was highly dependent on the level of driver compliance.

Islam et al. proposed a predictive VSL control strategy and evaluated its safety effects using 

microsimulation and a crash prediction model.(88) The crash prediction model relied on loop 

detector data and developed a surrogate relationship between the detector data (average 

detector occupancy, speed variation, and volume variation) and crash occurrence. The model 

was calibrated using crash data from the study location, a 9.2-kilometer urban freeway segment 

in Edmonton, Canada. The authors found that a high standard deviation in vehicle speeds is the 

most important variable for predicting a crash. Results revealed lower average crash

probabilities for the tested VSL scenarios compared to the “no VSL” case in congested crashes, 

resulting in a 31- to 50-percent reduction in predicted crash probability (standard errors and 

confidence levels were not reported by the authors). The model showed no improvements in 

the uncongested case. The best VSL scenario for safety performance involved an update 

frequency (frequency with which the VSL system is changing the speed limit) of five minutes, the 

longest update frequency of any tested scenario. This indicates that frequent changes to the

posted speed limit may introduce traffic disturbances that may increase crash risk. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

60

Summary

The synthesis of VSL safety research describes several safety evaluations based on an analysis of 

crash data. Almost all indicated improvements in safety performance although some findings 

were not significant (see table 8). Study designs varied from the less reliable naïve before-after 

studies comparing observed crash frequencies or rates to more robust before-after EB 

approaches. In some cases, the durations of before and after periods were short (e.g., one 

year). A search for “variable speed limit” in the CMF Clearinghouse results in the following:

• Four CMFs (three four-star and one two-star) for “install variable speed limits” based

on the Pu et al. work discussed above (CMF IDs 8730 – 8733).(79)

• A four-star CMF for “install variable speed limit signs” based on the Bham et al. work

discussed above (CMF ID 3340).

(78)

Safety performance evaluations based on surrogate measures of safety had similar results to the 

safety performance benefits shown by the crash-based studies. The surrogate-based studies 

were able to offer results related to VSL system parameters such as activation threshold, 

update frequency, and intervention duration. The studies showed that these factors have the 

potential to significantly alter the safety performance effects of VSL (as, for instance, in Islam et 

al.’s study of VSL update frequency).(88)

At the time of this safety analysis needs assessment, FHWA Office of Safety R&D had initiated a 

safety evaluation of VSL under Evaluation of Safety Improvements, Phase X. 

Table 8. Summary of safety performance evaluation findings for variable speed 

limits. 

Study Location Site type Method Findings

Bham et al., 

2010(78)

St. Louis, MO Urban freeway Before-after with 

EB 

• Mixed results, with

decrease in peak-period

AM crashes and slight

increase in peak-period

PM crashes

• Overall crash frequency

decrease of 8.4% (SE

3.8%, significant at 95%)

Pu et al., 

2016(79)

Seattle, WA Urban freeway Before-after with 

EB 

• Overall crash frequency

decrease of 29% (SE 5%,

significant at 95%)

• Reductions in no

apparent injury (25%, SE

7%), possible injury (28%,

SE 10%), and

fatality/suspected serious

injury/suspected minor

injury (19%, SE 24%)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

61

Study Location Site type Method Findings

crash frequency (all 

results significant at 95% 

except for fatality/serious 

injury/minor injury)

Siddiqui & AlKaisy, 2016(80)

Portland, OR Urban freeway Naïve before-after 

and analysis of

surrogate 

measures

• Overall crash rate

decrease of 14.8%

• Rear-end crash rate

decrease of 10.5%

Decrease in mean speed

and speed variability both

within the same lane and

between the median and

outside lanes.

De Pauw et 

al., 2018(81)

Flanders, 

Belgium

Urban freeway Before-after with 

EB 

• Minor injury crash

frequency decrease of

18% (SE 6.6%, significant

at 95%)

• Minor injury rear-end

crash frequency decrease

of 20% (not significant at

95%)

Kuhn et al., 

2015(82)

San Antonio, 

TX; Temple, 

TX; Ranger Hill, 

TX

Urban and rural 

freeway

Naïve before-after Overall crash frequency 

decrease of 31%

Buddemeyer 

et al., 2010(83)

Southern WY Rural freeway Naïve before-after Overall crash rate decrease 

of 30%

Gaweesh et 

al., 2018(84)

Southern WY Rural freeway Before-after with 

EB 

Fatal and injury crash 

frequency reductions 

(significant at 95%). No 

apparent injury crash 

frequency reductions not 

significant.

Abdel-Aty et 

al., 2005(85)

Orlando, FL Urban freeway Simulation-based 

surrogate study

Changing the speed limit by 

large values, with gradual 

change over time and abrupt 

change in space produced the 

greatest reduction in crash 

potential

Lee et al., 

2006(86)

Toronto, 

Canada

Urban freeway Simulation-based 

surrogate study

• VSL can lead to overall

crash potential

reductions of 5-17%

• Higher reductions in

crash potential for lower

activation threshold

values
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

62

Study Location Site type Method Findings

• Mid-length VSL

intervention duration

balanced reduced crash

potential with increased

travel time

Habtemichael 

& de Picado 

Santos, 

2013(87)

Lisbon, Portugal Urban freeway Simulation-based 

surrogate study

• Safety benefits of VSL

were highest under

congested conditions

• Safety benefits did not

come at the expense of

operational efficiency

Islam et al., 

2013(88)

Edmonton, 

Canada

Urban freeway Simulation-based 

surrogate study

• Crash probability

decrease of 31-50% for

best-case VSL scenario

• No safety benefits in

uncongested conditions
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

63

Traffic Signal Coordination 

General Description of Strategy: Coordination of traffic signal phasing across multiple 

signalized intersections can enhance the operation of one or more directional movements in a 

system. Figure 9 shows a time-space diagram as a graphical representation of a well-coordinated 

signalized corridor, with the blue arrows showing ‘green bands’ or areas where vehicles, if 

travelling at the speed trajectory denoted by the arrow, will not have to stop. Vehicles traveling 

both directions on the corridor can pass through the four signals without being stopped by a 

red indication, improving the platooning and operational efficiency of the corridor. Example 

applications for traffic signal coordination include two signals controlling a diamond interchange, 

a series of signalized intersections along an arterial street, and downtown networks of actuated 

and fixed time signals controlling an arterial system.(89)

 Source: FHWA 

Figure 9. Graphic. Time-space diagram of a coordinated timing plan for a corridor 

with four signals.

(89) 

The primary reason to coordinate traffic signals is to improve vehicle flow along the 

coordinated street by reducing stops, delay, and travel times for the selected movements on 

which the coordination is based.(89) This responds to an FHWA operational objective of

“Smooth Flow,” which is intended to encourage improved progression through signalized 

arterial corridors. Additionally, arterial signal coordination can have secondary effects on other 

objectives, such as “Maximize Throughput” and “Manage Queues”.(90) The performance effects 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

64

of traffic signal coordination will be observed primarily along the facility containing the 

coordinated signals. Performance effects along the crossing facilities are also possible.

A Denver Regional Council of Governments (DRCOG) summary of 15 arterial traffic signal 

coordination efforts showed consistent travel time reductions (as well as reductions in fuel 

consumption and emissions). On local facilities, cross streets, left turns, pedestrians, and cyclists

may experience higher delays depending on how coordination operation is programmed. 

Safety Evaluations: Williamson et al. conducted a before-after analysis with comparison sites 

for five urban corridors in southern Illinois and combined results across the different sites using 

a meta-analysis method with weighting based on standard error.(91) The results showed that 

implementing arterial signal coordination decreased the frequency of all crash types and

severities (though there were no fatal crashes in the dataset). Total crash frequency decreased

by 21-percent and injury crashes decreased by 52-percent (when the results from individual 

sites were combined). However, the changes in total and injury crash frequency at several of

the sites showed very high standard errors and were not statistically significant. 

Rakha et al. attempted to estimate the safety performance effects of arterial signal coordination

using before and after data from a 9.6-km corridor project between Tempe and Scottsdale, 

Arizona.(92) Safety performance effects were estimated by comparing observed before and after 

crash rates, which showed an overall corridor level 6.7-percent reduction following

implementation of the signal coordination. This result was statistically significant at a 99-percent 

confidence level, but the standard error was not reported. However, results varied across

intersections and likely suffered from small sample sizes at the individual intersection level and 

the less reliable study design and analysis approach.

Li & Tarko analyzed the safety performance effects of signal timing characteristics at 36

intersections that were part of coordinated systems in Indiana.(93) Their analysis focused on 

rear-end and angle crashes. The authors used a multinomial logit model to estimate the 

probability of either a rear-end crash, right-angle crash, other crash, or no crash during 15-

minute intervals. They also developed a binary logit model to estimate the probability that a

crash that did occur resulted in either an injury or no injury. The following were signal timingrelated safety findings: 

• Higher concentrations of vehicles arriving in the first half of the red signal were

associated with higher probabilities of a rear-end or right-angle crash;

• Higher concentrations of vehicles arriving in the first two seconds of the green signal

were associated with higher probabilities of a rear-end or right-angle crash;

• Higher concentrations of vehicles arriving during the second half of the green signal

were associated with lower probabilities of a rear-end or right-angle crash;
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

65

• Higher concentrations of vehicles arriving during the second half of the green signal

were associated with higher severities of rear-end crashes, but lower severities of rightangle crashes.

The model developed by Li & Tarko also includes variables related to intersection geometry, 

presence of turn lanes, and various signal timing parameters.

There are several studies that identify potential reductions in safety performance as a result of 

signal coordination. Jolovic et al. used negative binomial and multinomial logit techniques to 

model the effect of various traffic signal timing parameters on crash frequency and severity in 

Fort Lauderdale, Florida.(94) The researchers expected longer cycle lengths to reduce crash 

severity, as longer cycle lengths introduce more flexibility for implementing signal coordination 

and improving arterial progression. This in turn reduces the number of stops at an intersection, 

which could be correlated with the occurrence of rear-end crashes, one of the most common 

intersection-related crash types. However, the negative binomial model results showed that 

longer cycle lengths had the opposite effect, increasing crash frequencies. The researchers 

hypothesized that longer cycle lengths may be a sign of increased exposure on the corridor in 

question, implying increased time required to clear the intersection due to heavier traffic. The 

multinomial logit results indicated that crash severity decreased with longer cycle lengths. This 

may be because traffic speeds decrease due to congested conditions that might be more 

common when longer cycle lengths are present.

Tindale & Hsu contended that coordinated traffic signal systems may incentivize drivers to 

increase their speed to stay in or catch up to the progression platoon.(95) Their study focused 

on one-way roads, which are often easier to coordinate. The study involved a crash analysis 

based on a descriptive analysis of observed crash data in the Tampa-St. Petersburg metropolitan 

area in Florida. The results showed that coordinated urban one-way roads exhibited a 

disproportionate share of red light-running crashes, representing nearly 15-percent of crashes 

compared to an average of 3.3-percent across all road types.

Guo et al. assessed expected crash frequency (all crash types and severities combined) at fourleg, signalized intersections in Central Florida. The study used a variety of modeling techniques, 

including Poisson and negative binomial Bayesian models, mixed-effects models, and conditional 

autoregressive models. The Poisson spatial model provided the best fit based on Deviance 

Information Criterion (DIC). The results showed that the intersections in coordinated systems 

had 53-percent more intersection and intersection-related crashes than those that were not in 

coordinated systems.(96) The authors noted that it was unclear how much of this finding to 

attribute to the operational results of coordination (e.g., higher speeds) versus the confounding 

effects of variables omitted from their analysis. These omitted variables could distinguish 

intersections that are likely to be coordinated from those that are not (e.g., high volumes, 

proximity to other intersections).(96)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

66

Stevanovic et al. performed a simulation-based study to examine the effects of signal timing 

optimization on surrogate measures of safety.(97) The study used Vissim microsimulation to

produce vehicle trajectory data, which was then fed into the SSAM to assess the number of 

vehicle conflicts. The signal timings in the Vissim model were optimized using VISGAOST, a

Vissim plug-in. Vehicle conflicts were identified based on threshold values of TTC and PET, and 

SSAM determined the type of conflict based on the conflict angle and geometric information. 

Results showed there is a balance between improving safety performance by reducing conflicts

and increasing efficiency measured as intersection throughput. By using the ratio of the number 

of conflicts to throughput as the objective function in the optimization process, the optimal 

scenario decreased the estimated number of conflicts by 7-percent while the throughput 

remained steady. 

Summary

The synthesis of safety research on traffic signal coordination uncovered several safety 

evaluations based on an analysis of crash data and one based on simulated safety surrogates. 

Results were mixed (see table 9) ranging from 21-percent and 53-percent reductions in total 

and injury crash frequencies, respectively, to 53-percent increases in intersection and

intersection-related crashes. Study designs and analysis varied, likely leading to this mix in 

findings. 

A search for “coordinate” in the CMF Clearinghouse results in the following:

• 18 CMFs (two-star to three-star) for “coordinate arterial signals,” for all crash types and

various severities and areas, based on the Williamson et al. study summarized above

(CMF IDs 9853 – 9870).(91)

• Two CMFs (one two-star and one three-star) for “presence of right turning lane on

arterial with signal coordination,” for rear-end and angle crashes at all severities (CMF

IDs 3071 and 3073). One three-star CMF for “change number of cycles per hour on

arterial with signal coordination,” for rear-end crashes of all severities (CMF ID 3072).

These three are based on the study by Li & Tarko.(93)

One surrogate-based study indicated a balance or trade-off between vehicle throughput and 

number of intersection conflicts. 

The work of Li & Tarko offered an example of the effective use of a probabilistic model of crash 

occurrence in short time intervals to gain key insights that are not possible with more macrolevel (i.e., annual) crash-based analysis. Their models were able to show how crash probabilities 

changes based on the number of vehicles arriving during different times of the red and green 

signal indications. Such an approach holds promise for several other TSMO strategies and could 

be used in combination with other study design and analysis approaches.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

67

Table 9. Summary of safety performance evaluation findings for traffic signal 

coordination.

Study Location Site type Method Findings

Williamson et 

al., 2018(91)

Southern IL Urban signalized 

arterial

Before-after 

with 

comparison 

sites; metaanalysis

• Overall crash frequency

decrease of 21% (not

significant)

• Injury crash frequency

decrease of 52% (not

significant)

Rakha et al., 

2000(92)

TempeScottsdale, AZUrban signalized 

arterial

Naïve beforeafterDecrease of 6.7% in overall crash 

rate (significant at 99%)

Li & Tarko, 

2011(93)

Danville, IN; 

Frankfort, IN; 

West Lafayette, 

IN 

Urban signalized 

arterial 

Multinomial 

logit for 

occurrence of 

specific crash 

types

Binary logit 

for crash 

severity

• More vehicles arriving in first

half of red signal associated

with higher probabilities of RE

or RA crash;

• More vehicles arriving in first

two seconds of green signal

associated with higher

probabilities of RE or RA

crash;

• More vehicles arriving during

second half of green signal

associated with lower

probabilities of RE or RA

crash;

• More vehicles arriving during

second half of green signal

associated with higher

severities of RE crashes, but

lower severities of RA crashes.

Jolovic et al., 

2016(94)

Ft. Lauderdale, 

FL

Urban signalized 

arterial 

Negative 

binomial 

regression for 

crash 

frequency 

Multinomial 

logit for crash 

severity

Longer cycle lengths increased 

crash frequencies, but decreased 

crash severities

Tindale & 

Hsu, 2005(95)

Tampa-St. 

Petersburg, FL

Urban one-way 

streets

Naïve 

comparison of 

descriptive 

statistics 

Coordinated urban one-way 

streets represent 15% of crashes 

compared to average of 3.3% 

across all road types

Guo et al., 

2010(96)

Orlando, FL Urban signalized 

arterial 

Poisson and 

negative 

binomial 

Bayesian 

models

Coordinated intersections had 

53% more intersection-related 

crashes
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

68

Study Location Site type Method Findings

Stevanovic et 

al., 2013(97)

Boca Raton, FL Urban signalized 

arterial 

Conflict 

analysis 

(SSAM)

• There is a balance between

increasing safety by decreasing

conflicts and increasing

efficiency measured as

intersection throughput

• Optimal timing scenario led to

decrease of 7% in overall

conflicts while throughput

remained steady
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

69

Adaptive Signal Control Technology 

General Description of Strategy: Adaptive Signal Control Technology (ASCT) uses vehicle 

detection technology and traffic control algorithms to continually adjust traffic signal timing

parameters based on traffic conditions. ASCT includes algorithms that adjust split, offset, phase 

length, and phase sequence to better accommodate patterns in demand and therefore minimize 

delays and reduce the number of stops.(98) There are numerous private-sector ASCT products

available, with a variety of unique methods, algorithms, and hardware and data implications. 

Some of the most common products include SCATS, SCOOT, InSync, RHODES, ASC Lite, and 

SURTRAC. ASCT is an FHWA Every Day Counts (EDC) – Round 1 innovation. 

The primary reason to implement ASCT is to address high variability in traffic demand. High

variability in demand can require frequent modification of traffic signal timing, which is difficult 

to accomplish using traditional time-of-day plans. ASCT can be particularly useful for 

intersections that are affected by special events or where conventional time-of-day cycles

cannot be effectively determined because of highly-variable or unpredictable demand. ASCT is

commonly applied to a series of intersections on a facility, though it may offer improvement in

the case of isolated major intersections.(90) The spatial extent of ASCT performance effects 

would therefore depend on the application but is commonly observed at the arterial facility 

level. 

Evaluations of ASCT have shown improvements in travel time, control delay, emissions, and fuel 

consumption, with the level of improvement dependent on characteristics of demand and the 

traffic signal systems that were replaced by ASCT.(98) This strategy also has a strong link to 

travel time reliability because it enables signal timing to adjust in response to changing traffic 

conditions, even if the changes are unexpected. 

Safety Performance Evaluations: Ma et al. evaluated the safety performance effects of ASCT 

at 47 urban and suburban intersections in Virginia. The researchers employed a before-after 

with EB study design and analysis approach.(99) The analysis produced a total crash reduction 

(i.e., all types and severities) of 17-percent (with a standard error of 5-percent). This result was

significant at a 95-percent confidence level. Fatal and injury crash frequency decreased by 8-

percent (with a standard error of 8-percent). This result was not statistically significant. The 

authors pointed out that safety benefits varied by facility and traffic volume and noted that this 

could be due to the differences in operational changes that resulted from ASCT on those 

facilities. They could not, however, confirm this explanation through statistical analysis.

Fink et al. investigated the safety effects of the SCATS-based ASCT system known as FASTTRAC in Oakland County, MI.(100) The study used a cross-sectional before-after design to 

compare intersections with FAST-TRAC in Oakland County to similar intersections without it

elsewhere in Michigan. Crash frequencies were modeled using negative binomial models and
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

70

crash severity was modeled using multinomial logit models. The resulting estimates showed that 

the frequency of angle crashes was 19.3-percent lower at intersections with SCATS-based 

controllers. There were no statistically significant differences in the frequencies of total (i.e., all 

types) and rear-end crashes at intersections with and without the SCATS-based controllers. 

The analysis of crash severity indicated that crashes occurring at intersections with SCATSbased controllers were less severe, but the results were not statistically significant.

Khattak et al. estimated CMFs for ASCT technologies at 41 urban and suburban intersections in 

three Pennsylvania cities and towns.(101) They used the before-after EB study design and analysis 

approach to estimate the changes in the frequency of multiple-vehicle crashes within 350 ft of 

intersections where ASCT was implemented. The dataset consisted of two types of ASCT 

systems, SURTRAC and InSync, which differ in their data collection and optimization methods. 

The number of before years for the intersections ranged from two to four and the number of 

after years ranged from three to five. Intersecting roads had speed limits ranging from 25 mph 

to 45 mph with average mainline AADTs of 26,700 vehicles per day. The article did not include 

details on signal phasing and timing in the before conditions. The analysis resulted in a decrease 

of 13-percent (with a standard error of 6-percent) for total multiple-vehicle crashes (i.e., all 

multiple-vehicle crash types and severities) and a decrease of 36-percent (with a standard error 

of 6-percent) for fatal and injury multiple vehicle crashes across all intersections. These results 

were significant at a 95-percent confidence level. Additionally, the study found statistically 

significant reductions in the frequency of fatal and injury multiple-vehicle crashes for both 

systems independently, while only the InSync system was shown to result in a statistically 

significant reduction in total multiple-vehicle crashes (at a 95-percent confidence level). 

Statistically significant reductions were found independent of the number of intersecting legs 

(three or four). The authors also performed an analysis of crash type proportions and found a 

2.9-percent increase in the proportion of head-on crashes (significant at a 95-percent 

confidence level). 

Khattak et al. performed a follow-on study of the effect of ASCT on injury severity, specifically 

focused on deployments of different products across different states.(102) The study used a naïve 

before-after analysis and a random parameters ordered probit model to examine crash and 

intersection data from 49 intersections in Virginia (3,836 crashes over 10 years) and 42 

intersections in Pennsylvania (722 crashes over 7 years) where one of two ASCT products had 

been deployed (the products were not identified by name). The sample size of fatal crashes was 

too small to analyze so fatal crashes were discarded from the analysis and crashes were 

grouped into three crash severity categories: suspected serious and suspected minor injury 

crashes (AB in KABCO); possible injury crashes (C in KABCO), and property damage only 

crashes (O in KABCO). An overall model of both states and both ASCT product types 

revealed that the presence of ASCT decreased the potential for suspected serious injury and 

suspected minor injury crashes by 5.24-percent and possible injury crashes by 9.91-percent, 

while increasing the potential for property damage only crashes by 15.15-percent. These results 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

71

were significant at the 99-percent level (standard errors were not reported by the authors). In 

analyzing crash types, the study found that rear-end crashes resulted in increased injury severity

in the presence of ASCT. The researchers hypothesized that this could be due to increased 

speeds encouraged by improved traffic flow following the implementation of ASCT. Individual 

models were estimated for each state, and these models showed that the differences between

the two states were negligible. Additionally, the results showed similar effects between the two

different ASCT products.

Stevanovic et al. performed a simulation-based study of surrogate safety performance measures 

related to the implementation of SCATS on 14 intersections in Utah.(103) The study involved 

feeding the vehicle trajectory results of a Vissim microsimulation into the SSAM, using TTC, 

PET, and conflict angle as surrogate measures of safety performance. The results showed that 

implementing SCATS led to an 11-percent reduction in total vehicular conflicts (all types), 

which was primarily driven by a reduction in rear-end conflicts. However, implementation of

SCATS increased the number of crossing conflicts (particularly for the side-street movements) 

by 28-percent. Standard errors and confidence levels were not reported by the authors. The 

researchers noted that most of these crossing conflicts occurred at the busiest intersection on 

the corridor, and also pointed to Gettman et al.’s study which reported that crossing conflicts

are generally among the less reliable SSAM estimates.(104) The study also attempted to validate 

the link between SSAM conflict identification and crash data but was unsuccessful due to a small 

sample size of crash data as well as construction activities and other confounding factors. 

Summary

The synthesis of safety research on ASCT uncovered several safety evaluations based on an 

analysis of crash data and one based on simulated safety surrogates (see table 10). Results

indicated that ASCT reduces both crash frequency and severity. The extent of reductions will 

likely depend on the operational conditions along the facility with ASCT both before and after

its implementation. As with some of the studies on traffic signal coordination, there were 

indications that the severity of some crash types could increase due to higher operating speeds 

that result from the improved operational performance of ASCT. A search of “adaptive” on the 

CMF Clearinghouse returned 25 different CMFs for “install adaptive traffic signal control.” Most

were three- and four-star, and came from the studies by Ma et al., Khattak et al., and Fink et al.

detailed above (CMF IDs 6856 – 6861, 9764 – 9781, 7922).

(99,100,101)

At the time of this report, FHWA Office of Safety R&D had initiated a safety evaluation of 

adaptive signal control technologies under Evaluation of Safety Improvements, Phase X. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

72

Table 10. Summary of safety performance evaluation findings for adaptive signal 

control technology.

Study Location Site type Method Findings

Ma et al., 

2014(99)

VA Urban signalized 

arterial

Before-after with 

EB 

• Overall CMF of 0.83 (17%

crash frequency decrease,

SE 5%, significant at 95%)

• Fatal and injury CMF of

0.92 (8% crash frequency

decrease, SE 8% not

significant)

Fink et al., 

2016(100)

Oakland 

County, MI

Urban signalized 

arterial

Naïve beforeafter, multinomial 

logit model of 

crash severity

• Decrease of 19.3% in

angle crash frequency

• No significant results for

overall or rear-end

crashes or crash severity

Khattak et al., 

2018(101)

Pittsburgh, PA; 

Montgomery 

Township, PA; 

Upper Merion 

Township, PA

Urban signalized 

arterial

Before-after with 

EB 

• Overall multiple-vehicle

CMF of 0.87 (13% crash

frequency decrease, SE

6%, significant at 95%)

• Fatal/injury multiplevehicle CMF of 0.64 (36%

crash frequency decrease,

SE 6%, significant at 95%)

• Increase of 2.9% in

proportion of head-on

crashes (significant at 95%)

Khattak et al., 

2019(102)

Pittsburgh, PA; 

Montgomery 

Township, PA; 

Upper Merion 

Township, PA; 

Winchester, VA; 

Stephens City, 

VA; Staunton, 

VA; Frederick, 

VA; York 

County, VA; 

Roanoke 

County, VA; 

Fauquier 

County, VA

Urban signalized 

arterial

Naïve beforeafter

ordered probit 

model of crash 

severity

All results significant at 95%:

• Decrease of 5.24% in

suspected

serious/moderate injury

crashes

• Decrease of 9.91% in

minor injury crashes

• Increase of 15.15% in no

apparent injury crashes

• Increase in severity of

rear-end crashes

Stevanovic et 

al., 2011(103)

Park City, UT Urban signalized 

arterial

Simulation-based 

surrogate study

• Decrease of 11% in overall

conflict frequency

• Increase of 28% in

crossing conflicts, but

heavily influenced by one

intersection
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

73

Transit Signal Priority 

General Description of Strategy: Transit signal priority (TSP) involves communications 

between transit vehicles and traffic signal controllers so that the controllers can alter signal 

timing to give priority to transit vehicles.

(105) The Signal Timing Manual: Second Edition, published 

as NCHRP Report 812, identifies the two most common TSP strategies: 1) extending a phase 

to allow a transit vehicle to pass (i.e., green extension) or 2) terminating conflicting phases to 

allow early service to the transit vehicle and reduced red time (i.e., red truncation).

(106)

Implementation of TSP is based on the assumption that transit vehicles achieve higher personthroughput and that preferential treatment would improve reliability. Both of these factors

could incentivize transit use, thus improving overall system efficiency.

While TSP can be implemented at isolated signalized intersections and have an effect on the

performance of those intersections, it is commonly applied to a series of signalized

intersections along a facility that serves a transit route.

Streets sometimes receive other transit preferential treatments in conjunction with transit

signal priority. These additional treatments, such as bus turn-outs or queue jump lanes, could 

alter the roadway cross section. 

Safety Performance Evaluations: Song & Noyce performed a before-after EB study of TSP 

implementation on 11 corridors in Washington over 13 years.(107) Nine of the 11 corridors 

showed a significant reduction in crash frequency due to TSP, while the other two showed a

slight increase. Overall, the study found a 13-percent reduction in total crash frequency (for all 

types and severities) (standard error of 1.8-percent), as well as a 16-percent reduction in no 

apparent injury crashes (standard error of 2.1-percent) and 5-percent reduction in fatal and 

injury crashes (standard error of 3.2-percent). All reductions were statistically significant at a

95-percent confidence level.

Shalah et al. estimated negative binomial regression models of the expected frequencies of 

transit-involved crashes and total crashes (i.e., all types) using data from signalized intersections

in Toronto, Ontario, Canada.(108) Model estimation results indicated that the presence of TSP at 

signalized intersections was associated with higher numbers of both transit-involved and total 

crashes. The authors advised caution in creating CMFs using their regression models because of

the cross-sectional study design and uncertainty in the interpretation of the results.(108)

Goh et al. conducted a safety performance analysis of various transit priority treatments along

four bus routes in Melbourne, Australia.(109) The study used three years of before data and one 

year of after data to conduct a before-after EB analysis to examine the trends in crash 

occurrence related to the implementation of TSP. The results showed decreases in overall

crash frequency on segments with TSP, but none of the results were significant at a 95-percent 

confidence level. A naïve analysis of crash data showed an 18-percent reduction in overall crash
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

74

frequency and a 31-percent reduction in fatal and serious injury crash frequency; however, 

these results were not significant at a 95-percent confidence level (they were significant at an 

80-percent confidence level). Standard errors were not reported by the authors. The results

showed that non-TSP bus priority improvements (such as exclusive bus lanes) may provide

greater safety performance benefits than TSP, though the analysis of crash types indicated that

adding bus lanes may increase pedestrian-related crashes (due to increased road-crossing

distance).

In another study, Goh et al. modeled the expected frequency of crashes involving buses using 

mixed-effects negative binomial models and back-propagation neural networks.(110) Model 

results indicated more than 50-percent fewer bus crashes (of all severities) along bus routes 

with bus preferential treatments (this result was significant at a 95-percent confidence level). 

Standard errors were not reported by the authors. The models did not include details about 

the segments and intersections along the routes and did not distinguish between the types of 

preferential treatments (which included TSP along with other strategies).(110)

The lack of these 

details, along with the cross-sectional study design, makes it challenging to create CMFs from 

the study results.

Nanzin et al. conducted a before-after EB study of eight tram routes in Melbourne, Australia.(111)

The study included 29 intersections where tram signal priority was implemented and 23 road 

sections where tram lane treatments were implemented. The analysis period consisted of five 

years of before data and two years of after data. Results indicated that the tram signal priority 

treatments reduced the total crash frequency (for all crash types and severities) by 13.9-

percent (standard error of 8.2-percent) at intersections, and the tram lane treatments reduced 

total crash frequency by 19.4-percent (standard error of 9.1-percent) along the study segments. 

This represented a 16.4-percent overall reduction along the eight tram routes (standard error 

of 6.1-percent).(111)The crash frequency reduction corresponding to tram signal priority was not 

statistically significant at a 95-percent confidence level (the result was significant at an 80-

percent confidence level). The crash frequency reductions corresponding to lane treatment and 

the overall effect were significant at a 95-percent confidence level.

Subsequently, Nanzin et al. performed another statistical analysis of tram crash frequency in 

Melbourne.(112) The study used a random effects negative binomial model to examine 1,177 

tram-involved crashes over five years. The model results indicated that route sections with a 

higher share of tram signal priority intersections were less likely to experience tram-involved 

crashes (this result was statistically significant at a 95-percent confidence level). The authors 

also noted distinct differences in signal priority implementation between Australia and other 

countries, particularly Canada, which may account for differences in results across studies.

Li et al. used microsimulation to explore the effects of various transit design alternatives, 

including TSP, on the safety performance of signalized intersections in Toronto, Canada.(113)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

75

Vehicle trajectories were collected from a PARAMICS microsimulation and then processed in 

SSAM to identify vehicle conflicts. The simulation results showed fewer total vehicle conflicts

(rear-end, angle, and side-swipe) occurred in the non-TSP scenarios; however, the differences

were minimal (all less than 3-percent) and not statistically significant. 

Summary

In summary, multiple safety evaluations of TSP appear in the literature (see table 11). Results

vary with respect to safety performance effects of TSP, but most studies indicate safety 

performance benefits. In some cases, results did not distinguish between effects of TSP and 

effects of other transit preferential treatments implemented in combination with TSP. Some 

studies focused on transit-involved crashes, while others considered crashes of all types. It is

unclear what mechanisms would lead to some of the observed reductions in crashes unrelated

to transit vehicles. A search for “transit priority” on the CMF Clearinghouse results in different

CMFs for various TSP installation scenarios (some include transit lane priority as well). The

CMFs are all three- and four-star, and are derived from works by Nanzin et al., Song & Noyce, 

and Shalah et al. as described above (CMF IDs 7273 – 7275, 9664, 9819, 9820, 2078 – 

2097).(107,108,111,112)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

76

Table 11. Summary of safety performance evaluation findings for transit signal 

priority. 

Study Location Site type Method Findings

Song & 

Noyce, 

2018(107)

Seattle, WA Urban 

signalized 

arterial

Before-after with 

EB 

• 9 of 11 corridors showed

significant reduction in crash

frequency, while 2 showed slight

increase.

• Overall crash frequency decrease

of 13% (SE 1.8%, significant at 95%)

• No apparent injury crash frequency

decrease of 16% (SE 2.1%,

significant at 95%)

• Fatal/injury crash frequency

decrease of 5% (SE 3.2%, not

significant at 95%)

Shalah et al., 

2009(108)

Toronto, 

Canada

Urban 

signalized 

arterial

Negative 

binomial 

regression

TSP associated with higher frequency of 

total and transit-involved crashes 

Goh et al., 

2013(109)

Melbourne, 

Australia

Urban 

signalized 

arterial

Before-after with 

EB 

• Decrease of 18% in overall crash

frequency (11% attributable to TSP)

• Decrease of 31% in fatal and

serious injury crash frequency

• No results significant at 95%

Goh et al., 

2014(110)

Melbourne, 

Australia

Urban 

signalized 

arterial

Negative 

binomial 

regression

• Decrease of 50% in crash frequency

along bus routes with buspreferential treatment (significant at

95%)

• Did not distinguish which routes

had TSP

Nanzin et al., 

2015(111)

Melbourne, 

Australia

Urban 

signalized 

corridor

Before-after with 

EB 

Overall crash frequency decrease of 

13.9% at intersections with tram signal 

priority (SE 8.2%, not significant at 95%)

Nanzin et al., 

2016(112)

Melbourne, 

Australia

Urban 

signalized 

corridor

Negative 

binomial 

regression

Route sections with a higher share of 

tram signal priority were less likely to 

experience crashes of all types and 

severities (significant at 95%)

Li et al., 

2017(113)

Toronto, 

Canada

Urban 

signalized 

corridor

Simulation-based 

surrogate study

No statistically significant results, but 

slight increase in total vehicle conflicts 

in TSP scenario
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

77

Truck Signal Priority 

General Description of Strategy: Truck signal priority (TkSP) is another form of signal 

priority that gives preferential treatment to large trucks. Trucks take longer to accelerate and

decelerate and can have detrimental effects on traffic flow, particularly in urban areas.(114)

Additionally, frequent accelerations and decelerations of large trucks can have negative effects

on pavement infrastructure, noise, and emissions, particularly on high-speed signalized

roadways.(114,115)

TkSP generally operates by providing green time extension when a truck is detected on the

approach. A common method for differentiating trucks from passenger vehicles is to use two 

separate loop detectors (or video detectors) spaced 20 to 30 feet apart and engage the TkSP 

extension when both detectors are occupied simultaneously. It is possible that this method

could simply capture two separate passenger vehicles, though at free flow speed the headway 

between them would likely be smaller than is typical.(116) Other methods that have been 

explored include vehicle-to-infrastructure (V2I) technology, vehicle-to-vehicle (V2V)

technology, model predictive control (MPC), and co-simulation optimization control.(114)

Summary of Safety Performance Evaluations: There are very few performance evaluations 

of TkSP reported in the literature, and no studies of its effects on safety performance. 

However, Mahmud contends that TkSP could result in safety benefits by potentially reducing 

the number of red light-running incidents involving trucks.(115)

There are no relevant results for truck signal priority in the CMF Clearinghouse. 

Queue Jump Lanes 

General Description of Strategy: A queue jump lane is a short bus-only lane at a signalized 

intersection that allows transit vehicles to bypass the queue of passenger vehicles.(117) Queue 

jump lanes are frequently combined with transit-specific signals or transit signal priority to allow 

buses to easily enter the traffic flow ahead of the queue.(118) Typically, green times for throughtraffic passenger vehicles are reduced by three to five seconds to enable this transit

movement.(119) Queue jump lanes are implemented to reduce delay to and improve service

reliability of transit vehicles, thereby incentivizing transit use. Queue jump lanes can be applied

at isolated intersections or a series of intersections along a facility.

Several geometric considerations play into the implementation of a queue jump lane. The

roadway cross-section must provide enough space for a dedicated bus lane. In some cases 

where right-turning traffic is minimal, the queue jump lane can be combined with the right-turn 

lane. The queue jump lane should be long enough to allow buses to access it when peak-hour 

general traffic queues are present at the intersection. The design of the queue jump can be 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

78

affected by the presence of either near- or far-side bus stops at the intersection. The Transit

Cooperative Research Program’s (TCRP) Bus Rapid Transit Practitioner’s Guide advises extra

signing and pavement marking due to potentially unexpected transit vehicle maneuvers.(119)

Summary of Safety Performance Evaluations: There are no known safety performance 

evaluations of queue jump lanes. A search for “queue jump” in the CMF Clearinghouse does 

not return any relevant results. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

79

Safety Warning Applications 

General Description of Strategy: Safety warning strategies provide drivers information about 

downstream conditions. This is intended to improve driver expectation of the downstream 

conditions and therefore mitigate potential negative safety effects of the conditions. There are 

several different forms of safety warning strategies. The most common are 1) intersection

warning, 2) curve warning, 3) queue warning, and 4) animal warning. Intersection warning 

strategies give motorists advance warning of upcoming stop- or signal-controlled intersections

or the presence of other vehicles on other intersection approaches. Curve warning systems

serve to make drivers aware of upcoming horizontal curves that may necessitate a speed

reduction for maneuvering comfortably within the travel lane. Queue warning strategies warn

travelers about downstream queues on facilities with frequent congestion.(120) Animal warning

systems may be placed at locations where vehicle-animal (particularly large mammal) crashes

are more frequent or more likely than normal and may include systems for detecting animal 

presence. These strategies can be implemented in a variety of ways, but most consist of some

sort of signage (static or dynamic), and many also include flashing beacons. 

Summary of Safety Performance Evaluations: Safety performance evaluations of safety 

warning applications cover a range of intersection warning, curve warning, queue warning, and 

animal warning strategies. 

Intersection Warning

Simpson & Troy studied the safety performance effectiveness of intersection conflict warning 

systems (ICWS) at 74 stop-controlled intersections in North Carolina.(121) ICWS use vehicle 

detection to trigger warning messages alerting motorists of incoming vehicles on an intersecting

approach. The specific form of ICWS examined by this study was “Vehicle Entering When 

Flashing” (VEWF) systems, which include actuated flasher sign assemblies at or near

intersections. The study used the before-after EB method, and results indicated a 6.8-percent 

reduction in total crash frequency (all types and severities) following installation of the VEWF 

systems (with a standard error of 2.2-percent). This decrease in crash frequency was 

statistically significant at a 95-percent confidence level.

Himes et al. performed an analysis of ICWS as part of an FHWA pooled-fund study of low-cost

safety strategies.(122) The study analyzed the effect of ICWS deployments in Minnesota, Missouri, 

and North Carolina on crash type and severity using the before-after EB method. The study 

examined 93 ICWS installation sites across the three states, with 66 coming from North

Carolina. The results across the three states indicate an overall safety performance 

improvement due to implementation of ICWS. The results for two-lane/two-lane intersections

showed a CMF of 0.73 (crash frequency reduction of 27-percent, standard error of 4-percent) 

for all crash types and all severities. The CMF for fatal and injury crashes was 0.70 (crash
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

80

frequency decreased by 30-percent, standard error of 5-percent) and the CMF for right-angle 

crashes was 0.80 (crash frequency decreased by 20-percent, standard error of 5-percent). The 

results for four-lane/two-lane intersections showed an overall CMF of 0.83 (crash frequency 

reduction of 17-percent, standard error of 6-percent). The CMF for fatal and injury crashes was 

0.80 (crash frequency reduction of 20-percent, standard error of 7-percent), and the CMF for 

right angle crashes was 0.85 (crash frequency reduction of 15-percent, standard error of 8-

percent). The CMFs were all statistically significant at the 95-percent confidence level, except 

for the CMF for right angle crashes at four-lane/two-lane intersections (which was significant at 

a 90-percent confidence level). Frequencies of nighttime crashes for two-lane/two-lane 

intersections and rear-end crashes for four-lane/two-lane intersections did not decrease by 

statistically significant magnitudes. The results indicated greater benefits than the study by 

Simpson & Troy had identified. The researchers contributed this to several factors, including 

the use of data from multiple states, the use of SPFs to account for changes in traffic volumes, 

and the use of annual multipliers to account for trends over time at the treatment sites. A 

disaggregate analysis indicated additional safety benefits for sites with intersection lighting and 

for sites with higher crash frequency in the before period. Additionally, the study performed an 

economic analysis using estimates for cost and service life, with the benefits based on total 

crash reduction. The benefit-cost (B/C) ratio was 27:1 for two-lane at two-lane intersections 

and 10:1 for four-lane at two-lane intersections.

Appiah et al. examined the effect of actuated advance warning systems at 26 signalized 

intersections in Nebraska.(123) Actuated advance warning systems combine the functionality of 

advance detection systems and advance warning systems to both extend green time and warn 

drivers as they approach a high-speed signalized intersection. The study used the full Bayesian 

(FB) method to analyze crash data from a 13-year period (all intersections had at least one year 

of before and after data). The results indicated a total crash reduction (all types and severities) 

of 8.2-percent (standard error of 5.8-percent). The frequency of injury crashes showed an 11.3-

percent reduction (standard error of 10.5-percent), and right-angle crash frequency decreased 

by 43.6-percent (standard error of 5.6-percent). Of these results, only the decrease in right 

angle crash frequency was statistically significant at a 95-percent confidence level (the decrease 

in total crash frequency was significant at an 80-percent confidence level). Reductions in the 

frequencies of rear-end crashes and heavy vehicle crashes were negligible. 

Schultz & Talbot evaluated an advance warning signal (AWS) system in Riverton, Utah.(124) The 

system consisted of an overhead sign, flashers, and advance video detection, and was installed in 

response to concerns about safety and pavement damage caused by large trucks coming to 

abrupt stops at three intersections along a corridor. The study used two years of before data 

and two years of after data to conduct a naïve before-after analysis. Results showed that 

observed crash rates (for all crash types and severities) decreased by an average of 10.9-

percent, including a 19.4-percent decrease at the key intersection on the corridor. Standard 

errors and confidence levels were not reported by the authors. Operating speeds were not 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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affected by the installation of the system. The authors noted that it may be difficult to make 

strong conclusions based on these results, as many different factors changed at the intersection 

over the course of the study period that could not be accounted for by the naïve before-after

approach. 

NCHRP Report 705, Evaluation of Safety Strategies at Signalized Intersections, presents safety

evaluations and CMFs for a variety of safety strategies at signalized intersections.(125)

Implementation of dynamic advance warning flashers was included in the list of Tier I priority 

treatments. The report involved a critical review of existing research, surveys of agencies, and

prioritization of strategies. Advance warning flashers were chosen as one of the top three 

priority strategies and further evaluation was conducted using data from Nevada, Virginia, and

North Carolina. A variety of analysis methods were used, including cross-sectional analysis, 

before-after with comparison group, and before-after with EB. The results showed a consistent

reduction in total crashes (all types and severities) across the three states, as well as reductions

in angle, fatal and injury, and heavy vehicle crashes. The CMF for total crashes (all types and

severities) was 0.81 (crash frequency reduction of 19-percent, standard error of 6-percent), and 

the CMF for fatal and injury crashes was 0.82 (crash frequency reduction of 18-percent, 

standard error of 8-percent). These CMFs were statistically significant at a 95-percent 

confidence level. An effect on rear-end crashes could not be clearly identified. 

Curve Warning

Lyon et al. performed a safety evaluation of two different curve warning treatments: in-lane 

curve warning pavement markings and chevron signs.(126) The study used the before-after EB

method and data from a variety of sites in Iowa, Kansas, Missouri, and Pennsylvania to examine 

specific crash types and severities. Results for in-lane curve warning pavement markings showed 

significant reduction in total crash frequency (all types and severities), equating to a CMF of

0.62 (crash frequency reduction of 38-percent, with a standard error of 9-percent). The 

reduction was due exclusively to the significant reduction in nighttime crashes, as neither the 

injury nor run-off-road categories (for both daytime and nighttime crashes) produced 

statistically significant results at a 95-percent confidence level. Results for chevron signs showed

a reduction in the frequency of total crashes, as well as injury and nighttime crashes, though 

none of the reductions were statistically significant at a 95-percent confidence level.

Montella studied the safety performance effects of various curve warning enhancements at 15 

sites in Italy.(127) The various improvements consisted of installation of chevron signs (five sites); 

installation of curve warning signs and chevron signs (six sites); and installation of curve warning 

signs, chevron signs, and flashing beacons (four sites). An example of a warning sign with flashing 

beacons is shown in figure 10. The study employed the before-after EB analysis method. 

Overall, for all 15 sites, the results indicated a 28.2-percent reduction (standard error of 7-

percent) in total crash frequency (all types and severities) due to the implementation of curve 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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warning enhancements. Excluding one site due to confounding issues of pavement skid 

resistance degradation resulted in a 39.4-percent reduction in total crash frequency (standard 

error of 7-percent). Results also showed that treatments were more effective for curves with 

smaller radii (defined by the authors as curves with radius of 300 meters or less). Total crash 

frequency reduction was 52.2-percent (standard error 8-percent) for small radius curves and 

25.4-percent (standard error 11-percent) for large radius curves. A disaggregate analysis 

showed statistically significant total crash frequency reduction for curve warning signs and 

chevron signs (40.8-percent, standard error 10-percent) and curve warning signs, chevron signs, 

and flashing beacons (47.6-percent, standard error 9-percent). The crash frequency reduction 

for installing only chevron signs was not statistically significant. However, all other listed results 

were statistically significant at a 95-percent confidence level.

 Source: FHWA

Figure 10. Photo. Curve warning sign with flashing beacon.(128)

Khan et al. performed a study of safety performance on horizontal curves in Wisconsin and 

used negative binomial modeling to estimate the relationship between crash frequency on 

curves and various geometric and signage factors.(129) The results indicated that fewer crashes 

occurred on curves with a curve sign (MUTCD W1-2) as opposed to a turn sign (MUTCD W1-

1). However, sign usage was not a significant factor in crash frequency on sharper curves, 

indicating that other factors may influence crash occurrence on sharper curves.

Srinivasan et al. produced an analysis of the safety performance effects of improved curve 

warning treatments for the previously-mentioned FHWA pooled-fund study regarding low-cost 

safety strategies.(130) The study examined curves on rural, two-lane roads (89 curves in 

Connecticut and 139 curves in Washington). Treatments included chevrons, horizontal arrows, 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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advance warning signs, and the introduction of fluorescent yellow sheeting. A before-after EB

analysis was performed, and the results revealed a statistically significant 18-percent reduction 

in fatal and injury crash frequency (standard error of 8.6-percent), as well as statistically

significant reductions in nighttime crash frequency (27.5-percent, standard error of 7.3-percent) 

and nighttime lane departure crash frequency (25.4-percent, standard error of 7.8-percent). 

These results were all statistically significant at a 95-percent confidence level. The results also

indicated that the treatments were more effective on sharp curves in Connecticut, but there 

was no clear trend regarding curve radius in Washington or when the data from the two states 

were aggregated. 

Queue Warning

Elvik et al. developed CMFs for changeable “Queue Ahead” warning sign installations on urban 

freeways by combining results from three international studies (Erke & Gottlieb, 1980; Cooper

et al., 1992; Persaud et al., 1995).

(131,132,133,134) The combined CMFs indicate a 16-percent 

reduction in the frequency of fatal and injury crashes and a 16-percent increase in the frequency

of no apparent injury crashes. Elvik et al. do not go into detail on the analysis methods used in

the individual studies, and both the Erke & Gottlieb and Cooper et al. studies are not available 

online. However, Elvik et al. do indicate that the combined CMFs for both fatal and injury

crashes and no apparent injury crashes are statistically significant at a 95-percent confidence 

level.

The Persaud et al. study consists of a safety evaluation of a system of 13 overhead changeable 

message signs used to warn motorists of downstream queuing and congestion on a 19-mile 

stretch of highway in Toronto, Canada.(134) The study defined conventional, collector, and 

express segments along the length of the corridor and used separate regression models to

estimate the safety performance of the freeway before and after the implementation of the 

queue warning system. The models focused on fatal and serious injury rear-end crashes, as 

these were the target crashes for the implementation of the system. The results indicated

reductions in fatal and serious injury rear-end crash frequency across express sections (18.3-

percent decrease, significant at a 95-percent confidence level), collector sections (27.7-percent 

decrease, significant at a 95-percent confidence level), and conventional sections (25.7-percent 

decrease, not significant at a 95-percent confidence level). The standard errors of the changes

in crash frequency were not reported by the authors. The study showed the effectiveness of 

the queue warning system in reducing the target crashes, as reductions in all non-target crashes

(i.e., non-fatal/serious injury, non-rear-end crashes) were minimal and not statistically significant

at a 95-percent confidence level. 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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 Source: FHWA

Figure 11. Photo. Portable dynamic end-of-queue warning sign in Wisconsin.(135)

Ullman et al. evaluated an end-of-queue (EOQ) warning system at a work zone on I-35 in 

Texas.(136) Figure 11 shows one type of portable queue warning sign for implementation in work 

zones. The EOQ warning system consisted of a portable system of radar speed sensors linked

to one or more portable changeable message signs and portable transverse rumble strips. The 

EOQ system was deployed upstream of nighttime lane closures where queues were expected

to develop. The authors implemented a variation of a before-during study design with the EB 

analysis approach: the base condition was a freeway segment with no work zone, and the two

treatment conditions were a work zone with no EOQ warning system and a work zone with an 

EOQ warning system. The authors developed the EOQ warning system CMF by comparing the 

relative safety performance of the work zones with and without the EOQ warning systems to

their no work zone conditions. The computed CMF attributed to EOQ warning system

deployment in work zones was 0.559 (a crash frequency reduction of 44.1-percent with a

standard error of 25.5-percent). This result was not statistically significant at a 95-percent 

confidence level (but was significant at a 90-percent confidence level). Additionally, crashes that

did occur tended to be less severe.(136)

 

Animal Warning

An example of a dynamic animal warning sign is shown in figure 12. Huijser et al. performed an

analysis of a similar animal detection and motorist warning system in Montana.(137) The system

was optimized to detect large mammals, particularly elk. The study used ten years of crash and

wildlife mortality data to perform a naïve before-after analysis. The preliminary results from the

before-after analysis indicated a 66.7-percent decrease in vehicle-large mammal crashes on the 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

85

road section served by the warning system. Also, after system implementation, the vehicle-large 

mammal crash frequency on the study section was 57.6-percent lower than on the control 

sections. However, a small sample size of crashes and only one year of after data limited the 

statistical significance of the results. Standard errors and confidence levels were not reported 

by the authors.

 Source: FHWA

Figure 12. Photo. Animal-activated dynamic warning sign in Arizona.(138)

Dai et al. examined the effectiveness of an animal detection and motorist warning system on a 

1.36-mile segment of U.S. 191 in Wyoming.(139) The system was geared toward preventing

crashes between vehicles and deer. The study used two years of before data and two years of 

after data to perform naïve before-after analysis with statistical hypothesis testing. The results

of the analysis indicated that there was no statistically significant difference in crash frequency

or crash rate following the implementation of the warning system, though a slight downward

trend was identified. However, the researchers noted that more post-implementation crash

data should be collected before conclusions are drawn. 

Summary

The safety warning application synthesis effort uncovered safety performance research in four 

general areas: 1) intersection warning, 2) curve warning, 3) queue warning, and 4) animal 

warning. Table 12, table 13, table 14, and table 15 summarize these studies. It was common to

see application of the before-after with EB study design and analysis approach for evaluations 

across all four areas, and statistically significant reductions in crash frequencies for at least some 

of the crash types and severities considered. Unlike several other TSMO strategies, safety

warning applications have been the focus of key safety-related funding mechanisms for both 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

86

implementation and evaluation, including the Evaluation of Low-Cost Safety Improvements 

Pooled Fund Study (ELCSI-PFS). 

A search of “warning” in the CMF Clearinghouse results in many CMFs, with three-, four-, and 

five-star CMFs derived from works summarized above for the following applications:

• Elvik et al.(131):

o Install changeable crash ahead warning signs (CMF ID 75).

o Install changeable “Queue Ahead” warning signs (CMF IDs 76 and 77).

o Install changeable speed warning signs for individual drivers (CMF ID 78).

• Lyon et al.(126):

o Install in-lane curve warning pavement markings (CMF IDs 9167 – 9171).

• Appiah et al.(123):

o Installation of an actuated advance warning dilemma zone protection system at

high-speed signalized intersections (CMF IDs 4853 – 4857).

• Montella(127):

o Install a combination of chevron signs, curve warning signs, and/or sequential

flashing beacons (CMF IDs 1851 – 1885).

• Himes et al.(122):

o Install an intersection conflict warning system (ICWS) with a combination of

overhead and advanced post mounted signs (various messages) and flashers

(CMF IDs 8432 – 8476) .

• Srinivasan et al.(125,130):

o Install dynamic signal warning flashers (CMF IDs 4198 – 4202).

o Install new fluorescent curve signs or upgrade existing curve signs to fluorescent

sheeting (CMF IDs 2431 – 2435).

o Install chevron signs on horizontal curves (CMF IDs 2436 – 2440).

Table 12. Summary of safety performance evaluation findings for intersection 

warning applications. 

Study Location Site type Method Findings

Simpson & 

Troy, 2012(121)

NC Stop-controlled 

intersection

Before-after with 

EB 

Decrease of 6.8% in overall 

crash frequency (SE 2.2%, 

significant at 95%)

Himes et al., 

2016(122)

NC; MN; MO Stop-controlled 

intersection

Before-after with 

EB 

Two-lane at two-lane 

intersections:

• Overall CMF of 0.73

(crash frequency

decrease of 27%, SE 4%,

significant at 95%)

• Fatal and injury CMF of

0.70 (crash frequency

decrease of 30%, SE 5%,

significant at 95%)
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Study Location Site type Method Findings

• Right-angle CMF of 0.80

(crash frequency

decrease of 20%, SE 5%,

significant at 95%)

Four-lane at two-lane 

intersection:

• Overall CMF of 0.83

(crash frequency

decrease of 17%, SE 6%,

significant at 95%)

• Fatal and injury CMF of

0.80 (crash frequency

decrease of 20%, SE 7%,

significant at 95%)

• Right-angle CMF of 0.85

(crash frequency

decrease of 15%, SE 8%,

significant at 90%)

B/C ratio of 27:1 for 

installation of ICWS for twolane at two-lane and 10:1 for 

four-lane at two-lane 

intersections

Appiah et al., 

2011(123)

NB Signalized 

intersection

Before-after with 

full Bayesian (FB)

• Overall crash frequency

decrease of 8.2% (SE

5.8%, not significant at

95%)

• Injury crash frequency

decrease of 11.3% (SE

10.5%, not significant at

95%)

• Right-angle crash

frequency decrease of

43.6% (SE 5.6%,

significant at 95%)

Schultz & 

Talbot, 

2008(124)

Riverton, UT Urban signalized 

arterial

Naïve before-after • Overall crash rate

decrease of 10.9%

• Overall crash rate

decrease of 19.4% at key

intersection on corridor

Srinivasan et 

al., 2011(125)

NV; VA; NC Signalized 

intersection

Cross-sectional 

analysis, beforeafter with 

comparison 

groups, beforeafter with EB

• Overall CMF of 0.81

(crash frequency

decrease of 19%, SE 6%,

significant at 95%)

• Fatal/injury CMF of 0.82

(crash frequency

decrease of 18%, SE 8%,

significant at 95%)
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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Table 13. Summary of safety performance evaluation findings for curve warning 

applications.

Study Location Site type Method Findings

Lyon et al., 

2017(126)

IA; KS; MO; PA Roadway curve Before-after with 

EB 

• In-lane curve warning

pavement markings

resulted in overall CMF

of 0.62 (crash frequency

decrease of 38%, SE 9%,

significant at 95%)

• Chevron signs did not

generate significant

results, but showed slight

decrease in overall crash

frequency

Montella, 

2009(127)

Naples, Italy Roadway curve Before-after with 

EB 

• Decrease of 28.2% in

overall crash frequency

(SE 7%) (39.4%, SE 7%

when excluding one

outlier). Significant at

95%.

• Decrease in total crash

frequency of 52.2% (SE

8%, significant at 95%) for

small radius curves,

25.4% (SE 11%, significant

at 95%) for large radius

curves

• Decrease in crash

frequency for installation

of curve warning and

chevron signs (40.8%, SE

10%, significant at 95%)

and curve warning signs,

chevron signs, and

flashing beacons (47.6%,

SE 9%, significant at 95%),

but no significant results

for installation of chevron

signs only

Khan et al., 

2012(129)

WI Roadway curve Negative binomial 

regression

Fewer overall crashes 

occurred on curves with a 

curve sign as opposed to a 

turn sign

Srinivasan et 

al., 2009(130)

WA; CT Roadway curve Before-after with 

EB 

• Decrease of 18% in

fatal/injury crash

frequency (SE 8.6%,

significant at 95%)
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Study Location Site type Method Findings

• Decrease in overall

nighttime crashes (27.5%,

SE 7.3%, significant at

95%) and nighttime lane

departure crashes

(25.4%, SE 7.8%,

significant at 95%)

Table 14. Summary of safety performance evaluation findings for queue warning 

applications.

Study Location Site type Method Findings

Elvik et al., 

2012(131)

Numerous 

international 

locations

Urban freeway Synthesis of 

previous studies

• Decrease of 16% in

fatal/injury crash

frequency

• Increase of 16% in PDO

crash frequency

Persaud et al., 

1996(134)

Toronto, 

Canada

Urban freeway Regression 

modeling

Fatal/serious injury rear-end 

crash frequency:

• Decrease of 18.3% on

express freeway

(significant at 95%)

• Decrease of 27.7% on

collector freeway

(significant at 95%)

• Decrease of 25.7% on

conventional freeway

(not significant at 95%)

Ullman et al., 

2016(136)

Central TX Urban and rural 

freeway

Before-after with 

EB 

Overall CMF of 0.559 for 

EOQWS (crash frequency 

decrease of 44.1%, SE 25.5%, 

not significant at 95%)

Table 15. Summary of safety performance evaluation findings for animal warning 

applications.

Study Location Site type Method Findings

Huijser et al., 

2009(137)

Yellowstone 

NP, MT

Rural highway Naïve before-after Decrease of 66.7% in vehiclelarge mammal crashes 

Dai et al., 

2008(139)

Trapper’s Point, 

WY

Rural highway Naïve before-after

with statistical 

hypothesis testing

No significant results due to 

implementation of animal 

warning system
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Work Zone Management and Temporary Traffic Control Applications 

General Description of Strategy: Work zone management is a holistic approach to the 

planning and implementation of roadway construction operations, addressing road users, 

vehicles, workers, infrastructure, the environment, and the management system itself.(140)

Specific work zone management applications include strategies that pertain to traffic control, 

traveler information, worker and traveler safety, or other facets of work zone operation.(141)

Driving conditions in work zones differ from normal driving conditions, and often vary 

significantly from work zone to work zone. The U.S. road infrastructure system has reached a 

stage of increased maintenance and repair operations of existing facilities and decreased

construction of new facilities. This increases the exposure of the traveling public to work zones.

Work zone management strategies are implemented to mitigate potential negative effects 

associated with road construction activities, while at the same time accommodating

construction and maintenance operations aimed at improving the safety, operations, and 

condition of road infrastructure in the long run. The geometric effects of work zone

management applications are often site-specific given the individual characteristics and needs of 

the construction project, though they commonly involve the closure of one or more lanes of 

the roadway. 

Summary of Safety Performance Evaluations: There has been no exploration of the overall 

effect of work zone management on safety performance. This is because work zone 

management involves a wide spectrum of specific strategies and techniques and is difficult to

broadly assess. Researchers have studied the safety performance effects of work zones and 

factors associated with work zone crashes from a variety of perspectives.

(142,143,144,145,146,147,148,149)

These studies have established the consensus that work zones, on average, increase the 

potential for crashes. Safety performance evaluations of specific work zone management and

temporary traffic control strategies aimed at mitigating this increased crash potential have also

been conducted. 

Some agencies increase police patrolling and enforcement in active work zones in an effort both

to alert drivers of upcoming queues or congestion and to increase compliance with reduced

speed limits. Chen & Tarko performed a survey of construction project managers in Indiana and 

analyzed various other data to model crash frequency in work zones and contributing 

factors.(150) The crash frequency model showed that presence of enforcement in work zones

resulted in a 41.5-percent reduction in crash frequency for all types and severities. This analysis

relied on the project managers’ memory of police presence. Standard error and confidence 

level were not reported by the authors.

Tudor et al. examined the effectiveness of an Automated Work Zone Information System

(AWIS) in preventing crashes at a work zone in Arkansas.(151) The AWIS included traffic sensors 

and changeable message signs and was designed to detect queues, report delay times, and 

suggest speed reductions to travelers. The study compared the work zone equipped with AWIS 

to two other work zones in Arkansas without AWIS, using one year of crash data. The results

showed that the observed rate of fatal crashes at the work zone where AWIS was
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91

implemented was significantly lower than at the other two sites (2.2, compared to 3.2 and 3.4). 

The rate of rear-end crashes at the AWIS site was in between the crash rates of the other two 

sites (33.7, compared to 43.2 and 29.5). The standard errors and confidence intervals were not 

reported by the authors. This type of with-without study design using observed crash rates 

tends to produce less reliable results. 

Li & Bai used crash data to evaluate the effectiveness of various temporary traffic control 

methods including the use of flagger/officer control, stop signs/traffic signals, flasher devices, “no 

passing zone” control, and center and edge lines.(152) Logistic regression was used to investigate 

the effects of these strategies on crash severity and the occurrence of various crash report 

descriptors such as “disregarded traffic control,” “inattentive driving,” and “followed too 

closely.” The study used two months of fatal and injury work zone crash data, which consisted 

of 29 fatal crashes and 626 injury crashes, from the Kansas DOT crash database. The results 

showed that using flagger control in a work zone could lower the likelihood of a fatal crash 

occurring by 4-percent. Implementing “no passing zone” control could lower the fatal crash 

likelihood by 3-percent. Additionally, the presence of center/edge lines in a work zone could 

lower the likelihood of a fatal crash by 2-percent. However, the presence of stop signs or traffic 

signals in work zones increased the risk of fatal crashes, particularly in the case of “followed too 

closely” crashes. These results were all statistically significant at a 95-percent confidence level 

(standard errors were not reported by the authors).

Ullman & Schroeder synthesized case studies regarding the use of work zone ITS 

technology.(153) One of the case studies detailed the implementation of smart work zone traffic 

monitoring systems at two work zones in Indiana. Both sites were rural interstates, and agency 

officials were concerned about increased crash potential due to unanticipated congestion 

because of lane closures and work zone activities. The researchers noted that the capacity of 

the facility adjacent to work zones was reduced, even when lanes were not closed. This could 

be due to decreased lane or shoulder widths, or other factors. Different ITS systems were 

deployed at the two locations, but both involved sensors for monitoring traffic conditions and 

changeable message signs for communicating information to drivers. Preliminary naïve beforeafter analysis using one year of before data (with the work zone active but no ITS technology)

and one year of after data (with both the work zone and the ITS technology active) showed a 

14-percent decrease in the frequency of queuing-related crashes and an 11-percent decrease in

injury crashes, despite a 52-percent increase in the number of days with temporary lane

closures. Standard errors and confidence levels were not reported by the authors.

Tarko et al. modeled a wide variety of factors surrounding work zone operations for the Super 

70 project on I-70 in Indiana, particularly the safety effects of various work zone traffic 

management strategies.(154) The study used crash data, geometric data, traffic data, weather data, 

and enforcement data, and employed a binary logistic regression crash likelihood model. The 

findings showed that the most significant safety improvement resulted from rerouting heavy 

vehicles (those over 13 tons) to alternate interstate routes. Other significant improvements 

came through traffic management strategies such as increased police enforcement and reduced 

speed limits. Together, these two aspects contributed to an estimated safety benefit of around 
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92

100 work zone crashes prevented during the nine-month duration of the project. Widening the 

inner and outer shoulders contributed to an incremental additional safety benefit. A naïve 

before-during study using a negative binomial test for change in crash frequency and 27 months 

of before data and 9 months of during data showed that the frequency of all types and severities 

of crashes on I-70 decreased during the Super 70 project, but crashes increased on I-456, a ring 

road which experienced increased traffic due to the construction operations.

Many agencies perform extensive roadway construction operations during overnight hours to 

minimize the effects of construction-related lane closures on mobility during peak travel hours. 

There are some concerns that decreased visibility at night may contribute to increased risk of 

crashes in nighttime-operating work zones. However, some studies indicate that nighttime 

work has no effect on safety performance or may even result in a net benefit. Turochy et al. 

analyzed work zone-related crashes in Alabama from 2007 to 2014 and developed an ordered 

probit model to explore crash severity in work zones.(155) The study showed that crash severity 

increased from 6 PM to 6 AM when compared to the 6AM to 6PM periods. The researchers 

attributed the increase in severity to reduced visibility as well as the possibility of traveling at 

higher speeds during nighttime hours than during the day when roads are more congested. 

However, the study was not able to identify whether the work zones were active during these 

nighttime crashes. Ullman et al. studied nighttime work zone activity in Texas in a similar 

manner.(156) Analysis of three years of crash records showed mixed results depending on how 

frequently the district in question performed nighttime work zone operations. However, as 

with the study by Turochy et al., crash data only indicated whether the crash occurred in a 

work zone, but not whether the work zone was active. In response to this, the researchers 

performed a more focused investigation of eight projects, reviewing project records to 

determine when work zones were active. The results showed that crash frequency for all types 

and severities increased by 48.7-percent in inactive nighttime work zones compared to the 

same segments prior to work zone implementation. This change was significant at a 95-percent 

confidence level. Alternately, exploration of active nighttime work zones showed that most of 

the sites experienced no significant increase in crash frequency compared to the inactive 

nighttime status, except at two locations where substantial queuing was associated with a much 

greater increase in crash frequency. The study also identified a divide in previous literature 

regarding the effect of nighttime work zones on traffic safety, with some studies concluding that 

crash frequency increased while others showed the opposite. Ullman et al. attributed this to 

confounding effects due to the lack of information surrounding nighttime work zone activity 

status.

NCHRP Report 627, Traffic Safety Evaluation of Nighttime and Daytime Work Zones, contains 

extensive research on the issue of nighttime work zone safety performance effects.(157) The 

study investigated work zone crashes in New York, California, North Carolina, Ohio, and 

Washington using the before-after EB method. The results of the data analysis showed that 

nighttime work zone operations do not significantly affect crash frequency, as the increase in 

crash frequency resulting from a lane closure was nearly identical between daytime and 

nighttime work. Additionally, the study accounted for lower traffic volumes during nighttime 

hours. Crash frequency (for all crash types and severities) increased by 66-percent during 
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93

daytime work zone operations (standard error of 7.3-percent) and by 61-percent during 

nighttime work zone operations (standard error of 5.7-percent) when compared to times when

work zones were not present. These results were both statistically significant at a 95-percent 

confidence level. However, the difference in crash severity between daytime and nighttime 

work zone operations was not statistically significant at a 95-percent confidence level. 

Interestingly, the only category that showed a statistically significant difference between daytime 

and nighttime conditions was when the work zone was inactive (and no lane closures in effect). 

Inactive nighttime work zones represented an increase in crash frequency of 23.7-percent 

(standard error of 2.9-percent) compared to 12.7-percent during the daytime (standard error 

of 1.4-percent) (for all crash types and severities). These results were significant at a 95-percent

confidence level. The study was unable to identify the cause of this difference, though the 

researchers speculate it may be due to degradations in the geometric conditions relative to

non-work zone conditions, or reduced driver expectancy and increased likelihood of

drowsiness or impairment. Overall, the change in crash occurrence was most noticeable for no

apparent injury crashes, indicating that while work zones increase crashes, they tend to be less

severe. In analyzing crash type, the researchers found that the proportion of rear-end and 

fixed-object crashes increased at night, but side-swipe crashes were not affected by time of day.

NCHRP Research Report 869, Estimating the Safety Effects of Work Zone Characteristics and

Countermeasures: A Guidebook, documents the results of different analyses related to 

understanding the safety effects of work zones and selected countermeasures.(159) The report 

shows that congestion and queueing caused by work zones both significantly contributed to 

increased crash potential, especially in the case of freeway work zones. Researchers analyzed

data from a multi-state database of interstate work zones to develop CMFs for different work 

zone features (e.g., shoulder widths, lane closures, lane shifts, etc.), but the analysis did not yield

statistically significant results for any specific geometric features. 

The report also contained a safety analysis of combined end-of-queue warning system

(EOQWS) and portable rumble strip (PRS) implementation during nighttime construction 

activities on 96 miles of I-35 in Texas. Using Bluetooth sensors to identify queues and a beforeafter EB analysis, the study revealed a 53-percent crash reduction due to implementation of 

EOQWS and PRS (standard error of 30.1-percent). This result was not statistically significant at

a 95-percent confidence level (it was significant at a 90-percent confidence level). Additionally,

results showed that only 16-percent of crashes resulted in fatal and injury (KABC) severities

when queues were present and safety treatments were deployed, compared to 50-percent 

when queues were present but safety treatments were not deployed. 

Summary

Several studies have evaluated individual aspects of the safety performance effects of work 

zones and various related strategies. Most of these studies employed statistical modeling of

crash data, and two used the before-after EB study design. The findings varied due to the 

specific details of the study but generally indicated that police enforcement, AWIS, flagger
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

94

control, edge lines, rerouting of heavy vehicles, and other work zone safety countermeasures 

decrease overall crash frequency. Results were mixed regarding the safety performance effects 

of nighttime work zone operations. 

A search for “work zone” on the CMF Clearinghouse results in about 80 different CMFs that

relate to specific aspects of work zone implementation, in the following categories:

• Active work with no lane closure (compared to no work zone).

• Active work with temporary lane closure (compared to no work zone).

• Implement left-hand merge and downstream lane shift (Iowa weave).

• Increase work zone duration.

• Increasing the inside shoulder width inside the work zone by one foot.

• Increasing the outside shoulder width inside the work zone by one foot.

• Modify work zone length.

• TLTWO (two-lane, two-way traffic operation – crossover closures) in work zones.

Most of the CMFs come from NCHRP Report 627, with others from Tarko et al.(156,157,154) Both 

studies are described above. Additionally, NCHRP Research Report 869 contains an extensive 

catalogue of work zone-related CMFs.(159)

Table 16. Summary of safety performance evaluation findings for work zone 

applications.

Study Location Site type Method Findings

Chen & 

Tarko, 

2012(150)

IN Freeway 

work zone

Survey of construction 

managers and project 

records, negative binomial 

model of crash frequency

Presence of enforcement in 

work zones resulted in 

41.5% decrease in overall

crash frequency

Tudor et 

al., 2003(151)

Lonoke 

County, AR

Freeway 

work zone

Comparison of observed 

crash data

Fatal crash rate at AWIS site 

was significantly lower than 

at comparison sites

Li & Bai, 

2009(152)

KS Work zone 

with 

temporary 

traffic 

control

Logistic regression on 

crashes that occurred

• Use of flagger control,

flashing devices, and/or

center and edge lines

significantly reduce

likelihood of a crash

being fatal by 2-4%

(significant at 95%)

• Use of stop signs or

traffic signals increased

risk of a crash being

fatal/injury (significant at

95%)

Tarko et 

al., 2011(154)

Indianapolis, 

IN 

Freeway 

work zone

Logistic regression of 

crash occurrence

Rerouting heavy vehicles, 

increased police 

enforcement, and decreased 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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Study Location Site type Method Findings

speed limits resulted in 

reduction of around 100 

crashes

Turochy et 

al., 2017(155)

AL Work zone Ordered probit Crash severity in work 

zones increased during 

overnight periods

Ullman et 

al., 2004(156)

TX Work zone Cross-sectional analysis Most nighttime work zone 

sites experience no 

significant increase in crash

frequency, though some that 

experienced substantial 

queueing showed increased 

crash frequency

Ullman et 

al., 2008(157)

NY; CA; 

NC; OH; 

WA

Work zone Before-after EB • Increase in overall

daytime crash frequency

of 66% (SE 7.3%) and

overall nighttime crash

frequency of 61% (SE

5.7%) during nighttime

(difference between the

two not significant at

95%)

• Change in crash

frequency most notable

for no apparent injury

crashes

Ullman et 

al., 2018(159)

TX Urban and 

rural 

freeway 

work zone

Before-after EB • Decrease of 53% in

crash frequency due to

EOQWS and PRS (SE

30.1%, not significant at

95%)

• 50% of crashes were

categorized as “severe”

when queues were

present and safety

treatments were not

deployed, compared to

16% when queues were

present and safety

treatments were

deployed
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Traffic Incident Management Strategies 

General Description of Strategy: Traffic incident management (TIM) is any combination of 

strategies designed to lessen the time required to respond to and clear traffic incidents. It

consists of planned and coordinated multi-disciplinary processes to detect, respond to, and 

clear traffic incidents so that traffic flow may be restored as quickly as possible. Public and 

private sector partners in a TIM program include law enforcement, fire and rescue, emergency 

medical services, transportation agencies, public safety communications, emergency 

management, towing and recovery, hazardous materials contractors, and traffic information 

media.(160)

Effective TIM reduces the duration and effect of traffic incidents, thereby reducing delay caused 

by incidents. TIM also seeks to improve the safety of people involved in incidents, emergency 

responders, and other road users.

Implementing traffic incident management strategies does not require changes to roadway

geometrics, but there are some roadway features (e.g., wider shoulders) that may support

improved TIM. Studies reviewed in the next section do indicate that some road geometric

features influence primary incident duration, which in turn likely influences the probability of a

secondary crash. 

Summary of Safety Performance Evaluations: TIM is designed to reduce emergency 

response times, enabling quicker treatment of injured road users. Quicker clearance of

incidents by police and other responders results in less exposure. Several transportation

agencies and researchers have attempted to quantitatively evaluate the benefits and costs of 

incident management programs.(161,162,163,164,165) However, none of these studies have presented

defensible, crash-based safety performance assessments. Most rely on broad assumptions of 

crash reduction, traffic diversion, and other factors of incident management without specific 

empirical evidence. This is likely because incident management often manifests in a broad, 

campaign-based approach which can involve numerous individual strategies and is difficult to 

perform experimental analysis on.

Safety performance evaluations of TIM that have linked incident duration to the occurrence of 

secondary crashes have found more success. Secondary crashes are crashes that occur in the 

timeframe beginning with the time of incident and within the boundaries of the incident scene 

or resulting queue. Various studies indicate that secondary crashes account for 14- to 25-

percent of all crashes.(163,164,166,167) The identification of secondary crashes is complex and rarely 

noted on crash reports, making the assessment of these crashes challenging. Multiple TIM

studies in the literature related the chances of secondary crashes to incident characteristics, 

including incident duration. 
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Khattak et al. developed an online tool called the Incident Management Integration Tool (iMiT)

that dynamically predicts incident durations, secondary incident occurrence, and associated 

incident delays.(168) The authors developed the prediction models using data provided by 

Hampton Roads Traffic Operations Center that spanned January 2004 through June 2007. The 

authors used a binary logit model to estimate the probability of a secondary incident as a 

function of primary incident characteristics, including incident duration.(168) However, due to

data limitations it was not possible to validate the tool’s secondary incident occurrence

predictions. 

Goodall developed methodologies to 1) identify crashes as secondary using third-party travel 

time data and 2) estimate the probability of secondary crash occurrence based on the duration 

of the primary incident, congestion levels, and the total number of vehicles that encounter the 

incident or its queue.(169) The study involved collecting data from the entire 75-mile I-66 

corridor in Virginia and used a binary logit model to estimate the probability of secondary crash

occurrence.

Yang et al. used logistic regression to estimate the probability of a secondary crash but made 

the case that traditional binary logit models could be problematic because of “class imbalance”

(i.e., only a relatively small number of primary incidents result in a secondary crash). They

instead implemented rare event logistic regression and found the probability of a secondary 

crash to increase with primary incident duration.(170)

Latoski et al. demonstrated how to incorporate reductions in secondary crashes along with

other benefits into a B/C analysis of a freeway service patrol incident management strategy.(166)

Summary

Results of these studies indicate the probability of a secondary crash increases by 1.2 to 3.2 

percent for every additional minute of primary incident duration. However, given the widely

varying characteristics of TIM programs and of crash incidents themselves, all of the existing

studies relied on assumptions in developing estimates of safety performance effects. However, 

these studies do provide a foundation for further examination of incident duration, TIM 

response time, secondary incident occurrence, and benefit-cost relationships.

Searches of “TIM,” “incident management,” “incident,” and “clearance” in the CMF 

Clearinghouse do not return any results. 
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Table 17. Summary of safety performance evaluation findings for traffic incident 

clearance strategies. 

Study Location Site type Method Findings

Khattak et al., 

2012(168)

Hampton Roads, 

VA

Urban freeway Binary logit Produced tool for predicting 

incident duration, secondary 

incident occurrence, and 

associated delays. Validation 

of secondary incident 

prediction was not 

conducted due to data 

limitations.

Goodall, 

2017(169)

Northern VA Freeway Binary logit Probability of secondary 

crash increases 1% for every 

additional 2-3 min of primary 

incident duration

Yang et al., 

2014(170)

NJ Freeway Rare event logistic 

regression

Probability of secondary 

crash increases 1.2% for 

every additional min of 

primary incident duration

Latoski et al., 

1999(166)

Northwest IN Freeway Logistic regression Hoosier Helper program 

could reduce secondary 

crash likelihood by 18.5% in 

winter and 36.3% in other 

seasons 
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CHAPTER 3 — RELATIONSHIPS BETWEEN TRAFFIC 

OPERATIONAL PERFORMANCE AND SAFETY PERFORMANCE 

INTRODUCTION

TSMO strategies generally have known and quantifiable direct effects on measures of traffic 

operations. By affecting traffic operations, these strategies may also have indirect effects on 

traffic safety performance, measured by the frequency and severity of crashes. Background 

discussion in Chapter 1 of this report noted, however, that many safety performance analysis 

methods based on an analysis of crash data incorporate average traffic volumes – specifically 

AADT – and do not consider the safety performance effects of the changes in and interactions 

between speed, flow, capacity, and density.

To gain a comprehensive understanding of the potential for TSMO strategies to influence safety 

performance outcomes, it is necessary to understand the relationships between traffic 

operational and safety performance measures. This chapter summarizes the methods and 

findings of a representative set of studies that have investigated the relationships between traffic 

operations and safety performance using these two distinct approaches. Such studies generally 

fall within one of two distinct categories: 1) those that focus on traffic operational and safety 

performance measured over long time periods (e.g., annually); and 2) those that focus on traffic 

operational and safety performance measured in real-time (e.g., on the order of a few minutes). 

Studies in the first category tend to relate annual traffic volumes (i.e., AADTs) or 

representative hourly traffic metrics (e.g., flows, speeds, densities) over the course of a year to 

annual crash frequencies. Studies in the second category seek to relate traffic metrics measured 

at specific locations and times with the crash potential at these locations and times. 

ANNUAL RELATIONSHIPS

The most common method used to relate safety performance and traffic metrics at the annual 

level is to estimate safety performance functions (SPFs). FHWA’s Safety Performance Function 

Development Guide: Developing Jurisdiction-Specific SPFs defines SPFs as crash prediction models 

that relate the number of crashes of different types and severities at a site to the characteristics 

of that site.(171) The SPF Development Guide notes that some SPFs may include only traffic volume 

and segment length (if the site type is a segment) as predictor variables. Others may include 

additional site characteristics, such as cross section geometry, alignment, and roadside

characteristics. SPFs can be developed for various spatial scales (e.g., National, State, regional) 

for individual or aggregated crash types, and for any specific level or combination of severity. 

The main advantage of this approach is that using annual data helps to address some of the 

randomness associated with crash occurrences. The limitation is that these models also require 

annual estimates of traffic metrics (e.g., annual traffic volumes), and therefore do not reflect 

safety performance effects of traffic fluctuations on sub-annual time scales.
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100

Typical Data 

The data required to estimate an SPF depends on the facility and site type. For roadway

segments, the minimum required data for each segment and year are AADT, the number of 

subject crashes reported, and the segment length.(171) Additional data, such as those describing 

the cross-section geometry, alignment, or roadside can also be included in the SPF to improve

its predictive ability. For intersections, the required data are the number of observed crashes

and AADT on the major and minor roadways for each intersection-year considered. Like 

roadway segments, other intersection-specific features (such as presence of crosswalks, leftturn phasing, or exclusive turn lanes) can be collected and integrated directly into an SPF or

incorporated as adjustment factors into a larger predicted method.

Some published research has incorporated traffic metrics into SPFs. Zhou & Sisiopiku used data 

from a 16-mile segment of I-94 in Detroit, Michigan to investigate the relationship between

crash rate and volume-to-capacity (V/C) ratio.(172) Volumes were obtained from hourly traffic

counts taken from count stations on the study segment, while segment capacities were 

estimated using the 1994 Highway Capacity Manual (HCM) methodology.(173) Lord et al. 

incorporated hourly traffic flow, density, and V/C data into their crash frequency models.(174)

Both studies collected crash, roadway, traffic volume, density, and capacity data for their

models. The methods used by the researchers and results of these studies will be discussed

later in this chapter. 

Methods 

Observed crash frequencies are count data which show a right skew when plotted and thus are 

often modeled using count regression. The most typical count model is the Poisson model, 

which assumes that the count data follows a Poisson distribution in which the mean value is 

equal to the variance. However, crash data are usually over-dispersed, meaning that the 

variance of crash frequency is greater than the mean crash frequency. To accommodate this, 

analysts typically use negative binomial regression models to estimate SPFs.(175) The use of 

negative binomial regression results in the proportional relationship between crash frequency

and AADT described in figure 13, which is a power function, where the regression coefficient

ββAAAAAAAA describes the shape of the relationship. A constant term, an adjustment for length, and 

other adjustment factors are also included in a standard SPF, with the standard functional form 

for a segment SPF shown in figure 14. 
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101

Figure 13. Equation. Proportional relationship between crash frequency and AADT 

in traditional SPFs.

Figure 14. Equation. Standard functional form of a segment SPF.

Where:

Npr,y,z – the annual number of predicted crashes of type y and severity z, in crashes per year;

Lβ – segment length, in miles, with a regression coefficient βL which is typically fixed as one;

AADT βAADT – AADT, in vehicles per day, with a regression coefficient βAADT which can be less 

than, equal to, or greater than one;

Πeβ i – a series of adjustment factors for geometry, roadside characteristics, and other features, 

typically referred to as CMFs; and

Cy,z – a local calibration factor, which can be used to adjust an SPF for crashes of type y and 

severity z to local conditions.

The power function relationship between crash frequency and AADT can be modified using 

CMFs. An example of this is in the freeway segment predictive method from the HSM, which 

includes a CMF for “high volumes.”(176)As shown in figure 15, the CMF is a function of a 

regression coefficient (α) and the proportion of daily traffic occurring during hours in which the 

volume exceeds 1,000 vehicles per hour per lane (P). The baseline of 1,000 was selected 

because freeway speeds typically drop once volume exceeds 1,000 vehicles per hour per 

lane.(177,178) The increase in density and reduction in speed is hypothesized to be correlated with 

increases in multiple-vehicle crash frequency. 

Figure 15. Equation. HSM CMF for high volume on freeway segments.

There are two notable exceptions to modeling the crash frequency-AADT relationship via the 

power function. The first was in Bonneson et al., which used the Hoerl function to describe the 

relationship on freeway ramp segments.(177) The Hoerl function relates crash frequency and 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

102

AADT via figure 16 where β0 and β1 are regression coefficients. The authors found the Hoerl 

function produced a better-fitting model than the traditional power function.

Figure 16. Equation. Proportional relationship between multiple-vehicle crash 

frequency and AADT for ramp segments in the HSM. 

The second notable exception to the power function relationship was in Kononov et al., who 

pointed out that the power function is commonly used because analysts often limit themselves 

to the readily available predictive capabilities of their selected software package.(179) To 

challenge the power function, the authors used a neural network to analyze five years of 

freeway segment crash data from California, Colorado, and Texas, developing a relationship 

between crash rates (crashes per mile per year, labeled APMPY on the y-axis in figure 17) and 

AADT. Regardless of State and for both six-lane and eight-lane cross sections, the authors 

found that a sigmoid shaped function (illustrated in figure 17) best represented the data. Such 

results may be transferable to other locations or may only be applicable to the locations 

studied. The transferability of SPFs across space (i.e., locations) and time remains an area of 

ongoing research.

© 2019 SAGE Publications. 

Figure 17. Graphic. The Sigmoid-shaped SPF estimated for Colorado six-lane 

freeways.(179)
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As noted previously, some authors have incorporated traffic metrics into SPFs. Zhou & 

Sisiopiku plotted the relationship between average V/C ratio and crash rate to estimate a bestfit curve describing that relationship.(172) It is important to note that crash rate assumes a linear 

relationship between crash frequency and traffic volumes. Research has shown that this 

assumption is false in many cases. Crash rates are therefore being phased out as a reliable 

metric for safety performance. 

Lord et al. developed models of annual crashes observed during specific hours (12:00-1:00, 

1:00-2:00, etc.) on specific days (weekdays, Saturdays and Sundays) for freeway segments in 

Quebec.(174) Three different model forms were considered: crash frequency as a function of 

hourly traffic volume (figure 18), crash frequency as a function of volume and density (figure 

19), and crash frequency as a function of volume and V/C ratio (figure 20). To better 

understand the effect density and V/C ratio have on crash frequency, the authors fixed traffic 

volume as an offset variable (forcing the model to relate traffic volume and crash frequency 

linearly). Models were estimated assuming a negative binomial distribution and using a 

Generalized Estimating Equation procedure. Capacity for each segment was calculated using the 

procedure described in the 2000 edition of the HCM.

(178) 

Figure 18. Equation. Predicted annual crash frequency for a freeway segment as a 

function of hourly flow.

Figure 19. Equation. Predicted annual crash frequency for a freeway segment as a 

function of hourly flow and density.

Figure 20. Equation. Predicted annual crash frequency for a freeway segment as a 

function of hourly flow and V/C ration.

Where:

Npr = predicted annual crash frequency for the freeway segment;

L = segment length, in kilometers;

F = hourly traffic volume for the segment, in vehicles per hour; and 
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β0 , β1 = estimated regression coefficients for the models. 

For each functional form, Lord et al. estimated seven models.(174) For both rural and urban 

contexts, the authors estimated models predicting the frequencies of all crashes, single-vehicle 

(SV) crashes, and multiple-vehicle (MV) crashes. Additionally, the authors developed a model for 

fatal and suspected serious injury (KA) crash frequency on rural freeways. 

Notable Results 

The HSM contains numerous SPFs estimated using the power function. The coefficient β AADT in 

figure 14 varies across SPFs in the HSM, with some having β less than one, some equal to one, 

and some greater than one: 

• Where β is less than one, the elasticity of the crash frequency-AADT relationship decreases

as AADT increases, meaning a one-percent increase in AADT results in a larger increase in

crash frequency when AADT is low compared to when AADT is high.

• When β is equal to one, the elasticity is uniform as AADT increases.

• Finally, when β is greater than one, the elasticity increases as AADT increases.

This β value helps to explain the effect of additional traffic on crash frequency and varies based 

on the type of crash being modeled. Consider two hypothetical SPFs: one that predicts SV 

crash frequency and another that predicts MV crash frequency. As AADT increases, an errant 

vehicle is less likely to depart the roadway without striking another vehicle and more likely to 

strike another vehicle. In this scenario, β would be less than one for the SV crash SPF and 

greater than one for the MV SPF.

Bonneson et al. showed how changes in traffic density affect the types of crashes observed by 

developing a “high volume” CMF for freeway segments in California, Maine, and Washington

(figure 15).(177) This study found that for MV crashes, the coefficient α is positive, which 

indicates crash frequency is likely to be higher than under base conditions. α is negative for SV 

crashes, which indicates crash frequency is likely to be lower than under base conditions. Figure 

21 provides a visual representation of this CMF as a function of the proportion of daily traffic 

that occurs while hourly traffic volume is greater than 1,000 vehicles per hour per lane (less 

than 1,000 vehicles per hour per lane is considered the base condition). These findings imply 

that as density on the freeway segment increases, MV crash frequency tends to increase, and SV 

crash frequency tends to decrease.
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105

 Source: FHWA

Figure 21. Graphic. High volume CMF for the freeway segment predictive 

methodology in the HSM1. 

Lord et al. found a similar effect with their data and models.(174) The coefficients in figure 18

through figure 20 relating the subject traffic metrics to predicted annual crash frequency varied 

by traffic metric and crash type.

Coefficient estimates for the flow, density, and V/C models in figure 18 through figure 20 are 

summarized in table 18. As one example finding, increases in density and V/C ratio are 

associated with decreases in SV crash frequency and increases in MV crash frequency, both of 

which are expected results, given that as the number of vehicles on the roadway increase, 

errant vehicles are more likely to strike another vehicle than have an SV crash. Figure 22

through figure 24 illustrate the relationships estimated by Lord et al. for rural freeway 

segments; the results were similar in shape for urban segments.(174) Lord et al. pointed out the 

differences in results between the “All” models and the sums of the multiple-vehicle and singlevehicle models (MV+SV). Note that the “All” models predict fewer crashes as volume, density, 

and V/C increase than the sum of the individual MV and SV models. Lord et al. cite this as 

evidence supporting Mensah and Hauer’s proposed modeling approach, which suggested 

disaggregated SPFs by crash types where appropriate.(180) Ultimately, Lord et al. recommend 

1 FI = fatal and injury crashes
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drawing conclusions based on the combined models (MV+SV), which indicate increases in 

volume, density, and V/C ratio are all associated with increases in the frequency of total 

(MV+SV) crashes.

Table 18. Summary of rural model coefficients (standard errors) for the models 

developed by Lord et al.(174) 

β1 All KA SV MV 

Figure 

18– Flow

0.542

(0.071)

0.354

(0.111)

0.412

(0.061)

1.156

(0.158)

Figure 

19– 

Density

-0.209

(0.038)

-0.294

(0.061)

-0.294

(0.033)

0.056 

(0.039) 

Figure 

20– V/C

-3.666

(0.808)

-5.116

(1.808)

-5.216

(0.713)

0.964 

(0.680) 

Table 19. Summary of urban model coefficients (standard errors) for the models 

developed by Lord et al.(174) 

β1 All SV MV 

Figure 

18– Flow

0.355

(0.125)

-0.660 

(0.980) 

1.085

(0.085)

Figure 

19– 

Density

-0.017

(0.007)

-0.077 

(0.067) 

0.012

(0.002)

Figure 

20– V/C

-1.957

(0.538)

-4.965

(0.690)

0.375

(0.134)
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107

© 2005 Elsevier. 

Figure 22. Graphic. Predicted crash frequency per kilometer for one freeway lane 

as a function of traffic flow.(174)

© 2005 Elsevier. 

Figure 23. Graphic. Predicted crash frequency per kilometer for one freeway lane 

as a function of vehicle density.(174)
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108

© 2005 Elsevier.

Figure 24. Graphic. Predicted crash frequency per kilometer for one freeway lane 

as a function of V/C ratio.(174)

This concept of relating traffic volume, density, and safety performance was also captured in the 

neural network SPFs estimated by Kononov et al.(179) On the AADT-crash frequency plot, the 

authors defined three regions of AADT (see figure 25):

1. Sub-critical zone.

2. Transitional zone.

3. Super-critical zone.

In the sub-critical zone, traffic is generally free-flowing, and increases in AADT result in

moderate increases in crash frequency. The relationship becomes more elastic after reaching a 

“critical density” point, so a change in AADT results in a steeper increase in crash frequency. 

The authors theorize that in this zone, the mix of higher vehicle speeds with the increasing 

density of traffic results in a more rapid increase in the probability of a crash occurring. Finally, 

a second “super-critical density” point is reached where overall vehicle speeds are reduced and 

density is high. For densities larger than this second critical point, the elasticity of the AADTcrash frequency relationship is reduced. At this point, traffic is congested, speeds are low, and 

the probability of crashes, while high, is not nearly as sensitive to increases in traffic. 
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© 2019 SAGE Publications. 

Figure 25. Graphic. Six-lane freeway SPF for Colorado with zone labels based on 

critical density.(179)

Kononov et al. also compared these three AADT zones to freeway level of service (LOS) using 

the methodology in the Highway Capacity Manual.

(178) Freeway LOS is based on vehicle speeds, 

traffic flow, and density. LOS and crash frequency were plotted in figure 26. The figure reveals 

that LOS A and B are associated with the sub-critical zone, LOS C occurs near the density at 

which the elasticity of crash rate begins to increase, LOS D and E are associated with the 

transitional zone, and LOS F is associated with the super-critical zone. The comparisons 

between LOS and predicted crash frequency are summarized in table 20. It is notable that the 

average slope for LOS D and E is more than double the next steepest average slope (LOS C).

© 2019 SAGE Publications. 

Figure 26. Graphic. Relating the six-lane freeway SPF to level of service.(179)
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Table 20. Comparing freeway LOS with predicted crash rates for six-lane freeways 

in Colorado, adapted from Kononov et al.’s plot in figure 26.

(179)

LOS LOS Description2

AADT Range 

from the 

Model 

[thousand 

vehicles per 

day]

Predicted 

Crash Rate 

Range [crashes 

per mile per 

year] 

Change in 

Crash Rate per 

one thousand 

vehicle per day 

change in 

AADT

A or B 

Vehicles able to freely 

maneuver in the traffic stream

or 

Slight restrictions on vehicle 

maneuverability within the 

traffic stream

55 – 67.5 12 - 17 0.40

C Noticeable restrictions on 

vehicle maneuverability 67.5 – 94.2 17 - 29 0.47

D or E 

Noticeable decline in speeds 

and strict limitations on vehicle 

maneuverability 

or 

Traffic is near capacity and 

highly volatile to even minor 

disruptions

94.2 – 142.1 29 - 76 0.97

F Congested conditions with a 

breakdown of traffic operations 142.1 – 190 76 - 94 0.38

REAL-TIME RELATIONSHIPS

Studies that are focused on the relationship between safety performance and traffic metrics in 

real-time are based on predicting the probability of a crash occurring for a given set of traffic 

conditions. Models are developed by merging crash data, roadway data, and high-resolution 

traffic operations data acquired from various detection systems. This approach has the 

advantage of providing information about what specific operational states are most closely 

associated with crash events. Some skepticism has been expressed as to the usefulness of these 

2 As defined in the 6th edition of the Highway Capacity Manual.(170)
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111

models given reporting errors around the time of crash. However, these models are mostly

developed for urban freeway facilities which are likely to have minimized reporting errors

because of factors such as the presence of traffic management centers that track incidents and 

the fact that urban freeways have high traffic volumes, which increases the likelihood of crashes

being reported at or near the time of the crash. 

Typical Data 

The real-time models are typically developed by integrating crash data with freeway detector

data (usually from loop detectors). Crash data typically include crash type, severity, date, 

location, and time, while operational data include vehicle speeds, traffic flow, and occupancy at

regular intervals (e.g., every 5 or 15 minutes).(181,182,183,184,185) Some efforts have also included data

on real-time weather conditions at the time of the crash.(186,187) 

Methodology 

In the annual methods previously discussed, observed crash frequencies are count variables. 

Generally, one crash at most occurs at the shorter time intervals required for real-time 

analysis. Thus, the variable being modeled is often a binary variable where 1 represents a crash

occurrence for a given set of conditions and 0 represents no crash. The principal statistical 

analysis approach used for these real-time models is the binary logit model. This model can be

used to estimate the probability of an event occurring or not based on a series of independent 

variables.(188) In safety analyses, traffic data over the short analysis period are used to predict the 

probability of a crash occurring within that period or not. This contrasts with the annual 

approach, which predicts the number of crashes expected to occur within a year. The 

traditional form of the binary logit model is provided in figure 27. As an example of this 

approach, Lee et al. used a binary logit to model the real-time likelihood of a crash on Toronto

freeway segments.(187)

Figure 27. Equation. Typical functional form of a binary logit prediction the 

probability of a crash occurring.

Where:

Pcrash – the probability of a crash occurring.

Xi

β – a vector of independent variables (Xi) and associated regression coefficients (β ).
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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Abdel-Aty et al. incorporated the binary logit into a matched case-control analysis of seven 

months of crash and loop detector data from 13 miles of Florida freeway.(181) The analysis was 

meant to identify relationships between traffic operations and the risk of a crash occurring. For 

every crash that occurred, the authors identified 11 cases with similar characteristics (time of 

day, day of week, season) where a crash did not occur to create the population against which 

the crash characteristics could be compared. For each crash, traffic data in five-minute intervals 

were obtained from loop detectors both upstream and downstream of the crash location. 

Three traffic variables were considered for this analysis: traffic volume, traffic speed, and 

detector occupancy. Detector occupancy is the proportion of a fixed amount of time during 

which a vehicle is sensed by a detector. It is a surrogate for density, where occupancy is 

proportional to the product of density and vehicle length.(189)For each traffic variable, the 

authors reviewed the effect of the average, standard deviation, and coefficient of variation 

(calculated as the standard deviation divided by the average). 

This case-control binary logit approach has been used in other studies. Hourdos et al. used the 

approach to model crash risk on a section of I-94 in Minneapolis/St. Paul, Minnesota.(186) AbdelAty et al. used this approach to produce a histogram comparing average speed with crash 

frequency. The histogram revealed a two-peak distribution, with a proportion of the data 

centered at 25 mph and the rest of the data centered at 60 mph (see figure 28).(182) The 

researchers saw this as indicating the need for two separate models for low (0 to 35 mph) and 

high (40 to 80 mph) speeds. 
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 © 2019 SAGE Publications. 

Figure 28. Graphic. Histogram of freeway crashes based on average vehicle speed 

across all three lanes at the loop detector immediately upstream of the crash 

location (Station F).(182)

Pande et al. incorporated a spatial component, using the matched-case modeling approach to 

develop spatial risk maps.(183) Three-dimensional contour maps were developed for each 

predictive variable showing locations within a series of loop detectors and across a given 

amount of time when crash probability is highest as a result of changes to a traffic metric.

The binary logit model can also be used to estimate the probability of a crash resulting in an 

injury given that the crash occurred. Golob et al. used freeway loop detector data to develop 

eight traffic metrics and identify correlations between these characteristics and various crash 

outcomes.(185) A binomial logit model was used to correlate these traffic characteristics with the 

likelihood of a crash resulting in an injury (compared to property damage only). Additionally, a 

multinomial logit model was developed to describe the crash type; specifically, to differentiate 

between the chances of the crash type being sideswipe, rear-end, or hit-object (compared to an 

overturn or broadside crash).

Neural networks were also used to analyze crash occurrence using this real-time framework. 

Pande et al. used random forests and multilayer perceptron neural networks (MLPNN) to 

identify relationships between traffic operation conditions and rear-end crash occurrence.

(184) 
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Notable Results 

The modeling efforts described above all had similar results. Abdel-Aty et al. found the biggest 

predictors of real-time crash probability around a subject location were the coefficient of

variation of speed (CVS) downstream of the location and the occupancy upstream of the 

location (based on loop detectors within a quarter-mile upstream and downstream).

(181) Figure 

30 is a box-and-whisker plot that compares the differences in CVS, defined in figure 29,

between crash and non-crash events. Figure 31 provides the box-and-whisper plot for 

occupancy. For both CVS and occupancy, it is clear from the figures that increased rates are 

associated with increased crash probabilities.

Figure 29. Equation. Coefficient of variation of speed at loop detector I.

Where:

CVSi – coefficient of variation of speed for loop detector i. 

σi – standard deviation of speed at loop detector i.

μi – average speed at loop detector i.

 © 2019 SAGE Publications. 

Figure 30. Graphic. Comparison of coefficient of variation of speed at the 

downstream detector for non-crash and crash events.(181)
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 © 2019 SAGE Publications. 

Figure 31. Graphic. Comparison of occupancy at the upstream detector for noncrash and crash events.(181)

In a similar study, Hourdos et al. found that the traffic characteristics associated with an 

increase in crash probability include:(186)

• Transitions from uncongested flow to congested flow at the study section;

• Increased uncongested speed upstream of the study section;

• Transition from low speed variance to high speed variance in the right lane; and

• Increased range of speeds in the right lane.

In general, the Abdel-Aty et al. and Hourdos et al. results were like the results of an analysis 

performed by Lee et al. on a section of freeway in Toronto.(187) Lee et al. found that the 

likelihood of a crash increases as speed variance increases, density increases, hourly volume 

increases, and weaving increases.(187) 

Pande et al. found upstream density, downstream speed, and downstream coefficient of 

variance of volume to be significant predictors of crash occurrence.

(184)

When Abdel-Aty et al. improved upon the 2004 study using significantly more data, the 

researchers developed separate models for low-speed conditions and high-speed conditions.(182)

For crashes under low-speed conditions, the model identified statistically significant correlations 

between the risk of a crash and the coefficient of variance for speed as well as average 

occupancy both upstream and downstream of the crash. As both increased, indicating higher 

frequencies of stopping and starting from congestion, the risk of a crash occurring increased. 
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The authors interpreted this as being representative of the likelihood of rear-end crashes 

occurring under congested conditions.

The model for crashes under high-speed conditions had different results. Increased occupancy 

upstream of the crash was found to be correlated with a decrease in the likelihood of a crash, 

while increased occupancy downstream was associated with an increased likelihood of a crash. 

Increased volume upstream of the crash was associated with increased crash probability, while 

increased volume downstream of the subject loop detector was associated with decreased 

crash probability. Ultimately, it was theorized by the authors that the combined occupancy and 

volume characteristics are associated with a congestion-causing event occurring downstream 

and propagating towards the location of the crash (i.e., a shockwave). Where congestion is 

already present (high occupancy upstream of the crash), it is less likely a crash would occur.

Pande et al. used the matched-case control modeling approach to develop spatial risk maps.(183)

They developed three-dimensional contour maps for each predictor variable showing the 

location within a series of loop detectors and the amount of time into the future when a crash 

is most likely to occur. Figure 32 is one of the plots developed by the authors. As the contour 

map gets darker, the chances of a crash increases. Stations B thru H indicate a series of loop 

detectors along a highway segment, with Station F being the study station, stations G and H 

being downstream, and B through E being upstream. The x-axis indicates time into the future. 

In practical terms, figure 32 shows that, based on increases in CVS, crash probability is highest 

at or just downstream of the subject loop detector (detector F) within the next five minutes. 

From here, traffic managers can take actions to either reduce crash potential (such as 

introducing speed harmonization countermeasures) or to prepare responses (e.g., relocating 

freeway incident patrols to the area of this loop detector). Note that the research discussed in 

this section did not consider crash severity.
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 © 2019 SAGE Publications. 

Figure 32. Graphic. Contour map showing relative odds of a crash as a function of 

changes in coefficient of variation in speed along a segment of highway.(183)

Golob et al. used binary and multinomial logit models to relate traffic operations to discrete 

crash outcomes; the results are summarized qualitatively in table 21.(185) Scores were assigned 

to eight traffic operations factors that were derived from loop detector data. Golob et al. found 

that only some of these factors (summarized in table 21) were predictors for crash type and 

severity.

1. Congestion in Left and Middle Lanes – this score increases as density increases.

2. Traffic Volume – this score increases as traffic volume increases.

3. Synchronization Between Lanes – when traffic characteristics change across lanes, the

score increases as the changes are more aligned.

4. Perturbation in the Right Lanes – this score increases as congestion in the right lane

increases and speed variance increases.

5. Variance in Traffic Volume Across Lanes – this score increases as volume variance

across the lanes increases.

6. Relationship Between Volume in the Right Lanes and the Other Lanes – this score

increases as the variance in congestion levels between the right lane and the other lanes

is reduced.

7. “Systematic” Changes in Traffic Volume – score based on statistically significant changes

in traffic volumes, such as entering or exiting AM peak period.

8. Synchronization in the Left and Middle Lanes – the score increases as traffic operations

in the middle and inside lanes becomes more synchronized.
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Table 21. Correlations between traffic factors and the probability of crash severity 

and type as found by Golob et al.

3 (185)

Traffic 

Factor to be 

Increased

Probability of 

an Injury 

Crash

Probability of 

a Sideswipe 

Crash

Probability of 

a Rear-End 

Crash

Probability of 

a Hit Object 

Crash

1 Decrease Increase Increase Increase

2 Decrease Increase Increase Increase

3 Decrease Increase Increase Increase

4 Increase Increase Increase

7 Increase Increase Decrease

1*6 Decrease

1*7 Increase

2*2 Decrease Increase Increase

2*4 Decrease

3*3 Decrease Decrease Decrease

4*6 Increase Increase Increase Increase

5*5 Increase Increase

It is notable that the models indicate an inverse relationship between the probability of a crash 

resulting in an injury and the probability of a crash being a sideswipe or rear-end. This seems 

reasonable as these crash types commonly occur in low-speed, high-congestion scenarios and 

thus are likely to be of lower severity. Overall, while these findings are helpful, they provide no 

indication of whether a crash will occur based on traffic conditions, only the characteristics of 

the crash given that it does occur. Additionally, while the general direction of the correlations is 

presented in table 21 for simplicity, assessments of elasticities would help determine whether 

effects are large enough to form the basis of subsequent policy or management decisions.

3 Italics indicate the relationship was statistically significant at the 95-percent confidence level.
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KEY TAKEAWAYS

It is clear from the literature reviewed in this chapter that traffic flow, speed, and density are all 

related to safety performance. In general, increases in traffic volume are associated with 

increases in crash frequency. Lord et al. demonstrated that the rate at which SV crash 

frequency increases with traffic volume decreases while the rate at which MV crash frequency 

increases with traffic volume remains roughly constant.(174) Kononov et al. showed that the 

power function may not accurately describe the traffic volume-crash frequency relationship and 

showed different operational levels of service (thus different states of density) exhibit different 

safety performance profiles.(179) Bonneson et al. used a CMF to show that density affects the 

frequencies and relative frequencies of SV and MV crashes on freeway segments.(177) Lord et al. 

identified unique shapes that relate changes in density and V/C ratio with SV and MV crash 

frequencies.(174) For both traffic metrics, SV crash frequency reaches a peak and then steadily 

declines as density and V/C increase. The MV crash-density relationship varied based on area 

type, being best described as a power function with a coefficient greater than one for rural 

freeways and less than one for urban freeways for the range of densities observed on each 

respective facility type. The MV crash frequency-V/C ratio relationship was found to be a power 

function with a coefficient slightly greater than one for both rural and urban freeways. 

Various results showed that short-duration traffic operations observations can provide valuable 

predictions of crash likelihood. Significant increases in density as well as the coefficient of 

variation of speed are both associated with increased likelihood of a crash.(181,187) Additionally, 

freeway crash likelihood has been shown to differ based on average speed, with low-speed and 

high-speed traffic flows having different crash potential indicators.(182) Shockwaves have also 

been shown to be associated with increased crash probability.(186) Crash type and severity 

outcomes are likely to vary based on operational conditions, as evidenced by the models 

developed by Golob et al.(185) For practical purposes, these relationships provide valuable 

insights for agencies that can implement strategies to reduce crash potential, whether through 

reducing speed variance,(181,182,186,187) producing more uniform flow across lanes,(185) or mitigating 

the effect of shockwaves.(186)
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CHAPTER 4 — EFFECTS OF WEATHER AND WEATHER-RELATED

ROAD CONDITIONS ON SAFETY PERFORMANCE

INTRODUCTION

Many factors that characterize the driver, vehicle, and roadway environment may contribute to 

the occurrence of a crash. Of these, weather directly influences the roadway environment. 

Adverse weather can create a less forgiving driving environment with reduced road friction and

limited visibility, which can in turn affect the performance of the driver or the vehicle. In these 

ways, weather can adversely affect safety performance, which is measured in terms of the 

frequency and severity of crashes. FHWA reports that 21-percent of crashes on U.S. roadways 

are weather-related, meaning they occur in inclement weather (e.g., rain, snow, high wind, etc.)

or on pavement that is slick due to precipitation.(190) Road weather management is therefore a

key tactical program area falling under the incident/event management category of TSMO

strategies (see figure 1).

There are three broad categories of strategies that can be employed to mitigate the adverse 

safety performance effects of weather on roadways: advisory strategies, control strategies, and

treatment strategies.(191) Advisory strategies provide information to motorists on impending 

adverse weather events, which can influence trip planning and driving behavior. Examples of 

advisory strategies include 511 interactive telephone services, transportation agency web sites, 

and on-road motorist warning systems, such as variable message signs. Control strategies utilize 

roadway devices to permit or restrict traffic flow in adverse weather. Examples include access

control, speed management—such as variable speed limits— and signal timing. Treatment

strategies involve the addition of agents to the roadway to minimize or eliminate the effect of

weather. Examples include snowplowing, anti-icing, and deicing. There are numerous 

management, maintenance, and operations techniques that fall into these three categories. 

However, the effects on safety performance of both adverse weather and these mitigation 

techniques are not always well understood. To set the stage for identifying safety performance

analysis needs in this area, this chapter summarizes the analysis approaches and findings of a 

representative set of studies that have investigated the relationships between weather and 

safety performance, as well as the effect of various weather mitigation strategies.

DATA 

Weather Conditions 

The availability of data is a crucial first step in exploring and quantifying the effect of weather on 

safety performance. Weather data are available from a variety of sources, with different

applications and objectives in mind. To maximize the utility and transferability of analysis
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techniques, it is crucial to use weather data that is widely accessible.(192) The primary sources of 

weather data geared toward transportation applications are Road Weather Information 

Systems (RWIS). The main purpose of an RWIS is to efficiently process weather data and 

condense it into an actionable format to support decision-making. Often managed at the State 

level, an RWIS is comprised of Environmental Sensor Stations (ESS), a communications system, 

and a central data collection system. The ESS are placed in and near the roadway to collect 

atmospheric, pavement, and/or water level data. Newer systems may also contain vehicle-based 

mobile RWIS sensors. There are over 2,400 RWIS ESS owned by State transportation agencies 

across the U.S., with at least one ESS in 49 States and Washington, D.C.(193) Atmospheric data 

include air temperature, humidity, visibility, distance, wind speed and direction, precipitation 

type and rate, cloud cover, tornado or waterspout occurrence, lightning, storm cell location 

and track, and air quality. Pavement data include pavement temperature, freezing point, 

condition, chemical concentration, and subsurface conditions. Water data include water level 

near the roadway and tide level (for assessment of storm surge). RWIS stations also often have 

video data collection capability. A given RWIS may only collect a subset of these data elements, 

which is determined by agency needs and available resources. Ewan & Al-Kaisy surveyed 24 U.S. 

States and 2 Canadian provinces, and determined that most agencies consider pavement and air 

temperature, pavement condition, wind speed and direction, and precipitation information to 

be the most essential data.(194) Operational data, such as traffic speeds and volumes, are not 

widely collected at RWIS sites, though this is beginning to become more common. Figure 33

shows the various data components of RWIS, as well as some uses of each component.
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 © 2017 Montana Department of Transportation. 

Figure 33. Graphic. Various sensor and data components of RWIS.

(194)

The original purpose of RWIS was for winter maintenance support, though its use has 

expanded significantly in recent years to include traveler information, operations activities, and 

ITS applications. Additionally, agencies have begun to approach RWIS more systematically, with 

many defining protocols for the placement of ESS. In the past, some agencies used techniques 

such as geo-spatial analysis and thermal mapping for optimizing ESS placement, but most relied 

on personnel expertise. Most agencies are currently expanding their RWIS networks, enhancing 

the capabilities of existing data collection locations, and/or expanding mobile RWIS efforts.

There are many other secondary sources of weather data. Depending on the application, these 

sources may be more suitable than RWIS data for examining specific facets of weather and the 

relationship between weather and safety performance. The National Weather Service (NWS) 

and the National Oceanic and Atmospheric Administration (NOAA) collect a variety of general 

weather data through local weather forecast offices, weather satellites, and cooperative 

observer programs. Additionally, the Federal Aviation Administration (FAA), U.S. Geological 

Survey, Department of Agriculture, Forest Service, and Environmental Protection Agency, 

among others, have deployed their own weather systems. Many State DOTs collect their own 

weather data and reports, often in relation to road maintenance operations. They may employ 

vehicle-based sensors, such as snowplow automated vehicle location (AVL) data and images. 

Figure 34 and figure 35 depict snowplow image data used by the Iowa DOT to assess road 

conditions when investigating crashes. Additionally, there are numerous private weather data 
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vendors—called Value Added Meteorological Services (VAMS)—that collect various data 

elements that are often tailored to a specific purpose, region, or industry. 

 Source: FHWA

Figure 34. Graphic. Map showing location of crash and location of snowplow 

image.(195)

 Source: FHWA

Figure 35. Photo. Snowplow image, taken 11 minutes after the crash occurred.(195)

Finally, another frequent source of weather data are crash reports, which often include a field 

for law enforcement to record the weather or roadway condition at the time of the crash. This 

can help determine if weather was a contributing factor to a crash (i.e., if the crash was 

weather-related). Because of the ease of use, especially in the case of safety performance 
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125

studies which generally already have access to crash data, this is a very commonly-used 

resource. However, it is widely understood that these weather reporting fields (and crash

reports in general) often suffer from inaccuracies and incomplete data. This is a result of

subjectivity and the inconsistent interpretation of reporting fields involved in the process of

individual law enforcement officers completing crash reports. In some cases, for instance, the 

road surface condition field may reflect the conditions when the form was filled out, not when 

the crash occurred. Additionally, many law enforcement agencies use a shortened crash report

form in inclement weather, which provides much less detail of the crash than the normal 

report.

Data Integration 

The FHWA has recently developed the Weather Data Environment (WxDE) as part of the 

USDOT’s Data Capture and Management (DCM) Research Data Exchange. The WxDE is a 

web platform for collecting and sharing transportation-related weather data, with a focus on

connected vehicle applications. It aggregates data from RWIS and mobile RWIS installations, as

well as other data providers, and computes additional metrics. Prior to development of the

WxDE, FHWA Road Weather Management Program partnered with the USDOT ITS Joint 

Program Office to implement the Clarus Initiative, an eight-year effort to develop an integrated

surface transportation weather data management system. The initiative concluded in 2013, at 

which point 37 U.S. States and 4 Canadian provinces were participating and contributing data to 

the system.(196) The Clarus Initiative was a form of a mesonet, or regional network of weather

information formed by integrating observational data from a variety of sources. Mesonets are 

common in weather agencies such as the NWS, but the Clarus Initiative represented a major

step towards weather data integration with a transportation focus. The FHWA has also

supported the integration of weather data into Transportation Management Center (TMC) 

operations, including implementing weather alert systems.(197)

Many of the studies reviewed for and referenced in this synthesis involved data integration 

operations on a smaller scale. Often this constituted joining weather data with crash data, 

maintenance data, roadway geometry data, and/or transportation operations data. Techniques 

for performing this sort of data integration vary based on the scale and format of the data. Hans 

et al. recommended that advancement of spatial and temporal integration techniques could 

allow for more effective analysis of weather data in conjunction with traffic operations and 

safety data, specifically suggesting an opportunity to combine local speed monitoring with

weather data to identify unstable and changing conditions, and to facilitate after-action

assessment and investigation of conditions that may have influenced the occurrence of a 

crash.(195)
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ANALYSIS APPROACHES

Strong et al. identified two broad approaches used by studies seeking to explore relationships 

between adverse weather and safety performance.(198) Macroscopic studies examine large 

geographic areas and long time periods, which is often preferred for weather and safety 

research applications since both types of data are prone to significant noise. However, the 

simplicity of macroscopic studies can make it difficult to generate precise, meaningful 

conclusions from the analysis. Microscopic studies, on the other hand, concentrate on a much 

smaller study region, both spatially and temporally, and generally focus on a single aspect of the 

data, such as one weather variable, a subset of roadway segments in a region, or a crash type. 

Microscopic studies are generally less common in the field of weather and safety performance, 

though improvements in data availability are making them more prevalent. The main concern 

with microscopic studies, from a safety performance perspective, is that there must be a large 

sample of crash data to produce statistically reliable results. Andrey et al. warn against this 

issue, saying “the stochastic nature of collisions and heterogeneity of driver response... limit 

the utility of statistical models in explaining collision risk for small spatial or temporal units.”(199)

The distinction between macroscopic and microscopic studies is usually the scale on which they 

operate. Beyond that, there may be many similarities in analysis approaches between the two.

Researchers have employed a wide variety of methods to analyze various aspects of the 

relationship between weather and safety performance. One of the most common techniques in 

safety performance research is the before-after EB method (see Chapter 1 for further 

information). Veneziano et al. used the before-after EB method to examine the safety effects of 

fixed automated spray technology (FAST) systems for deicing, and Hans et al. used it to 

systematically prioritize sites based on weather-related crash occurrence.(200,201) 

Another common analysis approach used in numerous studies is the matched pair framework, 

by which a candidate event is compared to one or multiple control events to detect variations 

between the two. In the case of weather and safety performance, this means comparing crash 

data from a day or time period with adverse weather to a similar one with clear weather. In 

theory, this allows the researcher to control for time-dependent variables that may affect crash 

occurrence, and thus changes in the likelihood of crash occurrence can largely be attributed to 

weather. The notable caveat is that this method does not directly account for exposure (e.g., 

traffic volumes), which may change in response to certain weather conditions. Andrey et al. 

used the matched pair framework to examine long term trends in crash risk due to weather by 

comparing six-hour precipitation periods with six-hour control periods that were on the same 

day of the week, exactly one week apart, with clear weather.(202) Other studies have performed 

similar analysis at the day level. Strong et al. examined weather-related crash and injury risk in 

Canada by comparing crash data for storm event days with control days exactly one week 

apart.(192) Knapp et al. observed the effects of extreme weather by comparing storm days (here 

defined as days containing a weather event lasting at least four hours, with an average snowfall 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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intensity of 0.20 inches per hour or higher) with three or four control days (the same day of 

the week but with clear weather) in the same month.(203)

Statistical modeling techniques are also commonly used to predict crash occurrence and 

determine the factors that most contribute to it. A wide variety of modeling approaches have 

been used, even within the relatively specific realm of weather and safety performance. 

Arguably the most popular model for use with count data (of which crash frequency data is one 

variety) is the Poisson regression model. It has frequently been applied to crash data, including 

studies of weather and safety performance.(203,204) However, the Poisson model requires the 

mean of the count process to be equal to its variance.(205) Crash data frequently exhibit 

overdispersion, where the mean is less than the variance.(206) Yu et al. used Poisson regression 

to model mountainous freeway crash data in winter weather but employed random effects to 

counter the issue of overdispersion.(207) El-Basyouny et al. applied a multivariate lognormal form 

of the Poisson model for this same reason to study weather effects on crash types.(208)

While it may be possible to avoid some of the shortcomings of the Poisson model by altering its 

formulation, the negative binomial (NB) regression model presents a more straightforward 

solution. The NB model is suited to address the issues of non-normal distribution and 

overdispersion and has been used in numerous safety performance studies. There are several 

different variations of the NB model, and each adjusts the formulation of the model slightly to 

better address aspects of the data or model conditions. Some of these which have been applied 

to studies of weather and safety performance include the NB model with fixed effects,(209) the 

generalized NB model, and the zero-inflated NB model.(210) 

In addition to these two primary families of count models, numerous other statistical methods 

have been applied to safety performance studies in the context of weather. These include 

multinomial logit,(211) fixed and random parameter logits,(212) and Bayesian logistic 

regression.(213,214) In the case of logistic regression, the dependent variable must be binary. In 

safety performance studies, this often manifests in the modeling of a crash as either “occurring” 

or “not occurring”,(214) or a crash result being either “severe” or “non-severe”.(212) Some studies 

of the effect of weather on safety performance employ multiple models (either from the same 

or different families) and assess their relative effectiveness using tools such as Deviance 

Information Criteria or Akaike’s Information Criteria, which quantify the trade-off between 

goodness-of-fit and model simplicity.(211,212) Generally, the better-performing model depends on 

specifics of the data and study formulation. Additionally, some studies have attempted to utilize 

neural networks and other artificial information techniques such as the support vector machine 

model; while these methods have consistently been outperformed by other methods in 

estimating the relationship between weather and safety performance, they represent an area for 

future exploration.(211,212)
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FINDINGS TO DATE 

Effects of Weather on Safety Performance 

Most agency and research efforts focus on winter weather (i.e., snow and ice), though a

significant amount of research has also focused on the effects of rain and wet weather. Fewer

studies have dealt with other weather events such as fog and high winds, as these generally

represent a more isolated threat to traveler safety. The emphasis on winter weather is likely

because winter weather is seen as actively “treatable” using physical techniques. There are few

treatment strategies that can be applied in the presence of rain or high winds. But while it is

understood that winter weather can produce more threatening conditions, the majority of 

weather-related crashes in the U.S. occur on wet pavement (70 percent) and during rainfall (46 

percent).(190) This is due in large part to the fact that snow is only prevalent during certain times 

of the year and in certain regions of the country, while rain is more prevalent both spatially and

temporally. 

Eisenberg & Warner produced one of the seminal explorations of weather’s effect on crash 

likelihood and severity, studying crash and weather data from 48 U.S. States from 1975 to 

2000.(209) They concluded that, while injury and no apparent injury crashes increased in the 

presence of snow (compared to dry days), the number of fatalities during snow conditions 

decidedly decreased. Black & Mote analyzed crash severity and weather data for 13 U.S. cities, 

and concluded that while winter precipitation was associated with an increase in crashes over

dry days, the chance of a crash being fatal did not increase significantly during winter 

precipitation. The study results showed that winter precipitation resulted in a 19-percent 

increase in no apparent injury crashes (with a standard error of 0.51-percent) and a 13-percent 

increase in injury crashes (with a standard error of 0.51-percent), both of which were 

statistically significant at a 95-percent confidence interval, while fatal crashes showed no

statistically significant change.(215) This result has been supported elsewhere in the 

literature.(216,217,218) One explanation could be that drivers behave more cautiously in the 

presence of winter weather, decreasing speeds and increasing following distances, thus

decreasing the likelihood of fatal crashes. Strong et al. postulated that drivers may not reduce 

speeds enough in the presence of wet roads or rain, but perceive snowy conditions to be more

dangerous and adjust their behavior accordingly.

(198) As part of their study, Strong et al. 

synthesized previous research on the effects of road surface condition on driving speed and 

crashes.(198) A summary of the findings is shown in table 22. The crash adjustment factors 

represent the expected crash frequency under the given weather condition compared to the 

crash frequency observed under normal weather conditions. The speed adjustment factors 

depict the expected percent reduction in in speeds for each condition. These findings show a 

substantial increase in crash frequency in the presence of snow and ice (particularly in the case 

of “snow-covered,” “icy,” and “very icy” conditions). However, the speed adjustment factor
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129

shows that while drivers are compensating for adverse weather conditions by decreasing their 

speeds, these speed reductions do not reflect the magnitude of the associated increases in 

crash frequency. This indicates that drivers may, in general, be underestimating the level of 

crash risk due to adverse road surface conditions. For example, the adjustment factors imply 

that “very icy” road surface conditions exhibit double the increase in crash frequency of “icy” 

conditions, but drivers only decrease their speeds another two percent on average.

Table 22. Adjustment factors for crashes and average driving speed under various 

road surface conditions, as synthesized by Strong et al.(198)

Road Surface 

Condition

Crash Adjustment 

Factor (%)

Speed 

Adjustment 

Factor

Dry 100 1.00 

Damp 100 1.00 

Wet 150 0.96 

Chemically wet 150 0.96 

Lightly slushy 150 0.90 

Slushy 175 0.87 

Deep slushy 200 0.84 

Frost 370 0.94 

Dusting of snow 150 0.96 

Lightly snowcovered 210 0.89 

Snow-covered 870 0.84 

Lightly icy 200 0.94 

Icy 800 0.85 

Very icy 1600 0.83 

In addition to the possibility that drivers behave more cautiously in the presence of winter 

weather, another explanation for decreased fatal crashes in the presence of winter weather 

could be that winter precipitation influences more drivers to delay or even cancel trips, leading 

to lower traffic volumes during winter storms events. Variations in traffic volume significantly 

affect crash occurrence.(213) Indeed, research shows that both explanations may contribute to 

the noted decrease in fatal crashes in snowy conditions. Knapp et al. found that winter storm 

characteristics decreased traffic volumes by 29-percent (with a standard error of 3.4-percent) 

and vehicle speeds by 16-percent (with a standard error of 7.6-percent). Both results were 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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statistically significant at a 95-percent confidence level. Numerous other studies have reported 

a range of similar volume and speed decreases.(203) Yu & Abdel-Aty reported that high speed 

variation in the traffic flow on a roadway significantly increased the likelihood of severe crashes, 

where severe crashes were defined as crashes resulting in any fatality or injury (K, A, B, or 

C).(212) One exception to the decrease in fatal crashes in the presence of winter precipitation 

may be those involving vulnerable road users such as pedestrians, cyclists, and motorcyclists.(213)

Li & Fernie identified significant reductions in pedestrian signal compliance in cold and snowy 

weather, a potential safety issue that is exacerbated by decreased visibility (of both pedestrians 

and drivers) and increased vehicle stop distances due to icy and slick roads.(219) However, these 

groups may travel less in the presence of adverse weather, similar to those noted above for 

other motorized traffic.

Furthermore, Eisenberg & Warner noted in their study that the first snowy day of the year 

exhibited substantially worse safety performance in terms of fatal crashes, with a tapering effect 

on the second, third, and fourth days.(209) They found that the effect of the first snow day was 

significantly higher for drivers over the age of 65, as shown in figure 36. Maze et al. found that 

crash risk was 3.5 times higher at the beginning of the winter season than at the end (as 

reported in Pisano et al., standard error and confidence level not reported).(223) El-Basyouny et 

al. investigated sudden extreme snow and rain events in Edmonton, Canada and found that 

major precipitation events which followed dry periods were highly significant for predicting 

increases in follow-too-close, stop-sign-violation, and run-off-road crashes.(208) These findings 

indicate that there may be some amount of driver adjustment involved in weather-related crash 

patterns. Interestingly, Andrey et al. concluded that elevated crash potential due to rainfall 

stemmed from reduced visibility, not reduced road friction, since crash rates quickly returned 

to normal following precipitation, even though the ground was still wet.(199) This phenomenon 

was not identified for snowfall events.
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© 2005 American Journal of Public Health. 

Figure 36. Graphic. Rates of fatal crashes on snowy days and the first snowy day 

compared with dry days, by age group.(209)

Andrey also noted that while high winds and fog account for a very small proportion of total 

weather-related crashes, they can represent conditions for increased risk when they occur in 

combination with precipitation (for instance, blowing snow or fog and wet pavement).(199) Wind 

can have a variety of effects on traveler safety, including directly pushing or overturning 

vehicles, obstructing visibility (blowing sand/snow/etc.), causing buildup of snow, or damaging 

bridges and other infrastructure. The stability of vehicles in motion is complex and is influenced 

by many factors, but strong crosswinds have the ability to overturn vehicles or push them out 

of their lanes or off the road. Edwards studied wind-related road incidents in England and 

Wales and discovered that the percent of total crashes that occurred in high winds was almost 

twice the percentage of time during which high winds were present.(221) Kumar & Strong studied 

the effect of high winds on two sections of roadway in coastal Oregon in relation to the 

implementation of automated wind warning systems.(222) This study found that there was a 

consistent difference in crash rates between the high wind season and non-high wind season. It 

also found that most wind-related crashes are single-vehicle or run-off-road crashes, reportedly 

involving drivers caught unexpectedly in high-wind situations. This is supported by the finding 

that drivers mostly rely on personal experience and observations in making decisions about 

travel.(199) Crash data from California and Minnesota indicate that wind affects single vehicle 

crashes more than multiple vehicle crashes. The data revealed that the average number of 

vehicles per crash was lower for wind-related crashes than for non-wind-related crashes: 1.69 

vs. 1.99 for California and 1.36 vs. 1.86 for Minnesota (confidence levels were not reported by 

the authors).(222) Additionally, the data showed that wind-related crashes were more likely to 

result in fatalities or injuries than non-wind-related crashes. This is because crash types that are 

more common in wind-related crashes (e.g., overturning, road departure) are also more likely 

to have severely harmful outcomes. Due to their size and profile, commercial trucks are 
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132

particularly prone to overturning or lane departure due to high winds (this is especially true of

lightly loaded trucks). According to Pisano et al., commercial trucks make up only 7 percent of 

vehicle miles traveled but are involved in 11 percent of fatal crashes.(223) They represent a

potential hazard to other drivers given their larger size and heavier loads, which result in a

more extreme impact in the case of a crash. Their size and weight also make them more 

vulnerable to icy and slick road surfaces, in addition to high winds. The physical and operational 

characteristics of trucks can also affect the behavior of the drivers of passenger vehicles that 

may share the roadway, and this can be exacerbated by adverse weather conditions.(224)

Furthermore, Maze et al. found that commercial vehicles are much less likely to divert, delay, or

cancel trips due to inclement weather conditions.(220) This means that during adverse weather, 

the proportion of these vehicles is likely to increase.

The issue of single- vs. multiple-vehicle crash characteristics can also be studied for weather 

conditions other than high winds. Yu et al. examined safety performance in terms of weather

on mountainous freeways and concluded that single-vehicle crashes have decidedly different

characteristics than multiple-vehicle crashes.(207) In modeling the effect of weather on 

mountainous freeway crashes, it was determined that some variables were significant for both

types of crashes, such as precipitation and average vehicle speed. However, weather

parameters were more significant for single-vehicle crashes and traffic characteristics were 

more significant for multiple-vehicle crashes. Wang et al. analyzed crashes on expressway ramps

with real-time traffic and weather data and found that curved ramp geometry and a wet 

roadway surface increased the probability of single-vehicle crashes, while off-ramps and high 

variance of speeds increased the probability of multiple-vehicle crashes.(214)

Assessments of Mitigation Strategies 

Highway system managers and operators believe that keeping roadways clear of ice and snow 

significantly increases traveler safety, but technological limitations in weather forecasting often 

lead to reactionary treatment of adverse road weather conditions. This aspect is generally

improving with modern meteorological technology and increasingly reliable forecasting. 

Additionally, technological advancements have increased the speed and effectiveness with which

agencies are able to deliver mitigation strategies, from advisory to control to treatment.

As stated previously, the overwhelming emphasis in safety- and weather-related literature is on

strategies intended to mitigate winter weather. Usman et al. examined winter weather

maintenance and snowplow operations and found that reducing the time it takes to expose at 

least 80 percent of the road surface from snow and ice (known as the bare pavement regain 

time) from eight hours to four hours would produce a 50-percent reduction in crashes.(225)

Additionally, Usman et al. found that a 1-percent improvement in road surface condition (as 

measured by the Road Surface Index) led to a 2- to 3-percent reduction in the expected 

number of crashes (the result was significant at a 95-percent confidence level but standard 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

133

errors were not reported by the authors).(210) The California DOT (Caltrans) installed an icy 

curve warning system on a five-mile segment of highway in northeastern California that was 

known for fatal crashes in icy conditions. A before-after EB study found an 18 percent 

reduction in crash rate following the implementation of the icy curve warning system (standard 

error and confidence level were not reported by the authors).(226) Saha & Young studied the use 

of weather-related VSL systems in Wyoming.(227) The study found that winter weather VSL 

implementation resulted in 17.42 fewer crashes per 100 miles of VSL per winter season along 

the 143 miles of VSL corridors in the study. 

Al-Kaisy et al. performed a synthesis of VSL use in adverse weather conditions. The following 

were among their findings:(228)

• In 2005, South Australia deployed a 45-sign VSL system to address weather and

observed a reduction in crash rates of 20 to 40 percent (standard errors and confidence

levels were not reported by the authors).

• In the early 2000s, Finland reported a 13 percent decrease in winter crashes (and 2

percent decrease in summer crashes) following deployment of a weather-focused VSL

system (standard error and confidence level were not reported by the authors).

• Germany began installing weather-based VSL systems in the 1970s and has reported

crash rate reductions of 20 to 30 percent (standard errors and confidence levels were

not reported by the authors).

• Various fog-related VSL systems in the U.S. (Alabama, New Jersey, South Carolina,

Tennessee, and Utah) have reported various safety improvements, though none

presented reliable, quantitative safety performance results.
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134

Source: FHWA

Figure 37. Photo. Example of variable speed limit sign with additional variable 

message sign for warning drivers of weather-related road conditions in 

Washington.(229)

Veneziano et al. explored the use of FAST.(200) FAST is designed for anti-icing at specific target 

locations, particularly bridges, ramps, and other elevated road surfaces. The study determined 

that, on average, FAST installation resulted in safety improvements, but the results varied 

widely. Crash reductions were computed to be from 16 to 70 percent on urban highways 

(standard errors ranged from 31 to 57 percent on rural interstates and from 19 to 40 percent 

on interstate interchanges). Some locations experienced crash increases, which the authors 

hypothesized may have been due to other factors such as increased traffic. The major weakness 

of this study was the lack of data detailing when the FAST system was in action during the study 

period. This is a common issue in studies examining mitigation strategies that operate 

dynamically but problematic because it includes time periods when the strategy is not 

operational. Standard errors and confidence levels for the results were not reported by the 

authors. 

An emerging field of research has begun exploring the implications of climate change on 

transportation safety and operations. Milder winters caused by climate change have the 

potential to reduce crashes due to slick pavement by up to 50 percent in some regions.(230)

However, other regions may experience increases in winter precipitation.(231)
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KEY TAKEAWAYS

Adverse weather can negatively affect road safety performance in a variety of ways. There is a 

moderate body of research which seeks to explore the effects of weather on safety 

performance, though many specific aspects of the issue are still not widely understood or 

agreed upon, such as the reason for decreased crash severity in winter weather conditions and 

the precise effects of weather on traffic exposure. This is due to several factors, including the 

varying characteristics of weather and driver behavior both spatially and temporally. 

Additionally, there are fewer studies which have performed reliable safety performance analyses 

of weather mitigation strategies. This is because crash studies generally require many years of 

data to produce statistically significant results. However, more countermeasures are beginning 

to be implemented long enough that they can be robustly analyzed given appropriate data 

availability. 

There is no shortage of weather data available. One of the most common transportationrelated sources is data from RWIS, regional networks of weather sensors geared specifically to 

road weather and primarily used for road maintenance. RWIS can collect a wide variety of 

different atmospheric and pavement weather data, and this data is widely available in almost 

every U.S. State. Another common source of weather data is crash records themselves. Most 

enforcement agencies record the prevailing weather and/or road surface condition when 

reporting a crash. This data can be extremely valuable, as it can reveal the conditions at the 

time and location of a specific incident. However, there is a certain amount of subjectivity 

involved in crash record data, and this can make it unreliable in some cases. Many studies use 

data from various sources, including weather data, crash data, and traffic operations data, 

among others, so it is crucial to be able to integrate these data sources smoothly. The FHWA 

has developed multiple initiatives to advance the cause of data integration, most notably the 

ongoing establishment of the WxDE platform. 

Researchers have used a variety of analysis tools to explore the effect of weather on safety 

performance. The fundamental safety performance metrics are crash frequency, which was 

more commonly used in the studies reviewed here, and crash rate. As noted in Chapter 3, 

crash rates assume a linear relationship between crash frequency and traffic volumes. Research 

has shown that this assumption is false in many cases. Crash rates are therefore being phased 

out as a reliable metric for safety performance. The most common technique for identifying the 

effect of a parameter or mitigation strategy is the before-after EB method. Another common 

technique is the matched-pair framework, which multiple studies have used to compare crash 

occurrence on days with inclement weather with that of normal days. Many different statistical 

modeling techniques have been applied to various aspects of weather and safety performance to 

determine which factors have the most significant effect on safety performance and the 

aggregate effect of certain weather conditions or mitigation strategies. The method most 

commonly used is the negative binomial regression family of models.
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136

These different analysis approaches have resulted in a wide range of different findings. Eisenberg 

& Warner studied the entire continental U.S. and concluded that while crashes increased in 

snowy and icy conditions compared to dry conditions, fatal crashes decreased.(209) This could be 

caused by heightened driver caution and/or decreased vehicle volumes during conditions of 

inclement winter weather.(203) Furthermore, fatal and serious injury crashes tend to be more 

likely during the first snow event of the season compared to the second, third, and fourth 

storm events, and the early part of the winter season represents higher crash potential than the 

latter part.(209,220) El-Basyouny revealed a similar result for major rain and snow events following 

dry periods.(208) Kumar & Strong found a high number of crashes occurring in high-wind 

conditions in some regions,(222) which can particularly affect commercial trucks.(223) Additionally, 

different weather conditions can have different effects on the occurrence of single-vehicle 

crashes versus multiple-vehicle crashes. In terms of the effect of weather mitigation strategies, 

most studies reviewed in this chapter indicate improvements in safety performance. However, 

studies frequently suffered from incomplete data and small sample sizes of crashes. It is also 

important to note that studies finding increases in crash frequency or severity are often less 

likely to be published or reported.

There are several challenges that remain in coming to a full understanding of the effect of 

weather and weather-related road conditions on safety performance. Identifying a balance 

between macroscopic and microscopic study approaches continues to be a need for producing 

reliable-but-useful results. As Strong et al. identified, macroscopic studies are preferred when 

dealing with crash data because of the nature of traffic crashes and the presence of noise in the 

data.(198) However, these large-scale studies often produce results that inform the “bird’s eye 

view” of the issue but leave many questions as to the specifics. Developing techniques for 

reliable, actionable studies at the local and regional level represents a key area of advancement. 

Additionally, defining common, accepted approaches in data collection, aggregation, and 

definition will increase the transferability of methods and the translatability of results.(198) Finally, 

the effect of climate change on transportation safety, especially regarding winter and extreme 

weather, is a field that is growing in importance.
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CHAPTER 5 — MODELING TRAVELER BEHAVIOR AND SAFETY 

INTERACTIONS

INTRODUCTION 

Some TSMO strategies are expected to affect performance at a corridor, area, or system level. 

For some strategies, this might occur because the strategy regularly increases capacity or more 

effectively utilizes capacity on a facility during the peak period and in the peak direction of 

travel. This additional capacity and the resulting changes in travel time or travel time reliability, 

may attract drivers from parallel facilities. For other strategies, this might occur because the 

strategies increase the attractiveness of non-automobile modes or incorporate the use of 

traveler information, specifically to influence traveler behavior, including whether to make a 

trip, trip timing, mode choice, route choice, and destination. 

A previous FHWA Office of Planning effort, An Open Source Dynamic Traffic Assignment Tool for 

Assessing the Effects of Roadway Pricing and Crash Reduction Strategies on Recurring and NonRecurring Congestion, attempted to integrate crash prediction models with DTALite, an opensource dynamic traffic assignment (DTA) package. The effort ran into multiple challenges 

related to the temporal and network detail compatibility of the crash prediction and DTA 

models. Other than this effort, there are no known efforts related to the integration of safety 

performance modeling and analysis with transportation demand modeling and analysis for 

strategies that are expected to affect performance at a corridor, area, or system level. There 

are, however, some high-level estimates in the literature of safety performance benefits of 

travel demand management strategies, some of which are related to the demand management 

category of TSMO in figure 1.The purpose of this chapter is to provide an overview of ways in 

which travel demand patterns and safety performance might interact within a corridor, area, or 

system. The chapter also includes considerations for future research aimed at modeling these 

interactions and quantifying their safety performance outcomes. 

To model the possible operational and safety effects of TSMO strategies that influence traveler 

behavior, including whether to make a trip, trip timing, mode choice, route choice, and 

destination, practitioners must grapple with the systemic effects of induced demand and the 

spectrum of complications implied within triple convergence.

(232) Downs defines triple

convergence as the self-regulating relationship inherent to traffic in a transportation 

network.(233) For instance, if vehicle capacity were hypothetically expanded along a corridor to 

alleviate peak period congestion, travelers that had otherwise 1) taken an alternate route, 2) 

used the corridor at an alternate time, or 3) taken an alternative mode during the peak travel 

periods would migrate back to the newly expanded corridor to take advantage of the improved 

performance. This would likely lead to a return to congestion during the peak periods. Travel 

demand is a fluid and interrelated phenomenon, and operational changes on one facility will 
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138

have follow-on and spillover effects for much of the adjacent transportation system. These 

effects can be distilled into four categories:

1. Trip Generation: How many trips are made?

2. Temporal Shift: When are trips made?

3. Route Shift: Where are trips made?

4. Modal Shift: By what mode are trips made?

Selected TSMO and other demand management policies and strategies influence the 

fundamental costs and trade-offs associated with transportation choices, usually during the peak 

periods of travel, to optimize the performance for a given corridor or region. For instance, 

agencies may apply tolls and fees to price out demand for a given roadway and attempt to 

provide reliable service for those individuals who are willing to pay. Likewise, operational 

improvements to alternative transportation modes may reduce existing barriers or travel time 

costs and encourage additional usage of those modes. While some TSMO strategies attempt to 

influence the factors that govern individual travel demand decisions, like any other 

transportation policy, they are not immune to spillover effects. 

These complex interrelationships underscore the difficulty in determining the potential safety 

outcomes of a given strategy. The safety effects of any TSMO strategy are likely dependent on 

the geographic constraints of the strategy, as well as the systemic network outcomes on 

adjacent facilities that result from the implementation of that strategy. Three examples of 

TSMO programs demonstrate thinking about these outcomes in practice: PTSU, HOT lanes, 

and TSP.

Each of the aforementioned strategies will likely have a tangible, but significantly different 

influence on the four components of travel demand. Each strategy influences trip generation, 

but PTSU and TSP temporarily expand and optimize existing capacity to accommodate 

additional trips while HOT lanes attempt to curtail demand of a particular facility to improve 

travel time reliability. In response, travelers may be attracted to the new capacity along a given 

corridor provided by PTSU and TSP, while strategies that convert existing general-purpose 

lanes into HOT lanes may push away drivers unwilling to pay for travel time benefits onto 

parallel facilities or an alternative mode, or they may choose to travel during less congested 

times when tolls are reduced or not present. Finally, PTSU may encourage additional motor 

vehicle travel by adding vehicular capacity, while TSP may encourage transit ridership by 

optimizing travel time. The specific performance of any TSMO strategy is highly contextdependent, but these four measures of travel demand (trip generation, route shift, temporal 

shift, and modal shift) are the foundation for discussing changes in outcomes.

Because of these complex and interrelated travel demand results, practitioners and researchers 

attempting to quantify the safety performance of TSMO strategies must apply a modeling 

process that can address all four components of the strategy to arrive at estimates of exposure 
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139

on affected facilities, which are key to analyzing safety performance. The following will affect 

exposure because of a TSMO policy aimed at demand management and/or better utilization of 

capacity: 

1. Change in traffic volumes on the treated facility(ies) during period of active operation.

2. Change in traffic volumes on the treated facility(ies) during period of inactivity.

3. Change in traffic volumes on parallel/alternate/competing routes.

4. Change in traffic volumes on alternate/competing modes.

The following sections of this chapter will survey the literature surrounding travel demand 

responses to TSMO and some other demand management strategies, provide a taxonomy of 

likely outcomes for certain strategies, and outline potential methods for future research to 

consider when modeling changes in exposure that may lead to changes in safety performance.

PREVIOUS RESEARCH

This section outlines some of the literature describing the relationship between strategies 

aimed at managing travel demand and/or more effectively utilizing existing capacity and travel 

demand. These strategies vary in scope and application; they range from more traditional 

programs, such as telework and transit service improvements, to more complex operations, 

such as managed lanes and congestion pricing schemes. Much of the related literature focuses 

on an economic approach that defines demand with respect to price elasticity. This review 

outlines the behavioral expectations associated with the four dimensions of travel demand: trip 

generation, temporal shift, route shift, and modal shift.

Trip Generation

Transportation responds to the forces of supply and demand like any other market; however, 

there are numerous confounding factors that make it difficult to quantify exact relationships 

between available capacity on a given corridor and the number of trips taken. Burris succinctly 

summarized this challenge in describing the effect of flat rate tolls and variable rate tolls (i.e., 

congestion pricing and dynamic lane tolling) on travel demand, “empirical estimates of both ETvariable and ET-flat range from (almost) perfectly inelastic (-0.02) to unit elastic (-1.0)” (p. 55).

(234)

Essentially, similarly-designed strategies will have wide ranging effects based on the exact 

context. To this point, surveys of elasticity of demand for select strategies, including 

parking price, transit price and service, fuel price, and dynamic HOT lane pricing, 

provide wide ranges of empirical estimates for the effect of these strategies on 

willingness to travel (i.e., trip generation).(234,235,236,237,238) This research suggests that the 

relationship between the cost of travel, in direct and indirect price, time, reliability, and 

transportation demand, is non-linear and highly dependent on an individual’s 

transportation options. 
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Changes to travel behavior may not necessarily be the difference between a trip made

or deferred. More nuanced solutions, such as car-pooling, alternative routes, altering

trip timing, or shifting modes, may satisfy traveler demand. As Lee notes, vehicular travel 

demand is not synonymous with person travel demand.(236) Vehicle travel demand might

be more elastic than personal travel demand, as the former implies the consumer can 

replace a vehicle trip with alternatives (i.e., alternative times, car-pooling, and alternative 

modes), while the latter suggests that a trip deferred entirely is the least likely outcome.

Lee illustrates demand for transportation is the product of a bundle of cost inputs (fuel 

cost, vehicle maintenance, insurance, tolls, parking, etc.) that can translate into a variety

of travel choices (route choice, trip timing, modal shifts, occupancy shifts, etc.).(236)

Furthermore, these constituent costs may not factor equally in an individual’s decision. 

Ephemeral component costs, like parking and toll fees, may be significantly less

important than more direct personal costs, such as travel time savings and the cost of

fuel, in the decision to drive a personal motor vehicle. The balance between these 

individual costs and the resulting trade-offs incentivize individuals to seek transportation

alternatives. 

Alternatives: Route Shift, Temporal Shift, and Modal Shift 

Like trip generation, individual decisions to seek alternatives is a product of costs relative to the 

viable options. As Burris concludes, a significant determining factor in the outcome of a strategy 

aimed at managing travel demand and/or more effectively utilizing existing capacity is the 

diversity and viability of legitimate alternatives.(234) Travel demand will tend to be highly inelastic 

in communities with fewer alternatives available to consumers (i.e., travel modes, alternate 

routes, flexibility of trip timing). For example, drivers may be more willing to pay a toll if they 

believe that there are no viable alternatives to evade the toll. An example of this balance in 

practice relates to the performance of HOT lanes.

Jansen & Levinson (2013) and Liu et al. (2011) both found a positive correlation between HOT 

lane price and demand: as price increases, demand for HOT lanes increases.(239,240) While this 

may seem counterintuitive, both studies note that price acts as a signal of future congestion. 

Drivers might believe increases in toll price reflect more congested conditions and less reliable 

travel times. At a certain threshold, the travel time savings and reliability outweigh the toll 

price. However, as Brent & Gross note, the demand for a tolled facility relative to the general 

purpose lanes is a much better indicator of the elasticity of demand.(232) Demand for the HOT 

facility will be lower than the general purpose lanes by virtue of the direct cost of using the 

tolled facilities. Individuals must make the trade-off of sitting in congestion, paying the toll, or 

identifying an alternative.
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Sullivan reported that variably-priced express lanes on SR-91 in southern California seemed to 

experience route diversion from parallel facilities.(243) After the express lanes opened on SR-91, 

reduced congestion on the tolled facility drew traffic from adjacent local streets. Conversely, as 

congestion returned to SR-91 over time, traffic on parallel, local roads also increased indicating 

that drivers sought alternative routes. To a lesser extent, there were mixed effects on peak 

period congestion spreading; there was a modest increase in traffic volumes at the shoulders of 

the peak period, but there were strong directional and AM/PM components to any discernable 

trip timing diversion.

Like TSMO strategies that improve travel time for motor vehicle travel, several techniques, like 

TSP and bus-only hard shoulder running, improve the reliability of transit to make this mode 

more appealing to consumers. To estimate the effect of transit improvements on transit 

ridership, Litman surveyed existing literature surrounding transit fare and service elasticities and 

recommended the transit elasticity ranges in table 23.(237) 
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Table 23. Recommended transit elasticity values.(237)

Market Segment Short Term Long Term

Transit ridership 

WRT* transit fares Overall –0.2 to –0.5 –0.6 to –0.9

Transit ridership 

WRT* transit fares Peak –0.15 to –0.3 –0.4 to –0.6

Transit ridership 

WRT* transit fares Off-Peak –0.3 to –0.6 –0.8 to –1.0

Transit ridership 

WRT* transit fares Suburban Commuters –0.3 to –0.6 –0.8 to –1.0

Transit ridership 

WRT* transit service Overall 0.5 to 0.7 0.7 to 1.1

Transit ridership 

WRT* auto operating 

costs

Overall 0.05 to 0.15 0.2 to 0.4

Automobile travel 

WRT* transit costs Overall 0.03 to 0.1 0.15 to 0.3

Note: * WRT = with respect to.

Price elasticities refer to the proportional response in demand (consumption) to a change in 

price. For instance, if the estimated price elasticity of transit ridership relative to transit fares is 

-0.4, then a ten percent increase in the cost of a transit fare is expected to lead to a four

percent decline in total ridership. Conversely, a ten percent decline in the price of a fare should

lead to a four percent increase in ridership.
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Table 23 illustrates two important points: 1) demand is a range of values and tends to be more 

elastic in the long term, either reducing or encouraging ridership as customers become 

accustomed to the changes, and 2) the increasing costs of motor vehicle travel are less effective 

at incentivizing new ridership than direct investments in transit. The first point suggests that the 

travel demand (and potential safety benefit) of improved transit ridership is not fixed; the same 

increase in transit service frequency will yield vastly different increases in ridership numbers 

depending on context (i.e., street connectivity, density of land uses, existing traffic congestion).

To the second point, if rising vehicular costs do not drastically increase transit ridership, then 

the inverse is likely true. Improvements to transit may have a slight effect on modal shift to 

transit, but ultimately, the direct costs of motor vehicle travel will govern the demand for 

personal motor vehicle travel. If the strategy does not produce a sustained decline in the 

number of vehicles on a given corridor, then the net effect on traffic volumes may be negligible. 

If new transit trips are eventually replaced by new vehicle trips due to available capacity, either 

through individuals shifting routes or travel times, then it does not seem intuitive to expect a 

corresponding change in traffic volumes.

Challenges to Quantifying Strategy Effectiveness

Many strategies aimed at managing travel demand and/or more effectively utilizing existing 

capacity have a temporal dimension, in addition to a geographic element. While this narrowly 

applies to time-specific policies, such as peak-period tolling or congestion pricing, there is 

short- and long-run effectiveness to every policy.(236,237) In addition to this temporal dimension, 

strategies often affect traffic characteristics which vary over time. Both factors complicate 

analysis of strategy effectiveness. Travel demand tends to be more elastic over the long term, as 

individuals adapt, identify alternatives, and make trade-offs that suit their individual needs. While 

this initially supports the longevity of effective TSMO management, empirical evidence tends to 

suggest that the limited scope of some TSMO strategies can produce mixed effects. For 

instance, if a toll only applies to single-occupancy vehicles and a congestion charge only applies 

to certain classes of vehicles, then travelers may choose to car-pool or drive non-charged 

vehicles to escape or minimize the toll, or they may simply decide that the additional costs are 

an acceptable trade-off for time savings and reliability. In these circumstances, the policy may 

have effectively targeted a specific component of demand, but the net effect on congestion may 

not be as strong as anticipated.(241)

TSMO strategies have mixed long-run effects as travelers adjust to the shifting transportation 

costs. Long-range housing and work location decisions may further distort the effects of TSMO 

strategies on traffic volumes, as well as safety. Land use and transportation’s reciprocal 

relationship suggest that over time, individuals may make permanent housing and work 

decisions that conform to transportation conditions. As an example, housing within London’s 

congestion charge cordon appears to command a price premium over non-cordoned areas of 
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central London.(242) Individuals may decide that the housing premium is worth the latent 

transportation benefits, as well as to take advantage of a residential discount in the policy. The 

response to the strategies may be mixed and uneven over time–from a few months to several 

years–but it is clear from the literature that these tactics do not produce linear, easily

transferable results that can be evenly applied to a range of contexts. The effects of the

strategies are highly network-specific. 

Connections to Safety 

While studies of the empirical effects of strategies aimed at managing travel demand and/or 

more effectively utilizing existing capacity have typically focused on operations or price 

sensitivity, researchers and DOTs have only recently investigated the influence of these 

programs on safety. Approaches have varied between more macro-level trends, such as the

cost of fuel,

(243) and more local trends like London’s peak-period congestion charge.(245)

Intuitively, these studies note a negative correlation between travel costs and the number of 

crashes (i.e., higher travel costs are associated with having fewer crashes). If travel costs 

decrease overall travel, particularly single-occupant vehicles, then one might expect a similar 

decline in the number of crashes as well. However, the direct effects of these policies may be 

just as nebulous as the empirical measures of travel demand elasticity.

Grabowski & Morrisey found that decreasing retail gasoline prices appear to yield 

proportionally smaller increases in traffic fatalities.(246) This suggests that travel demand 

increases as the general cost of motor vehicle travel decreases, resulting in increased fatalities. 

Likewise, Green et al. noted that although traffic crashes within London’s congestion cordon 

decreased faster than the rest of the country, it is also true that the general trend in the rate

and frequency of roadway crashes in the United Kingdom was still declining.(245) Additionally, the 

study observed that the number of crashes occurring outside of the charged time period 

decreased, as well. While one may conclude that temporal shifts in vehicle travel did not 

contribute to additional spillover crashes outside of the charged period, it may also suggest that 

the congestion charge itself had a limited influence on safety outcomes. The effect of policies 

that reduce motor vehicle travel are likely beneficial for safety, but the direct, quantifiable effect 

is difficult to ascertain.

Litman attempted to provide preliminary estimates of travel demand management and TSMO’s 

effects on safety by providing safety benefits in a manageable format, similar to one that might 

be provided in a traditional CMF format.(247) Table 24 and table 25 provide a summary of 

Litman’s conclusions. 
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Table 24. Ten new shorter-term paradigm safety strategies (less than three 

years).

(247)

Strategy Traffic Safety Effects Crash Rate Reductions

Transit service 

improvements (more 

routes, frequency, etc.).

Reduces vehicle travel directly, 

and often leverages additional 

reductions. 

Each 1% transit ridership gains 

typically reduces traffic 

casualties by 1% or more.

HOV and bus traffic 

priority.

Reduces automobile travel and 

encourages transit and 

ridesharing.

Can reduce affected traveler’s 

crash rates 10-30%, and total 

rates 1-5%.

Active transport 

improvements (better 

sidewalks, crosswalks, bike 

lane, etc.).

Reduces walking and bicycling 

crash rates, and total per capita 

crash rates.

Comprehensive active transport 

improvements can reduce 

resident’s total crash casualty 

rates 5-10%. 

Expanded carsharing 

services.

Reduces crashes by reducing 

car ownership.

Reduce total crashes 0.3-3%, 

with larger reductions in denser 

areas.

Raise fuel taxes to fully

finance roadway costs, or as 

a carbon tax.

Reduces total vehicle travel and 

traffic speeds.

A 50¢ per gallon tax should 

reduce crash casualty rates 4-

12%. 

Efficient parking pricing 

(motorists pay directly for 

using parking spaces).

Charging motorists directly for 

parking typically reduces 

affected trips 10-30% and may 

reduce vehicle ownership.

Each 10% increase in the 

portion of parking that is 

efficiently priced reduces crash 

casualties 1-3%.

Congestion pricing (road 

tolls that increase under 

congested conditions).

Reduces crashes by reducing 

automobile use, particularly in 

large cities.

Reduces affected areas crash 

casualty rates 15-30%, with 

smaller reductions in nearby 

areas.

Distance-based vehicle 

insurance and registration 

fees.

Reduces vehicle use, especially 

higher risk driving.

Reduces affected vehicles’ 

crashes by 10-20%. 

Commute trip reduction 

programs.

Typically reduces affected 

commute trips 5-30% and may 

cause some vehicle ownership 

reductions. 

Can reduce affected 

commuters’ crashes casualty 

rates 5-30% and total crashes 

0.5-3%.

Mobility management 

marketing.

Encourages travelers to use 

non-auto modes.

Can reduce affected 

households’ crashes 5-10% and 

total crashes 3-6%.
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Table 25. Five new longer-term paradigm safety strategies (more than three 

years)(247)

Strategy Traffic Safety Effects Crash Rate Reductions

More comprehensive and 

multi-modal planning.

Supports more multi-modal 

transport planning and 

considers safety effects. 

Can lead to large vehicle travel 

and crash reductions.

More connected and 

complete streets.

Reduces crash frequency and 

severity by reducing vehicle 

travel, improving non-auto 

modes and reducing traffic 

speeds.

Can reduce local crash casualty 

rates 10-30%. 

Reduced parking 

requirements (for zoning 

and development). 

Reduces crashes by reducing 

vehicle ownership and use.

Can reduce affected area’s 

crash casualty rates 5-15%. 

Urban rail and Bus Rapid 

Transit.

Reduces crashes by reducing 

vehicle ownership and use, and 

traffic speeds.

Can reduce crash rates 30-60% 

in affected areas and 10-30% 

region-wide.

Smart Growth and Transit 

Oriented Development.

Reduces crash frequency and 

severity by reducing vehicle 

travel, improving non-auto 

modes and reducing traffic 

speeds.

Can reduce crash casualty rates 

30-60% in affected areas and

10-30% region-wide.

These point estimates typically reflect individual studies or the author’s own work, and they 

may not reflect a wide range of contexts, scenarios, and potential outcomes. As previous 

literature on travel demand strategies has indicated, unique circumstances often dictate the 

effectiveness of individual programs. However useful these estimates may be as a starting point, 

they likely suffer from many of the same issues that affect estimates of elasticity of travel 

demand. Actual effects to safety outcomes likely vary widely according to geographic context, 

availability of alternatives, personal preferences, and long-range temporal effects. 

TAXONOMY OF DEMAND MANAGEMENT STRATEGIES

Demand management strategies, depending on the individual policy design, will vary in their 

influence on the four dimensions of travel demand: trip generation, route shift, temporal shift, 

and modal shift. Furthermore, these strategies typically take two general approaches, either 

through directly pricing transportation or by optimizing the existing capacity of the system. 

Table 26 and table 27 outline a conceptual taxonomy for demand management strategies, 

including some that fall under the umbrella of TSMO, according to their design and potential 

effects on the transportation demand modeling process. It is important to note that terms such 
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147

as high, medium, and low are subjective and can be interpreted differently even within the 

traffic operations industry.

Table 26. Example taxonomy for considering transportation demand effects of 

pricing strategies.

Demand 

Management

Strategy

Trip 

Generation

Route 

Assignment Temporal Shift Modal Shift

Congestion 

Pricing High Medium High Medium

VMT Tax High High Low High

HOT 

Lanes/Tolls High High Medium Low

Rideshare High Medium Low Low

Fuel Tax High Low Low Medium

Parking Fees Medium Low Medium Medium

Parking Cash 

Out Medium Medium Low Medium

Transit Fare 

Subsidies Medium Low Low High
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Table 27. Example taxonomy for considering transportation demand effects of 

capacity optimization strategies.

TSMO Strategy Trip 

Generation

Route 

Assignment Temporal Shift Modal Shift

PTSU Medium Medium Medium Low

Transit 

Improvements Medium Low Low High

Text/Mobile 

Alerts Medium High High Medium

Bike/Scootershare Low Low Low High

Dynamic 

Message 

Boards

Low High Medium Low

HOV/

Carpooling Low Low Medium Low

Telecommuting Low Low Low Low

Flexible Work 

Schedules Low Low Low Low

Trip Generation 

Strategies with a strong to moderate effect on trip generation tend to either induce trips that

may not otherwise have been taken or discourage trips from happening at all. They typically

achieve this by either freeing capacity for additional trips or pricing more discretionary travelers

off a given corridor (i.e., through a toll). To evaluate the potential trip-generating effects of such 

policies, the practitioner must determine if the policy encourages additional trips through

additional capacity, or if the policy discourages use of a facility to improve travel time reliability

for those who are willing to pay. Every strategy has a magnitude of effect between these two

poles on the spectrum. If the policy improves capacity, then the practitioner must identify the 

source of any additional trips (i.e., other routes, other times of the day, or trips that would

have been forgone entirely). Conversely, if a strategy tends to restrict trips, typically through

direct pricing like congestion tolling or a fuel tax, then the practitioner must identify the 

potential alternatives to the affected network. Travelers will usually exhaust all potential 

alternatives before abandoning a trip, and the willingness for people to pursue those 

alternatives is directly related to the viability of the alternatives.(234,236)
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Route Shift 

The available road network within a community strongly influences route choice. Lee’s insight, 

that a trip deferred entirely is the most inelastic travel decision, suggests that individuals will

almost always exhaust all opportunities to make a trip before abandoning a trip altogether.(236)

While the nature of the trip (commuting as opposed to shopping, for instance) certainly affects 

an individual’s willingness to make a trip, most individuals will seek to optimize their

transportation before deferring it. To this point, Lee also notes that travel time is typically one 

of the strongest perceived costs of making a trip, usually overshadowing other direct costs like

tolls or parking. Individual travelers, particularly those making necessary trips, could be

expected to consider an optimized route for travel time before considering other alternatives.

Street network connectivity, as well as the real and perceived congestion along this network, is

typically the first set of trade-offs for a potential traveler. Limitations to connectivity could

include the lack of parallel road facilities, natural barriers (e.g., a single bridge over a water

feature), or strongly nodal community development (e.g., desert or mountain communities). In

limited connectivity environments, demand for transportation along the few available facilities

will likely be highly inelastic. Burris notes that price-based TSMO strategies on these facilities

would likely yield weak changes in traffic volumes; users are willing to pay higher fees to use the 

road.(234) This flexibility to choose alternate routes should be a critical factor in modeling

projected traffic volumes. 

Temporal Shift 

In addition to choosing alternative routes, travelers may choose to travel during off-peak 

periods when congestion has abated and a demand management or operational strategy may

not be in operation. This behavioral choice has a strong relationship with trip generation, as

individuals choose to travel during, or at the edges of, the peak travel period. Strategies with

the strongest effect on trip timing (e.g., congestion pricing) are those that either narrow or

expand the peak travel period. Certain time-independent strategies, like a fuel tax or VMT tax, 

would influence travel at any time of day and not during a limited window; these policies would

have a very weak influence on the temporal shift in travel demand. 

Modal Shift 

Strategies that target modal shift attempt to reduce the perceived costs associated with

alternative transportation modes. These costs may be direct or indirect, and include improved

travel time reliability of transit, perceptions of safety, or, in dense urban environments, the cost

of personal vehicle ownership. These improvements may or may not increase the cost of

driving. In fact, if ultimately successful, strategies that encourage shifts to alternative travel 

modes may even reduce the actual perceived costs of driving, particularly in terms of travel 
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time and reliability, to those that do not change modes. Unless a strategy is formulated as a 

direct cost to personal driving (e.g., a fuel tax), it is unlikely that the policy would directly result 

in a significant decline in VMT. Nevertheless, strategies that improve alternative modes, 

regardless of their effect on driving, may facilitate many more individual trips across all modes 

where they are applied, ultimately making the entire system more efficient.

MODELING THE SAFETY EFFECTS OF TSMO STRATEGIES 

This section builds on the preceding taxonomy by outlining a framework of practices that may 

allow practitioners to model the safety outcomes of strategies that influence trip-making 

behavior. This begins with a brief discussion of some simplifying assumptions that modelers will 

likely have to make in order to define the scope of analysis and concludes with a discussion of 

possible methods for assessing potential outcomes. Many of the recommended methods are 

already common practices for modeling travel demand and the operational results. To bridge 

the gap between changes in exposure and possible safety performance analysis applications, the 

framework must allow practitioners to parse the operational, and ultimately safety effects of

the strategies into the four modeling components listed in the introduction of this chapter: 

1. Change in traffic volumes on the treated facility(ies) during time of implementation.

2. Change in traffic volumes on the treated facility(ies) outside of time of implementation.

3. Change in traffic volumes on parallel/alternate/competing routes.

4. Change in traffic volumes on alternate/competing modes.

Assumptions 

The simplifying assumptions below provide a manageable construct for implementing a modeling

framework. 

The Framework Only Applies to Peak Period Conditions

Many of the strategies focus on peak period conditions, as they are intended to offset peak 

period congestion. This conceptual model considers only peak period performance, rather than 

disaggregating these strategies from more systemic trends. As previously noted, future 

advancements to this work would consider coordinated strategies that could address recurring

and non-recurring congestion in real-time, regardless of the time of day. 

The Framework Does Not Consider the Influence of Land Use Planning or Smart Growth

Dense urban environments with a variety of transportation options appear to yield better 

safety performance outcomes (on a per capita basis) than suburban or rural contexts.(247) While 

land use patterns and Smart Growth strategies certainly influence transportation demand and 
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safety performance, their effect on a specific program is likely highly dependent on context.

Safety performance predictive tools should not compare diverse contexts with highly distinct 

travel patterns; they should compare the performance of a specific location with the strategy in 

place to the performance of that location without the strategy in place. 

Methods to Model Trip Generation 

The traditional Four-Step Model consists of four sequential steps closely related to the four

previously outlined dimensions of travel demand: trip generation, trip distribution, modal split, 

and route assignment. The initial two steps, trip generation and trip distribution, define the 

demand within a transportation network by assigning magnitudes to trip generators and trip 

attractors within a network (trip generation) and applying a traditional gravity model to

distribute trips within the system according to a quantified friction or cost (trip distribution). 

While this method has strong applicability for modeling macro-level movements within a

transportation network, it may not provide an adequate means for assessing most strategies

aimed at managing travel demand and/or more effectively utilizing existing capacity. The linear, 

sequential process of trip-based modeling may struggle with factoring induced demand and 

distinguishing more necessary travel (e.g., commute trips) from more discretionary travel (e.g., 

recreation and shopping). Both elements are likely necessary to determine the effects of a given

policy, especially since the policies may directly incentivize or discourage additional trips on a

portion of a network. 

Activity-based models provide a useful enhancement that considers detailed land use and 

personal behavior in the modeling process. These models attempt to mimic the personal travel 

decisions (when, where, how long, and why) that individuals make throughout the day. As some

TSMO strategies clearly produce responses in travel demand and the willingness to pay for that

travel or destination, trip-based models alone would likely miss many of the system constraints

and trade-offs incentivized by these policies. Ferdous et al. found that activity-based models 

performed better than traditional trip-based models in estimating inter-county commuter flows, 

and at least as well as trip-based models in modeling project-level effects to traffic volumes in

metropolitan Columbus, Ohio.(248) As the research recognized, this comparison is only a first

step in testing and validating activity-based models. Further refinements promise to capture a

wider range of the transportation utility and the sensitivities to price and travel time reliability

noted in the literature. 

Methods to Model When a Trip is Made 

Like trip generation, trip-based models have limitations associated with allocating trips by time 

of day. The lack of sensitivity to individual traveler behaviors and temporal variation in trips 

throughout the day (i.e., considering all hours of the day individually rather than peak period vs. 

off-peak period) translates into travel time predictions that are not effective in traditional safety
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analyses. By contrast, Ferdous et al. found that activity-based models outperformed traditional 

trip-based models in modeling peak and off-peak commuter flows, trip time, and travel duration

in the same Ohio study. For evaluating the detailed changes in behavior, activity-based models 

promise to provide the granular detail necessary to evaluate operational and safety effects at a

specific site, as well as its parallel facilities.(249)

Methods to Model Mode Choice 

Activity and utility-based models also provide a better framework for modeling individual modal 

choices for certain trips. As Bhat observed, traditional trip-based modeling tends to 

overestimate modal shifts to alternative modes during commute trips, particularly because it 

struggles to capture trip-chaining and other travel behaviors that incentivize mode choice.(250)

Multivariate utility models allow for many of the individual preferences and economic costs of

transportation decisions to be weighted and factored into discrete choices (as shown in figure 

38).

 © 2015 Transportation Research Board. 

Figure 38. Equation. Example utility function for mode choice.(251)

While there are limitations to this method, such as the consideration personal and cultural 

preferences, it does improve on traditional trip-based modeling by considering individual 

changes to the unit costs outlined in the travel demand literature and translates each cost into a 

modellable trade-off.(251) 

Methods to Model Network Assignment 

The last step in the traditional Four-Step Model is route assignment. After travel demand, 

system constraints, and mode choice have been defined, the model must allocate trips to

specific routes based on the cumulative results of the preceding three steps. Route assignment 

generally assumes that all users will maximize their utility by selecting the shortest route, in

terms of travel time, relative to the decisions of all other users. However, as previous sections

have detailed, there are significant limitations with this method. Traditional models are illequipped to handle fine-grain temporal variations in traffic, and, therefore, travel times along

every route are static across the network. In other words, while traditional models assume that

each user will seek their shortest path, these models also struggle to determine the shortest

path based on the flow of traffic over precise time periods.(252) Naturally, this static model of the 
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transportation system may be ill-suited to quantify the nuanced network effects of some TSMO

strategies. 

By contrast, dynamic traffic assignment (DTA) is a potential solution to this critical 

shortcoming. DTA assigns traffic to routes with a similar goal of dynamic equilibrium but does 

so with the ability to model detailed temporal changes across the network. Therefore, modeled

travelers seek equilibrium more intuitively than traditional trip-based models. To this end, DTA 

has already been applied to TSMO scenario testing. Shelton et al. used DTA to model 

congestion along I-35 in central Texas.(253) The study noticed that DTA modeling efforts were 

able to discern the phenomena of induced demand as expanded capacity encouraged additional 

drivers and managed lanes produced lane changing and weaving by drivers attempting to access 

the facility. The modeled scenarios also tested the effectiveness of additional factors, such as

truck and heavy vehicle tolls and dedicated express lanes for traffic bypassing the city of Austin 

entirely. While these are still preliminary efforts for applying DTA to TSMO strategies, it is a

promising indication for the ability of DTA models to handle the complex and interdependent 

relationships implicit in behavioral responses to travel demand.

LIMITATIONS & FUTURE DIRECTIONS

While this methodology poses a useful guide for conceptual modeling, actual safety outcomes

will vary in relation to several confounding circumstances. In addition to the previously

mentioned assumptions, the following issues should be carefully considered while conducting

travel demand modeling and safety analyses for TSMO strategies. Furthermore, these 

limitations also present opportunities for future research into modeling TSMO and 

transportation safety. 

Non-Linear Relationship between Travel Demand and TSMO 

Safety analyses are highly dependent on the defined confluence of roadway facility infrastructure 

and traffic volumes (motorized and non-motorized). As the literature notes, TSMO strategies

have highly variable effects on travel demand based on specific circumstances. The emergence 

of real-time traffic information and predictive tools for personal and shared mobility modes 

only increases the complexity of modeling changes in demand and road user behavior. 

Consequently, it is unlikely that standardized SPFs could be reliably applied across different

contexts except in highly similar situations. However, the ability to model the effects of TSMO

strategies on travel demand may prove to be more transferable, and future research should 

explore the applicability of future modeling practices.
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Spillover Effects 

TSMO strategies will cause spillover effects across a wider geographic and temporal window 

than most planning and engineering studies could reasonably expect to capture. It may prove 

difficult to disentangle the total safety effects of a TSMO strategy from other background noise 

in the transportation system. 

Autonomous and Connected Technologies 

While the planning horizon for most TSMO strategies does not yet extend far enough into the 

future to consider the effect of widespread adoption of connected and autonomous vehicle

technologies, most preconceived notions of operational and safety performance may be 

challenged by these technologies. Future research and discussion surrounding all dimensions of 

TSMO performance should include evaluations of the effects of these technologies. 

Need for Additional Empirical Safety Analyses and Modeling 

While this chapter proposes a guide for modeling potential safety effects of TSMO strategies, 

this is not a substitute for empirical research on the observed effects. Future research should 

attempt to discern the practical responses to trade-offs presented by each individual context 

and transportation system. While responses to TSMO strategies may vary widely, each

individual study presents an opportunity to refine modeling techniques for both traffic 

operations and resulting safety outcomes. 
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CHAPTER 6 — SAFETY PERFORMANCE ANALYSIS NEEDS

This chapter outlines safety performance analysis needs for TSMO. The development of these 

needs was informed by the syntheses of strategy-specific safety performance knowledge and

capabilities documented in Chapter 2, as well as the syntheses of crosscutting topics contained 

in Chapters 3, 4, and 5. The safety performance analysis needs are organized into two general 

sections: 1) strategy-specific evaluation needs and 2) methodological needs. Strategy-specific

evaluation needs focus on developing knowledge on the safety performance effects of specific

strategies to inform agency decisions regarding implementation of those strategies. 

Methodological needs are not specific to any one strategy. They represent needed

advancements in safety data collection and analysis approaches to more accurately and reliably 

capture safety performance effects of TSMO strategies and to more broadly advance the 

practice of safety performance evaluation and prediction. 

STRATEGY-SPECIFIC EVALUATION NEEDS

The strategy-specific syntheses in Chapter 2 show that the literature contains various types of 

safety performance evaluations for some TSMO strategies. However, there are only a limited

number of published safety performance evaluations that are robust enough to inform future 

evaluations or the development of quantitative safety performance predictions. There is a need

for safety and operations staff from local, State, and Federal agencies, as well as researchers and

other stakeholders, to engage in regular dialogue on safety performance evaluation needs and 

priorities specific to TSMO strategies. TRB activities, AASHTO meetings, and pooled-fund 

efforts are logical forums for such communications. The following represent an overview of

potential near-term needs for consideration by these groups. 

HOV/HOT Lanes 

Fuhs & Obenberger found that there were about 1,200 route-miles of HOV facilities and 50 

route-miles of HOT facilities as of 2001.(262) By 2015, Fitzpatrick et al. indicated that these 

numbers increased to 1,800 route-miles of HOV facilities and 500 route-miles of HOT

facilities.(264) While the strategy-specific synthesis for managed lanes uncovered some safety 

performance analyses of HOV/HOT facilities, a stand-alone predictive methodology for freeway 

facilities with HOV/HOT lanes (and associated ramps) is needed. At the time of this report, work 

was initiated on Phase 2 of the NCHRP project 17-89A, HOV/HOT Freeway Crash Prediction

Method for the Highway Safety Manual. It makes sense to monitor the scope and review the 

results of this effort (once results become available) prior to detailing future research needs

regarding safety performance effects of HOV/HOT lanes. One consideration for detailing future 

needs will be the extent to which NCHRP 17-89A covers the range of applications, including 

exclusive lanes (with two-way or reversible operations), concurrent flow lanes, and contraflow 

lanes.
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Part-Time Shoulder Use 

Published safety research on part-time shoulder use focused primarily on the overall effect that 

implementation has on safety performance. Results vary significantly and are generally

inconclusive, with previous studies illustrating that this is likely because safety performance 

effects are dependent on the number of lanes present, the level of congestion, and any changes 

that may have occurred upstream of the part-time shoulder use location. No previous studies 

looked at the specific safety performance effects of the geometric considerations, including 

remaining shoulder widths, lateral clearance to roadside objects, shoulder cross-slopes, 

stopping sight distances on curves, and design characteristics at entrance and exit ramp

locations. With over 30 part-time shoulder use installations in operation covering 16 States as 

of 2016, quantitative safety performance knowledge on part-time shoulder use represents a

significant safety analysis need.

(50) At the time of this report, the team of NCHRP project 17-89, 

Safety of Part-Time Shoulder Use on Freeways, initiated work on the second phase of the project. 

It makes sense to monitor the scope and review the results of this effort (once results become

available) prior to detailing future research needs. One consideration for detailing future needs 

will be the extent to which NCHRP 17-89A covers the range of operating characteristics (e.g., 

bus-only, all vehicles) as well as application contexts in figure 1 (e.g., incident/event management

applications, traffic management applications). 

Bus Facilities and Preferential Treatments 

Safety performance research related to exclusive bus facilities and preferential treatments was

limited and inconclusive. Given the existence of safety performance predictive methodologies

for urban and suburban streets, a near term opportunity exists to enhance those 

methodologies by incorporating the effects of bus routes, bus stops, BRT presence and design, 

queue jump lanes, and TSP. Future research needs to consider safety performance impacts on 

all users and consider effects of other urban and suburban street features likely to interact with

the presence and design of bus facilities and preferential treatments (e.g., presence, extent, and

design of pedestrian and bicyclist infrastructure in the vicinity of bus facilities). 

Traffic Signal Technology and Timing Practices 

The results of safety performance research on traffic signal coordination, transit signal priority, 

and truck signal priority were mixed, inconclusive, or non-existent. Given the extent of

applications in the U.S., a near-term opportunity exists to enhance capabilities to estimate the 

safety performance effects of traffic signal technology and timing practices, with an initial focus 

on traffic signal coordination, transit signal priority, and truck signal priority. 

The literature did contain a limited number of defensible before-after crash-based evaluations of 

adaptive signal control technology, which helped set the stage for an ongoing (at the time of this 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

157

report) FHWA Office of Safety R&D safety evaluation of adaptive signal control technologies

under Evaluation of Safety Improvements, Phase X. The results of this “Phase X” effort should be 

reviewed (once they become available) prior to detailing future research needs with respect to

adaptive signal control technology. 

Ramp Metering 

The synthesis of ramp metering safety research uncovered several safety evaluations based on

an analysis of crash data. All indicated improvements in safety performance, but all used what is

generally thought to be the least reliable type of observational study design: a naïve before-after 

study comparing observed crash frequencies or rates (Chapter 1 contains additional detail on

the limitations of this approach). There is a need for a more robust and defensible safety

performance evaluation to build on these previous results. The research should consider the 

type of meter operation (e.g., static, dynamic) and application (e.g., incident/event management, 

traffic management); the effects of surrounding key geometrics (e.g., distance from the 

crossroad to the ramp meter for queue storage and the distance from the ramp meter to the 

freeway entrance terminal to provide for vehicle acceleration); and the group of crashes (type 

and location) influenced by ramp metering. 

Variable Speed Limits 

The literature contained a limited number of defensible before-after crash-based evaluations of 

variable speed limits as well as several surrogate-based evaluations, all of which indicate safety

benefits. A need exists to build on this research, distinguishing the safety performance effects of

variable speed limits across the applicable tactical program areas in figure 1. Variable speed

limits are used as strategies for road weather management, traffic incident and emergency

transportation operations, work zone management, planned special event management, freeway

management, and active transportation management. A safety evaluation of variable speed limits 

is occurring as part of an ongoing (at the time of this report) FHWA Office of Safety R&D 

effort, Evaluation of Safety Improvements, Phase X. The results of this “Phase X” effort should be 

reviewed (once they become available) prior to detailing remaining gaps and future research 

needs with respect to variable speed limits. 

Dynamic Lane Assignment 

The review of strategies did not uncover any safety evaluations of dynamic lane assignment

strategies (other than dynamic part-time shoulder use), including dynamic junction control, 

dynamic lane reversal, or other applications of dynamic lane use control (e.g., as part of incident

management). This is possibly due to relatively limited applications throughout the U.S. and 

variations in methods used for communicating dynamic lane use control to drivers. With
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158

transportation agency interest in these strategies continuing to increase, NCHRP 3-123,

Proposed Practices for the Application of Dynamic Lane Use Control, is underway and is expected to 

develop recommendations for the application of dynamic lane use based on human factors 

implications. The results of this NCHRP 3-123 effort should be reviewed prior to detailing 

future research needs with respect to dynamic lane assignment. 

Strategy Combinations 

This needs assessment has covered the safety performance evaluations of TSMO strategies and 

strategy groupings in isolation. It may be common to see the strategies discussed in this report

implemented in combination, particularly those with operational synergies (e.g., dynamic parttime shoulder use with dynamic lane assignment, or VSL with ramp metering). In these cases, it

may not be possible or useful to try to disaggregate the individual contributions to safety 

performance made by each strategy that is part of the combination. Future safety performance 

findings will need to be clearly tied to the strategy combinations and operational conditions for

which they apply. 

METHODOLOGICAL NEEDS

The syntheses of strategy-specific safety performance knowledge and capabilities documented in 

Chapter 2, as well as the syntheses of crosscutting topics contained in Chapters 3, 4, and 5 also 

resulted in insights on methodological (i.e., study design and analysis) issues and related lessons 

learned with respect to analyzing safety performance effects of TSMO. These issues and lessons

learned led to a set of methodological safety performance analysis needs for TSMO. This

section describes these methodological needs, which fall into five categories:

1. “Sub-annual” safety data collection and analysis.

2. Safety performance effects beyond the site level.

3. Safety performance effects of traffic operational conditions.

4. Study design and statistical analysis.

5. Mechanistic approaches to safety performance analysis of TSMO.

1. Need to Conduct “Sub-Annual” Safety Data Collection and Analysis

A significant number of TSMO strategies operate (or have the potential to operate)

dynamically, based on changing traffic or environmental conditions. Most safety performance

evaluations of TSMO strategies in the literature ignored these operational aspects, instead

analyzing annual crash frequencies (i.e., crash frequencies across all days, all times) and using an

annual average number of vehicles as a measure of exposure (i.e., numbers of vehicles across all 

days, all times). Two examples include the two reviewed safety evaluations of road weather

management strategies that used an annual analysis, even though the strategies were only “on”
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during specific weather and road conditions.

(200,226) Conducting such an annual analysis without 

regard for when a strategy is operational makes it challenging to distinguish what portion of the 

reported safety performance change was due to the strategy during the times it was 

operational.

There is a need to distinguish between the following measures of crash frequency and exposure 

as part of safety performance evaluations of TSMO:

• Average crash frequency (all days, all times) and actual or average number of vehicles

(all days, all times): most relevant to TSMO features and strategies that operate

permanently (“on” 24/7).

• Average crash frequency (during specific statically defined days or times, such as

daytime, weekday, weekday peak-period) and actual or average number of vehicles

(during the same specific statically defined days or times, such as annual weekday

volume, annual weekday peak-period volume): most relevant to features and strategies

that operate on a set time-of-day schedule.

• Average crash frequency (during dynamically defined days or times) and actual or

average number of vehicles (during dynamically defined days or times, such as during

certain operational conditions or weather conditions): most relevant to features and

strategies that operate dynamically.

In addition to the overall schedule that characterizes when a strategy is “on” or “off,” a given 

strategy may have characteristics that vary dynamically during times when the strategy is “on.” 

One example is ASCT, where the specifics of the traffic signal timing change dynamically in 

response to traffic conditions when the strategy is “on.” Ma et al. performed a before-after 

study of ASCT in which they analyzed annual crash frequencies (all days, all times) and used 

annual measures of exposure.(99) The CMF in this case is for “install adaptive traffic signal” (CMF 

Clearinghouse IDs 6856 – 6861). However, the differences in the signal timing parameters of 

the adaptive traffic signals across sites (which are changing dynamically as a function of traffic 

demand) are likely to add “noise” to the CMF estimate. This proved to be the case for Ma et 

al., who were unable to achieve a statistically significant CMF for fatal and injury crashes and 

noted that safety performance benefits appeared to vary by facility and by the traffic volume.(99)

Given that the current practice of statistical road safety analysis is largely based on statistical 

analyses of crash data and related hypothesis testing, “sub-annual” analyses during specific 

statically or dynamically defined days or times could result in sample size-related challenges. 

This was demonstrated in Lee et al., who attempted to develop an SPF that predicted crash 

frequency for a peak hour time period.(51) The objective of the analysis was to analyze the 

freeway safety performance effects of HOV operations on the inner (left) lane and part-time 

shoulder use, but the authors struggled to identify any statistically significant variables for the 

SPF because of the sub-annual analysis period. 

Several solutions are possible and will need additional study:
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• Utilizing longer periods of data than the typical 3-5 years since only crashes on specific

days and during specific times of day are counted. Collecting data over longer periods of

time should be done as part of longer-term performance monitoring but may cause

practical challenges when needing to know as soon as possible how strategies are

affecting safety performance. Including longer time periods can also introduce bias or

noise in the results because of changes in vehicle fleet characteristics, enforcement

funding and deployment levels, investments in maintenance, crash reporting thresholds,

and other factors that influence safety but change over these longer periods of time.

• Using probabilistic models of crash occurrence, such as those used to evaluate arterial

signal coordination and incident management.(93,165,166,168,169,170)

• Using traffic operational measures as safety surrogates if links between those

operational measures and crash frequency or severity exist (see methodological need 3:

Need to Analyze Safety Performance Effects of Traffic Operational Conditions).

Safety performance analyses that incorporate all three of these techniques could strengthen 

evaluations of TSMO strategies. For example, a surrogate analysis or probabilistic model of

crash occurrence at a shorter time interval (e.g., 15-minutes) could reinforce an analysis of 

longer time periods such as weekday peak-period, which could finally reinforce the more 

macro-level average crash frequency (all days, all times).

Needs for evaluating road weather management strategies offer additional challenges within this

sub-annual data collection and analysis context. 

Sub-Annual Data and Analysis Needs for Road Weather Management Strategies

As the synthesis in Chapter 4 showed, road weather management strategies have operational 

schedules that are dynamic and operational characteristics that are dynamic, sensitive to

prevailing weather and road conditions. To determine safety effectiveness of these strategies, 

one needs to be able to compare safety performance with the strategy operational to what

safety performance would have been under those same prevailing conditions without the 

strategy operational.

SPFs used in safety studies explicitly account for traffic and road geometrics and implicitly

account for “other unmeasured things” that change in systematic ways over time or space (e.g., 

weather and road conditions). This approach can be problematic for evaluating the safety 

performance effectiveness of road weather management strategies. In implicitly addressing

unmeasured factors such as weather and road conditions in SPFs, the assumption is made that 

the unmeasured factors are present in the same way, change in the same way, and influence 

safety in the same way, at both treatment locations (i.e., locations that receive the road

weather management strategy) and reference locations (i.e., locations that do not receive the 

road weather management strategy). This assumption may not hold when it comes to weather

and road conditions, which can vary significantly across time and space.
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There is a need to explore and develop alternatives for explicitly incorporating weather and

road conditions into safety performance analysis so that road weather management strategies

can be more effectively evaluated from a safety performance perspective by agencies investing

in them. This could include, for example, incorporating aggregate measures of weather and road 

conditions into predictions of annual crash frequency or incorporating more time-specific

weather and road conditions into probabilistic models of crash occurrence during much shorter 

timeframes. The methodologies will be influenced by current and foreseeable weather data

collection and management infrastructure. 

2. Need to Analyze Safety Performance Effects Beyond the Site Level

This methodological need deals with capturing safety performance effects of TSMO strategies

beyond a site level to get a more complete picture of their “total” safety performance effects. 

Many of the TSMO strategies reviewed in Chapter 2 can be expected to affect performance 

beyond an individual segment, intersection, or interchange. The needs assessment identified at 

least four different mechanisms by which this could occur. The first is that application of the 

strategy itself extends beyond an individual intersection or segment and therefore has an effect 

on performance at that level. Examples include arterial signal coordination, part-time shoulder

use, and managed lanes. 

The other three mechanisms that lead to a strategy affecting performance beyond a site level 

are more complicated and range from the migration of crashes to changes in traveler behavior. 

The next three sections describe these mechanisms. 

Crash Frequencies Increasing Downstream of Bottleneck Removal Projects and Other

Crash Migration Effects

In two different safety performance evaluations of part-time shoulder use, results indicated an

increase in crash frequency downstream of where a bottleneck was removed.(55,254) In the case 

of Margiotta et al., a significant bottleneck was removed upstream of the location with part-time 

shoulder use at about the same time part-time shoulder use began.(254) As a result of the 

upstream bottleneck removal, crashes increased significantly at the location with part-time 

shoulder use, making it challenging to determine the safety effects attributable to part-time 

shoulder use. In the case of Aron et al., part-time shoulder use contributed to removal of a 

bottleneck.(55) While the authors did observe a reduction in crash frequency at the location with 

part-time shoulder use, it was accompanied by an increase in crashes downstream of the 

location. In addition to these cases, Bauer et al. noted the possibility of crash migration leading

to an increase in crashes at managed lane conversion sites,(30) and Gayah used simulation

techniques to show that ramp metering introduced the potential for crash migration upstream 

or downstream of the ramp meter under some conditions.(74) Additional work is needed to
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understand and quantify the crash migration effects of permanent or temporary increases in

capacity or changes in traffic flow characteristics. 

Crashes Occurring Due to Non-Recurring Congestion Resulting from a Primary Incident

Published safety performance evaluations of traffic incident clearance programs were typically

related to effects of these programs on secondary crashes. Secondary crashes are crashes that 

occur in the timeframe beginning with the primary incident and within the boundaries of the 

incident scene or the queue that results from the primary incident, including the opposite 

direction of travel. Analyzing secondary crashes involves defining the boundaries of the primary

incident scene and queue. Goodall noted that there are three different approaches to doing 

this: 1) using a predefined distance from a primary incident, 2) estimating the distance based on 

an estimated deterministic queue, and 3) observing the queue with empirical measurements.(169)

The spatial boundary at which safety performance is affected by non-recurring congestion

resulting from an incident as well as the spatial boundary at which safety performance is 

affected by incident management strategies changes with the characteristics of each incident. 

The amount of existing work in this area is significant and could inform future efforts to 

develop a predictive method that captures interrelationships between road geometry, primary

crashes, incident management, and secondary crashes. 

Corridor, Area, and System Safety Performance Effects of Strategies that Change

Traveler Decisions 

A significant number of TSMO strategies are expected to affect performance at a corridor,

area, or system level. For some strategies, this occurs because the strategy regularly increases

capacity or more effectively utilizes capacity on a facility during the peak period and in the peak 

direction of travel. This additional capacity, and the resulting changes in travel time, may attract

drivers from parallel facilities. If this occurs, the safety performance effects of the strategy at the

location where it is implemented may be different than it would have been if no additional 

traffic was attracted by the increase in available capacity. Examples where this is most likely to

occur include deployments of dynamic lane reversal, part-time shoulder use, and managed lanes. 

For other strategies, corridor-, area-, and system-level effects may occur because the strategies

increase the attractiveness of non-automobile modes and/or incorporate the use of traveler

information, specifically to influence traveler behavior, including whether to make a trip, trip

timing, mode choice, route choice, and destination. Examples include providing traveler

information on alternative modes as part of integrated corridor management, deploying 

dynamically-priced parking, implementing dynamic parking wayfinding, disseminating weather

information to travelers, and implementing transit-only lanes. The spatial extent of the influence 

of traveler information and/or increasing the attractiveness of non-automobile modes on

traveler behavior that are part of these strategies varies, ranging from parallel corridors to an
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entire network. It is unclear whether these changes in traveler behavior can be accurately

measured and, furthermore, whether the changes have a significant effect on safety 

performance. The synthesis of related literature in Chapter 5 showed that the effects on

traveler behavior and resulting traffic patterns were very context-specific and may not lend 

themselves to CMF-type characterizations. Answering these types of questions will likely

require the integration of safety performance analysis with dynamic traffic assignment 

methodologies and tools. The integration of travel demand and safety performance modeling 

would likely occur at a system level and be able to incorporate dynamic changes in travel 

demand resulting from integrated sets of TSMO strategies that target recurring and nonrecurring congestion during any time of day. 

3. Need to Analyze Safety Performance Effects of Traffic Operational Conditions

TSMO strategies affect traffic operational and safety performance. The safety performance 

effects of TSMO strategies will depend on the traffic operational conditions with a strategy in

place and what would have been the traffic operational conditions without the strategy. The 

review of strategies and related safety literature in Chapter 2 uncovered evidence of this 

interrelationship between traffic operational conditions and safety performance effects. In an 

evaluation of ASCT, Ma et al. noted that safety performance effects varied by facility and by 

traffic volume, concluding that this could be due to the differences in traffic operational changes

that resulted from implementation of ASCT on the corridors.(99) Li & Tarko demonstrated that 

the safety performance of a coordinated traffic signal system will depend on the proportion of 

vehicle arrivals during different parts of the traffic signal indications.(93) The MnDOT safety 

performance evaluation of part-time shoulder use showed that safety performance was 

influenced by the presence (and subsequent removal) of upstream and downstream 

bottlenecks.(12) Jang et al. demonstrated that the potential for crashes associated with HOV

facility design varies by lane, and is therefore a function of lane-specific volumes.(34) Fitzpatrick 

and Avelar recommended that future research related to safety performance analyses of 

HOV/HOT lanes consider freeway operational characteristics.(37) It is well known that crash

severity increases with speed, but what is not known is how an increase in operating speed

interacts with changes in other operational measures (e.g., density, speed variance) and in turn

influences safety performance after implementing a TSMO strategy. Chapter 3 explored the

relationship between traffic operational and safety performance in detail, including an overview

of both annual and real-time measures and the associated data needs, methods, and findings. 

There is a need to develop methodologies and tools that incorporate traffic operational 

conditions into the safety performance analysis of TSMO. Depending on the strategy, examples 

of addressing this methodological need include:

• Incorporating macroscopic measures of traffic operations throughout the year into an

analysis of average annual crash frequency. This might include, for example, effects of
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annual average hourly flow, density, and V/C ratio during a defined hour on average 

annual crash frequency during that same hour (e.g., midnight – 1 a.m., 1 a.m. – 2 a.m., 2 

a.m. – 3 a.m.).

• Incorporating macroscopic measures of traffic operations during defined time intervals

into an analysis of crash frequency or crash probability during those time periods and

future time periods. This might include, for example, effects of average volume, average

speed, standard deviation of volume, and standard deviation of speed on crash

probability during the next 5, 10, 15, 20, 25, and 30 minutes from the time of the

macroscopic traffic operations measurement.

Quantifying relationships between traffic operations and safety performance would also inform 

how to operate TSMO strategies from a safety performance perspective. This could include, for 

example, the most desirable operational conditions from a safety performance perspective 

under which to dynamically open (or close) a shoulder as a travel lane. This could also lead to 

methods to achieve desirable operational conditions from a safety performance perspective 

using dynamic lane assignment by increasing or decreasing the number of open travel lanes 

during different times of day.

The synthesis in Chapter 3 covered a growing body of work that has started to address the 

interaction between operations and safety performance in some way. The findings of these 

studies have not made their way into commonly-used safety predictive methods, but they do 

offer a starting point for addressing this safety performance analysis need. 

Linking traffic operational measures to safety performance is also related to the use of 

alternative measures of safety (i.e., safety surrogates). Alternative measures of safety were 

identified by researchers more than 30 years ago as quantifiable observations that can be used

to supplement analyses of crash data.(262) Example alternative measures include different 

characteristics of speed (e.g., magnitudes and variations), traffic densities, lane changing 

behavior, gap acceptance behavior, lane departures and/or encroachments, traffic control 

compliance, stopping behavior (e.g., hard decelerations), and traffic conflicts. Alternative 

measures can, in theory, be used to estimate safety performance indirectly, in lieu of using crash 

data. For example, the use of alternative measures to estimate the safety performance effect of 

one or more safety countermeasures is identified as one possible study approach in FHWA’s A 

Guide to Developing Quality Crash Modification Factors.

(6) As noted in the introductory chapter of 

this report, Tarko et al. recommend that two conditions must be met before an alternative 

measure of safety can be useful in a transportation safety application:(9)

1. The alternative measure should be based on an observable non-crash event that is related

to crashes; and

2. There should be a practical method for converting the changes or differences in non-crash

events into corresponding changes or differences in crash frequency and crash severity.
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In other words, Tarko et al. argue that the key to the application of alternative measures of 

safety is the availability of reliable models to relate crash frequency (by crash type and severity) 

or changes in crash frequency (by crash type and severity) to the alternative measures or 

changes in the alternative measures. In the context of safety performance analyses of TSMO 

strategies, such models would likely be an outcome of continuing to address the methodological 

need of quantifying the level and extent to which traffic operational conditions influence safety 

performance effects. 

The strategy-specific syntheses in Chapter 2 uncovered numerous studies that used surrogate 

safety measures to assess TSMO strategies. These studies mostly focused on managed lanes 

strategies, ramp metering, and variable speed limits. Table 28 shows that these studies used a 

variety of surrogate measures and techniques. FHWA’s Surrogate Safety Assessment Model 

(SSAM) was a commonly used tool in this context. There is a need for increased research 

efforts in validating the link between surrogate measures (such as traffic conflicts) and the 

occurrence and severity of crashes. Of the surrogate-based studies reviewed in this report, 

only a small portion attempted to validate this link. Validating the relationship between 

surrogate measures and crash-based safety performance will provide further opportunities for 

applying these techniques in safety performance assessments of TSMO.
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Table 28. Summary of surrogate safety studies of TSMO strategies.

Study Authors and Year TSMO Strategy Site type Surrogate Measures

Method for 

deriving measure

Link to crashes? Link validated?

Adelakum, A.A. (2008).(45) Managed Lanes 

(Truck lane 

restriction)

Urban freeway Rate of lane change 

maneuvers

Simulation Previous work (Yang 

& Reagan, 2007; Cate 

& Urbanik, 2004) 

Not attempted

El-Tantawy, S., Djavadian, 

S., Roorda, M.J., Abdulhai, 

B. (2009).(46)

Managed Lanes 

(Truck lane 

restriction)

Urban freeway Lane changing, merging, 

rear-end conflicts

Simulation Previous work by Liu 

& Garber (2007) and 

FHWA (2003)

Not attempted

Tao, H. (2015). (44) Managed Lanes Arterial and urban 

freeway

Time to collision (TTC), postencroachment time (PET), 

conflict angle

Simulation SSAM Not attempted

Goh, K.C.K., Currie, G., 

Sarvi, M., & Logan, D. 

(2013). (48)

Managed Lanes Arterial with bus 

lane

TTC, deceleration rate to 

avoid a crash (DRAC)

Simulation SSAM Not attempted

Lee, C., Hellinga, B., & 

Ozbay, K. (2006). (72)

Ramp Metering Urban freeway Coeff. of variation of speed 

at a point, difference in 

speed between 

upstream/downstream 

locations, covariance of 

volume difference between 

upstream/downstream 

locations on adjacent lanes

Simulation Calibrated with 1 year 

of crash, traffic, and 

road geometry data 

from Toronto, CAN 

(study performed in 

California)

Not attempted

Abdel-Aty, M., Dhindsa, A., 

& Gayah, V. (2007). (73)

Ramp Metering Urban freeway Speed variation, loop 

detector occupancy, volume 

variation

Simulation Calibrated using 3 

years of historical 

crash data

Not attempted

Gayah, V. (2006). (74) Ramp Metering Urban freeway Speed variation, loop 

detector occupancy, volume 

variation

Simulation Classification trees 

used to identify 

factors associated 

with crashes

Not attempted

Abdel-Aty, M., Dilmore, J., 

& Dhindsa, A. (2005). (85)

Variable Speed 

Limits

Urban freeway High speed regime: 

occupancy, flow

Simulation Not attempted Not attempted
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

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Study Authors and Year TSMO Strategy Site type Surrogate Measures

Method for 

deriving measure

Link to crashes? Link validated?

Low speed regime: volume, 

occupancy, coeff. of 

variation of speed

Lee, C., Hellinga, B., & 

Saccomanno, F. (2006). (86)

Variable Speed 

Limits

Urban freeway Coeff. of variation of speed 

at a point, difference in 

speed between 

upstream/downstream 

locations, covariance of 

volume difference between 

upstream/downstream 

locations on adjacent lanes

Simulation Calibrated with 1 year 

of crash, traffic, and 

road geometry data

Not attempted

Islam, M.T., Hadiuzzaman, 

M., Fang, J., Qiu, T.Z., ElBasyouny, K. (2013). (88)

Variable Speed 

Limits

Urban freeway Speed variation, loop 

detector occupancy, volume 

variation

Simulation Calibrated using crash 

data

Not attempted

Habtemichael, F.G. & de 

Picado Santos, L. (2013). 

(87)

Variable Speed 

Limits

Urban freeway TTC, PET, conflict angle Simulation SSAM Simulated conflicts 

linked to five years of 

crash data (293 

crashes). Log-quadratic 

curve showed high 

correlation (adjusted 

R2 = 0.923)

Wang, L., Abdel-Aty, M. & 

Lee, J. (2017). 

Variable Speed 

Limits and Ramp 

Metering

Urban freeway TTC Simulation SSAM Not attempted

Stevanovic, A., Stevanovic, 

J., & Kergaye, C. (2013). (97)

Arterial Signal 

Coordination

Signalized arterial 

corridor

TTC, PET, conflict angle Simulation SSAM Validated using 

database of 83 urban 

signalized 

intersections, accuracy 

similar to traditional 

crash-prediction 
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168

Study Authors and Year TSMO Strategy Site type Surrogate Measures

Method for 

deriving measure

Link to crashes? Link validated?

models based on 

traffic volumes.

Stevanovic, A., Kergaye, C., 

& Haigwood, J. (2011). (103)

Adaptive Signal 

Control 

Technology

Signalized arterial 

corridor

TTC, PET, conflict angle Simulation SSAM Attempted, but 

unsuccessful

Li, L., Persaud, B., & 

Shalaby, A. (2017). (113)

Transit Signal 

Priority

Signalized arterial 

corridor

Unstated (likely TTC, PET, 

and conflict angle)

Simulation SSAM Referenced Gettman et 

al. (2008) for “strong 

positive association” in 

the regression 

relationship between 

simulated conflicts and 

observed crash 

frequency.
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

169

The availability of data sources and tools to support analysis of alternative measures of safety

continues to grow. Examples include SSAM, SHRP2 Naturalistic Driving Study (NDS) data, 

probe data from sources such as HERE or INRIX, data from community-based navigation

applications such as Waze, and data from connected and autonomous vehicles. At the time of

this report, NCHRP project 17-86, Estimating Effectiveness of Safety Treatments in the Absence of

Crash Data, was underway to develop a procedural guide for using alternative measures of

safety for developing CMFs and functions and other quantifiable measures in the absence of 

crash data.(49) 

4. Needs Related to Study Design and Statistical Analysis

A significant portion of published safety research on TSMO used naïve before-after or withwithout study designs and analyzed measures such as observed crash rates and observed crash 

frequencies. Methodological issues with these study designs and performance measures are well

documented, including in the Highway Safety Manual, and they are continuing to be phased out 

as options for defensible safety analysis and evaluation.

Generalized linear modeling and a limited amount of non-linear modeling, combined with

Empirical Bayes approaches to estimated expected crash frequency, form the basis of the 

current predictive methods in the Highway Safety Manual. These approaches represent a

significant improvement over safety analysis that relies solely on observed crash rates and

observed crash frequencies. 

Study design and statistical analysis approaches continue to advance. Examples include 

approaches to selecting treatment and corresponding comparison/reference sites for both 

before-after and cross-sectional studies, such as the propensity score-potential outcomes

approach as well as statistical analysis approaches that may more appropriately represent

uncertainty in safety performance effects estimates, such as random parameters and Bayesian 

approaches. Such advancements should be incorporated into future safety performance analyses 

of TSMO. 

5. Need to Explore Mechanistic Approaches to Safety Analysis of TSMO

This needs assessment did not identify any studies in the literature that used mechanistic 

approaches to analyze the safety performance of TSMO. The literature does, however, contain

examples of such approaches in contexts that show potential for future application to TSMO, 

such as Wang et al.’s approach to estimating rear-end crash occurrence at signalized 

intersections.(12) It could be applied to analyzing strategies such as arterial signal coordination

and ASCT and could potentially be modified for analyzing other strategies, such as ramp 

metering and variable speed limits. The fundamental concept of the approach could also extent 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

170

to other mechanisms and associated crash types, such as lane keeping, lane changing, and 

sideswipe crashes. There is a need to advance research on the use and validation of mechanistic 

approaches to quantify the safety performance effects of TSMO.
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CHAPTER 7 — SUMMARY, CONCLUSIONS, AND 

RECOMMENDATIONS

TSMO is an “integrated set of strategies aimed at optimizing the performance of existing 

infrastructure through the implementation of multimodal and intermodal, cross-jurisdictional 

systems, services, and projects designed to improve operations and the reliability of the 

transportation system” (MAP-21, § 1103 (a) (30) (A)). TSMO offers agencies a wide range of 

potential strategies for addressing system- and project-level performance needs with costeffective, tailored strategies. State and local agencies are increasingly recognizing TSMO as core 

business area in support of maximizing the performance of their transportation infrastructure 

and making better use of resources. 

The ability for agencies to quantify the effects of TSMO strategies on the number and severity 

of traffic crashes is limited when compared to similar abilities for operational performance 

measures. This report presents a safety performance analysis needs assessment for TSMO. The 

purpose of this needs assessment was to characterize the current state of safety performance 

analysis practice, knowledge, and skills with respect to specific TSMO strategies and broader 

TSMO program areas, and then to identify gaps and associated research needs. Readers of this 

report will gain an increased understanding of existing knowledge, analysis methods, and 

research needs in the context of quantifying the safety performance effects of TSMO. This 

information will be valuable to staff at various Federal, State, and local transportation agencies, 

as well as universities and other organizations that are involved in safety performance research 

and working to advance performance-based practices in planning, design, operations, and 

maintenance. 

The objective of a safety performance analysis of a TSMO strategy (or combination of 

strategies) is to determine how implementing the strategy (or combination of strategies) will 

affect, or has affected, safety performance. The number, severity, and types of crashes 

characterize the safety performance of a highway segment, intersection, facility, corridor, 

subarea, or network. While safety and TSMO have always had visible interrelationships, there is 

incompatibility between many existing safety performance analysis tools and the characteristics 

of TSMO. For example, few TSMO strategies have robust CMFs and the AASHTO Highway 

HSM analysis methods do not currently incorporate daily, hourly, or sub-hourly variations in 

traffic characteristics and the road environment that are key factors in fully assessing the safety 

performance impacts of TSMO. 

The needs assessment characterized the current state of TSMO-related safety performance 

analysis practice, knowledge, and skills through four sets of activities:

1) Syntheses of TSMO strategy-specific safety performance evaluations and analysis

capabilities (Chapter 2).
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2) A synthesis of research on interrelationships between measures of traffic operational

performance and safety performance (Chapter 3).

3) A synthesis of research relating weather and weather-related road conditions to safety

performance (Chapter 4).

4) A synthesis of research, tools, and challenges in estimating safety performance effects of

TSMO that result from changes in travel choices and traffic demand patterns (Chapter

5).

The syntheses of strategy-specific safety performance evaluations and analysis capabilities 

addresses 15 strategies or sets of strategies:

1) Managed lanes.

2) Part-time shoulder use.

3) Reversible lanes.

4) Dynamic lane use control.

5) Dynamic junction control.

6) Ramp metering.

7) Variable speed limits.

8) Traffic signal coordination.

9) Adaptive signal control technology.

10) Transit signal priority.

11) Truck signal priority.

12) Queue jump lanes.

13) Safety warning applications.

14) Work zone management and temporary traffic control applications.

15) Traffic incident management.

The strategy-specific syntheses show that the literature contains various types of safety 

performance evaluations for some TSMO strategies. However, there are only a limited number 

of published safety performance evaluations that may be robust enough to inform future 

evaluations or the development of quantitative safety performance predictions. There is a need 

for safety and operations staff from local, State, and Federal agencies, as well as researchers and 

other stakeholders, to engage in regular dialogue on safety performance evaluation needs and 

priorities specific to TSMO strategies. TRB activities, AASHTO meetings, and pooled-fund 

efforts are logical forums for such communications. The report presents an overview of 

potential near-term needs for consideration by these groups that cover HOV/HOT lanes, parttime shoulder use, bus facilities and preferential treatments, traffic signal technology and timing 

practices, ramp metering, variable speed limits, dynamic lane assignment, and strategy 

combinations. 

The syntheses of strategy-specific safety performance knowledge and capabilities documented in 

Chapter 2, as well as the syntheses of crosscutting topics contained in Chapters 3, 4, and 5, also 

resulted in insights on methodological (i.e., study design and analysis) issues and related lessons 
SAFETY ANALYSIS NEEDS ASSESSMENT FOR TSMO 

173

learned. These issues and lessons learned led to a set of methodological safety performance 

analysis needs for TSMO, which fell into five categories:

1) “Sub-annual” safety data collection and analysis.

2) Safety performance effects beyond the site level.

3) Safety performance effects of traffic operational conditions.

4) Study design and statistical analysis.

5) Mechanistic approaches to safety performance analysis of TSMO.

The following are offered as next steps in continuing to advance safety performance analysis 

capabilities of TSMO:

Short-Term Steps

1. Continue to monitor ongoing research related to safety performance analyses of TSMO

strategies, including AASHTO-sponsored research on HOV/HOT lanes (NCHRP 17-

89A), part-time shoulder use (NCHRP 17-89) and safety surrogates (NCHRP 17-86)

and FHWA-sponsored research on VSL and ASCT. These efforts may not only result in

valuable strategy-specific information for more commonly-implemented TSMO

strategies, but they may also provide new “lessons learned” for data integration and

analysis approaches that are related to the five areas of methodological needs described

in Chapter 6. Monitoring these projects could involve coordination calls between the

FHWA Office of Safety, FHWA Office of Operations, and the sponsor technical or

panel leads from the FHWA Office of Safety R&D and NCHRP. Annual and midyear

meetings of the following stakeholder groups offer additional opportunities for

coordination:

a. AASHTO: Standing Committee on Safety (formerly SCOHTS) and the HSM

Steering Group.

b. TRB: ANB10 (Transportation Safety Management), ANB20 (Safety Data, Analysis

and Evaluation), ANB25 (Highway Safety Performance), AHB65 (Operational

Effects of Geometrics).

2. Using the CMF Clearinghouse results documented in Chapter 2 of this report, the

FHWA Office of Safety could consider modifying the Clearinghouse search algorithms

so that strategies that fall under the umbrella of TSMO result from user searches for

“TSMO.” Similarly, strategies that fall under the three broader categories or 12 tactical

program areas of figure 1 could result from searches for those terms (e.g., CMFs for

VSL would result from searches for “Traffic Management,” “Freeway Management,”

“Active Traffic Management,” and other relevant categories and program areas).

3. The FHWA Office of Safety and the ITS JPO should consider coordinating the TSMOrelated safety performance information available in the CMF Clearinghouse and ITS

Benefits Database.
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4. Prepare research needs statements that hold promise for near-term success given

experience with strategy-implementation and data availability in the U.S. These research

needs statements could be developed immediately, as they do not overlap with ongoing

research and offer an additional set of opportunities to not only provide valuable

strategy-specific information but provide additional opportunities to implement data

integration and analysis approaches that are related to the five areas of methodological

needs described in Chapter 6. Considering or testing the methodological needs should

be made part of the project scope in the research needs statements. These research

needs statements could cover the following strategies:

a. Safety performance effects of bus facilities and preferential treatments on surface

streets (e.g., bus routes, bus stops, BRT presence and design, queue jump lanes,

and TSP). Results could be integrated into the urban and suburban predictive

methods chapters of the HSM as well as into the CMF Clearinghouse.

b. Safety performance effects of traffic signal technology and timing practices.

Results could also be integrated into the urban and suburban predictive methods

chapters of the HSM as well as the CMF Clearinghouse.

c. Safety performance effects of ramp metering. Results could be implemented into

the freeway and interchange predictive methods chapters of the HSM or

integrated with the HOV/HOT predictive methods in development given the fact

that HOV/HOT facilities and ramp metering can both be present along an urban

freeway facility.

5. Prepare a series of brief “how-to” guides on the use of probabilistic models of crash

occurrence, which have been applied in the literature to evaluate some TSMO strategies

that necessitate shorter analysis periods than traditional crash-based safety performance

analysis. Researchers have also applied these methods to link safety performance and

traffic metrics in real-time by developing models that predict the probability of a crash

occurring for a given set of traffic conditions. The “how-to” guides should describe the

analysis process as well as data needs and supporting tools required.

Long-Term Steps

1. The FHWA Office of Safety should consider a TSMO-specific section of the CMF

Clearinghouse and advance related approaches by which the results of new analysis

methods that will be more common to TSMO (e.g., probabilistic models of crash

occurrence, safety surrogates) can be presented and rated for use by the practitioner

community. This activity could build on the results of two ongoing and planned NCHRP

projects that may provide insights to the reliability of different analysis methods and

therefore inform the star-rating: NCHRP 17-86, Estimating Effectiveness of Safety

Treatments in the Absence of Crash Data, (ongoing) and NCHRP 22-48, Methods for ShortTerm Crash Prediction, (anticipated).
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175

2. Consider a program of strategically identified sets of research projects and project

sequences for TSMO tactical program areas that target broadly defined system-, area-,

and corridor-level management and seek to influence traveler behavior, including trip

timing, mode choice, route choice, and destination. Such tactical program areas include

congestion pricing, parking management, and integrated corridor management.

Development of a strategic research program in these and other related areas could

begin with a one- or two-day workshop that invites leading experts across

transportation demand modeling, operations, safety, data systems, statistical and

econometric analysis, and innovative simulations to present key developments in each

field and outline a prioritized research program surrounding safety analysis of TSMO.

The proceedings of the workshop, along with research needs statements, could be

documented in an FHWA or TRB publication. Examples of processes in other areas of

research include Transportation Research Circular E-C179, Theory, Explanation, and

Prediction in Road Safety and Transportation Research Circular E-C110, Geometric

Design Strategic Research.

(11,265)
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ACKNOWLEDGEMENTS 

This research report is a product of FHWA Office of Safety Task Order 

DTFH6116D00005/0013, Safety Analysis Needs Assessment for Performance-Based Practical Design 

and Transportation Systems Management and Operations. The Task Order was funded through 

Transportation Pooled Fund Study TPF-5(255), Highway Safety Manual Implementation. This 

report was prepared by Dr. Richard J. (R.J.) Porter, Mr. Michael Dunn, Mr. Ian Hamilton, Mr. 

Jeff Gooch, and Dr. Vikash Gayah of VHB. Other members of the project team included Mr. 

Pete Jenior, Mr. Joe Toole, and Dr. Anusha Musunuru of Kittelson & Associates, Inc. and Dr. 

Hugh McGee. 
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Text version of Figure 1. Graphic. Framework for organizing TSMO strategies. Source FHWA.

Management Strategies: Incident/Event Management (Address incidents and events)

Tactical Program Areas Tactics and Functions

Road Weather 

Management

Create weather-related traffic operational response plans; implement variable speed limits; detect road weather conditions and disseminate 

weather and warning information to travelers through dynamic message signs and other information dissemination systems; warn drivers of 

ice, snow, high winds, low visibility and flooded roads; provide emergency truck parking facilities and emergency parking information; and 

implement vehicle restrictions along roadways during inclement weather. 

Traffic Incident & 

Emergency 

Transportation 

Operations

Establish traffic incident management (TIM) teams; conduct after action reviews; stage tow trucks in critical areas; provide roadway safety 

service patrols; integrate computer-aided dispatch (CAD) into transportation management centers (TMCs); pre-establish towing service 

agreements; co-locate dispatch units; develop shared quick clearance goals; pre-plan detour routes; implement hard shoulder running; deploy 

adaptive ramp metering; provide variable speed limits; implement dynamic lane use controls; deploy network surveillance with cameras or 

detectors; implement incident traffic signal timing plans; enact supporting legislation (e.g., driver removal laws, authority removal laws, move 

over laws); and integrate TMC with law enforcement dispatch center/emergency operations center. 

Work Zone 

Management

Coordinate maintenance and construction activities; implement queue length detection; deploy network surveillance with cameras or 

detectors; implement variable speed limits; support dynamic lane merging; implement dynamic lane use controls; deploy temporary ramp 

metering; and implement dynamic warning systems (e.g., queues, congestion, over-dimension vehicles, and work zone intrusions). 

Planned Special Event 

Management

Activate special event signal timing plans; deploy adaptive ramp metering; implement variable speed limits; use hard shoulder running; support 

reversible lanes; implement dynamic lane use controls; establish dynamic pricing; deploy network surveillance with cameras or detectors; 

activate dynamic transit fare reduction; implement dynamic wayfinding; and conduct after action reviews. 

Management Strategies: Traffic Management (Manage movement of people and goods during typical conditions) 

Tactical Program Areas Tactics and Functions

Freeway Management Implement network surveillance with cameras or detectors; deploy high occupancy vehicle/toll lanes; implement adaptive ramp metering; 

deploy hard shoulder running; implement variable speed limits; deploy dynamic lane use control; and construct physical operations 

improvements such as overpass widening, curve corrections, or auxiliary lanes. 

Active Traffic 

Management

Adopt adaptive ramp metering; implement adaptive signal control; deploy dynamic lane reversal; implement dynamic lane use control; deploy 

dynamic merge control; implement hard shoulder running; use variable speed limits; deploy queue length detection; provide transit signal 

priority; and implement dynamic warning signs (e.g., queue, road weather conditions). 

Integrated Corridor 

Management

Implement network surveillance with cameras or detectors; conduct access management/driveway access controls; implement adaptive ramp 

metering; deploy adaptive signal control; deploy transit signal priority; install queue jumping lanes; provide traveler information on alternative 

modes; and deploy emergency vehicle preemption. 

Freight Management Implement roadside truck electronic screening/clearance; deploy truck signal priority; deploy truck parking management systems; install truck 

climbing lanes; and implement freight loading zone policies (e.g., delivery times, designated zones, electronic parking payment systems). 

Arterial Management Install queue jumping lanes; deploy transit signal priority; support emergency vehicle preemption; implement network surveillance with 

cameras or detectors; implement access management/driveway access controls; establish a TMC; deploy enhanced traffic signal operations 

(e.g., re-timing, adaptive detection, better detection); implement transit only lanes; implement truck traffic signal priority; design for complete 

streets; add capacity for critical movements; reduce travel speeds (e.g., speed limit reductions); provide automated enforcement; conduct

operations asset management; and provide enhanced bicycle and pedestrian crossings. 
Management Strategies: Demand Management (Avoid trips or change trip mode, time, or route) 

Tactical Program Areas Tactics and Functions

Congestion Pricing Implement dynamic pricing (variable pricing by lane, segment, time of day, or day of week); establish dynamic transit fare reductions; and 

deploy managed lanes (high occupancy vehicle lanes, high occupancy toll lanes). 

Parking Management Deploy dynamically priced parking; implement dynamic parking wayfinding, reservations, capacity; deploy dynamic overflow transit parking; 

implement electronic payment systems; implement shared-use parking; and provide incentives to use underutilized parking facilities. 

Public Transportation 

and Ridesharing 

Management

Implement dynamic ridesharing; implement transit signal priority; use queue jump lanes at signalized intersections; deploy electronic fare 

collection and integration; and implement transit surveillance and security. 
For More Information:

https://safety.fhwa.dot.gov/

FHWA, Office of Safety

Jerry Roche

Jerry.Roche@dot.gov

515-233-7323

FHWA-SA-19-041