Reliability of Safety 

Management Methods

Countermeasure Selection

FHWA-SA-16-039 August 2016

http://safety.fhwa.dot.gov
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

NOTICE
 

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

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document. This report does not 

constitute a standard, specification, or regulation. The U.S. Government does not endorse 

products or manufacturers. Trademarks or manufacturers' names appear in this report only 

because they are considered essential to the objective of the document. 

Any opinions, findings, and conclusions or recommendations, and translations thereof, 

expressed in this publication are those of the author(s) and do not necessarily reflect the views 

of the Federal Highway Administration. 

QUALITY ASSURANCE STATEMENT 

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

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

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

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

and processes to ensure continuous quality improvement. 

ii 
TECHNICAL DOCUMENTATION PAGE

 

 

 

 

 

 

 

 

 

 

 

 

1. Report No.

FHWA-SA-16-039

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

4. Title and Subtitle

Reliability of Safety Management Methods: Countermeasure Selection

5. Report Date

August 2016

6. Performing Organization Code

7. Author(s)

Geni Bahar, Raghavan Srinivasan, and Frank Gross

8. Performing Organization Report No.

9. Performing Organization Name and Address

VHB

8300 Boone Boulevard

Suite 700

Vienna, VA 22182

10. Work Unit No.

11. Contract or Grant No.

DTFH61-10-D-00022-T-13005

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, March 2015 – August 2016

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The Contracting Officer’s Task Manager (COTM) was Stuart Thompson.

16. Abstract

High quality data and reliable analytical methods are the foundation of data-driven decision-making. The Reliability of Safety Management

Methods series includes five information guides that identify opportunities to employ state-of-the-art (more reliable) methods to support

decisions throughout the roadway safety management process. Four of the guides focus on specific components of the Roadway Safety

Management process: network screening, diagnosis, countermeasure selection, and safety effectiveness evaluation. The fifth guide focuses

on the systemic approach to safety management, which describes a complimentary approach to the methods described in the network

screening, diagnosis, and countermeasure selection guides. The purpose of the Reliability of Safety Management Methods series is to

demonstrate the value of more reliable methods in these activities, and demonstrate limitations of traditional (less reliable) methods.

The Reliability of Safety Management Methods: Countermeasure Selection information guide describes various methods and the latest 

tools to support countermeasure selection. The target audience includes data analysts, project managers, and program managers 

involved in projects that impact highway safety. The objectives of this information guide are to 1) raise awareness of more reliable 

methods, and 2) demonstrate through examples the value of more reliable methods in countermeasure selection. This information guide 

compares more reliable countermeasure selection methods to traditional methods which are more susceptible to bias and may result in 

less reliable estimates and less effective decisions. Readers will understand the value of and be prepared to select more reliable methods 

in countermeasure selection. 

The remainder of this information guide includes five sections. The first section introduces the roadway safety management process and 

countermeasure selection. The second section provides an overview of various methods related to countermeasure selection. The third 

section demonstrates the value of more reliable methods compared to traditional methods for selecting countermeasures. Examples 

highlight the shortcomings of less reliable methods, which may lead to misinformed decisions, non-optimum use of funds, and the 

implementation of less effective treatments. The fourth section summarizes the data requirements to employ the various methods. The 

final section describes available tools and resources to support countermeasure selection. 

17. Key Words:

Roadway, Safety, Data, Analysis, Countermeasure,

Selection, Crash, Modification, Factor

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

34

22. Price

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

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 


 

iii
 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

ACKNOWLEDGMENTS



The project team would like to thank the following individuals who served as members on the 

Federal Highway Administration (FHWA) Office of Safety Technical Working Group for this 

effort. 

Office of Safety Technical Working Group 

Stuart Thompson, FHWA Office of Safety 

Ray Krammes, FHWA Office of Safety 

Yanira Rivera, FHWA Office of Safety 

Karen Scurry, FHWA Office of Safety 

Roya Amjadi, FHWA Office of Safety Research and Development 

Craig Thor, FHWA Office of Safety Research and Development 

Shawn Troy, North Carolina Department of Transportation 

iv
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

TABLE OF CONTENTS
 

1. Introduction to Countermeasure Selection...........................................................................................1
 

2. Overview of Countermeasure Selection Methods...............................................................................4
 

3. Demonstrating The Value of More Reliable Methods.........................................................................7
 

The Haddon Matrix .....................................................................................................................................7
 

Example 1: Application of the Haddon Matrix..................................................................................8
 

Data-Driven Crash-Based Method........................................................................................................ 12
 

Expected versus Observed Crashes ................................................................................................ 12
 

Example 2: Application of SPFs and the EB Method to Estimate Expected Crashes............ 14
 

Standard Errors of CMFs.................................................................................................................... 19
 

Example 3: Application of the Standard Error of a CMF to Estimate Variability ................... 20
 

Applicability of CMFs........................................................................................................................... 21
 

Example 4: Application of Applicable CMFs to Improve Reliability of Estimates................... 22
 

Reliability of CMFs................................................................................................................................ 24



Example 5: Application of High Quality CMFs to Improve Reliability of Estimates .............. 25



Data-Driven Behavior-Based Method .................................................................................................. 27



Example 6: Application of Data-Driven Evidence-based Human Factors Road Safety
 

Knowledge.............................................................................................................................................. 27



Summary of Methods for Selecting Countermeasures..................................................................... 29



4.Data Requirements for Countermeasure Selection........................................................................... 31



5.Tools and Resources for Countermeasure Selection.......................................................................... 32



Using the Roadway Safety Data and Analysis Toolbox .................................................................... 32



References....................................................................................................................................................... 34



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RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

LIST OF TABLES
 

Table 1. Haddon Matrix. ................................................................................................................................. 8
 

Table 2. Example 1 Haddon Matrix for right-angle and left-turn crashes......................................... 10
 

Table 3. Difference between observed and expected crashes............................................................ 17
 

Table 4. CMFs for converting all-way stop-controlled intersection to single-lane roundabout.. 23
 

Table 5. Potential range of CMFs for converting signalized intersections to roundabouts.......... 25



LIST OF FIGURES 

Figure 1. Diagram. Schematic of roadway safety management process. ..............................................2
 

Figure 2. Diagram. Integrating the roadway safety management and project development
 

process ................................................................................................................................................................3
 

Figure 3. Diagram. Diagram of study intersection for example 1..........................................................9
 

Figure 4. Chart. Short-term (observed) and long-term (expected) average crash frequency. .... 13
 

Figure 5. Graph. Example SPF relating crash frequency and traffic volume. .................................... 14
 

Figure 6. Equation. SPF for fatal and injury crashes. .............................................................................. 14
 

Figure 7. Equation. SPF for PDO crashes................................................................................................. 15
 

Figure 8. Equation. Example calculation using SPF for fatal and injury crashes................................ 15
 

Figure 9. Equation. Example calculation using SPF for PDO crashes. ................................................ 15
 

Figure 10. Equation. Weight adjustment for EB method. ..................................................................... 15
 

Figure 11. Equation. Weight adjustment for fatal and injury crashes................................................. 15
 

Figure 12. Equation. Weight adjustment for PDO crashes.................................................................. 15
 

Figure 13. Equation. EB method. ................................................................................................................ 16
 

Figure 14. Equation. Example calculation of expected fatal and injury crashes. .............................. 16
 

Figure 15. Example calculation of expected PDO crashes................................................................... 16
 

Figure 16. Equation. Example calculation of future predicted fatal and injury crashes. ................. 16
 

Figure 17. Equation. Example calculation of future predicted PDO crashes.................................... 16
 

Figure 18. Equation. Example calculation of future expected fatal and injury crashes................... 16
 

Figure 19. Equation. Example calculation of future expected PDO crashes. ................................... 16
 

Figure 20. Equation. Example calculation of future expected total crashes without treatment.. 18
 

Figure 21. Equation. Example calculation of future expected SVROR crashes without treatment.
 

........................................................................................................................................................................... 18
 

vi 
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 22. Equation. Example calculation of change in expected SVROR crashes with treatment. 

........................................................................................................................................................................... 18
 

Figure 23. Equation. Example calculation of future total crashes without treatment based on 

observed crashes. .......................................................................................................................................... 18
 

Figure 24. Equation. Example calculation of future SVROR crashes without treatment based on 

observed crashes. .......................................................................................................................................... 18
 

Figure 25. Equation. Example calculation of change in SVROR crashes with treatment based on 

observed crashes. .......................................................................................................................................... 18
 

Figure 26. Equation. Confidence interval.................................................................................................. 19
 

Figure 27. Equation. Example calculation of confidence interval. ....................................................... 20
 

Figure 28. Equation. Example calculation of lower bound of expected change in SVROR crashes.
 

........................................................................................................................................................................... 20
 

Figure 29. Equation. Example calculation of upper bound of expected change in SVROR crashes.
 

........................................................................................................................................................................... 20
 

Figure 30. Graphic. Screenshot of Roadway Safety Data and Analysis Toolbox............................. 32



Figure 31. Graphic. Screenshot of advanced search feature................................................................ 33



Figure 32. Graphic. Screenshot of filter options from advanced search page. ................................ 33



vii 
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

LIST OF ACRONYMS 

AADT Annual average daily traffic 

AASHTO American Association of State Highway and Transportation Officials 

CMF Crash Modification Factor 

EB Empirical Bayes 

F+I Fatal plus injury 

FHWA Federal Highway Administration 

NCHRP National Cooperative Highway Research Program 

NHTSA National Highway Traffic Safety Administration 

PDO Property damage only 

QRS Quality rating score 

SPF Safety Performance Function 

SVROR Single-vehicle run-off-road 

viii 
 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

PREFACE
 

High quality data and reliable analytical methods are the foundation of data-driven decisionmaking. The Reliability of Safety Management Methods series includes five information guides 

that identify opportunities to employ state-of-the-art (more reliable) methods to support 

decisions throughout the roadway safety management process. Four of the guides focus on 

specific components of the Roadway Safety Management process: network screening, diagnosis, 

countermeasure selection, and safety effectiveness evaluation. The fifth guide focuses on the 

systemic approach to safety management, which describes a complimentary approach to the 

methods described in the network screening, diagnosis, and countermeasure selection guides. 

The purpose of the Reliability of Safety Management Methods series is to demonstrate the value 

of more reliable methods in these activities, and demonstrate limitations of traditional (less 

reliable) methods. 

The Reliability of Safety Management Methods: Countermeasure Selection information guide 

describes various methods and the latest tools to support countermeasure selection. The 

target audience includes data analysts, project managers, and program managers involved in 

projects that impact highway safety. The objectives of this information guide are to 1) raise 

awareness of more reliable methods, and 2) demonstrate through examples the value of more 

reliable methods in countermeasure selection. This information guide compares more reliable 

countermeasure selection methods to traditional methods which are more susceptible to bias 

and may result in less reliable estimates and less effective decisions. Readers will understand the 

value of and be prepared to select more reliable methods in countermeasure selection. 

The remainder of this information guide includes five sections. The first section introduces the 

roadway safety management process and countermeasure selection. The second section 

provides an overview of various methods related to countermeasure selection. The third 

section demonstrates the value of more reliable methods compared to traditional methods for 

selecting countermeasures. Examples highlight the shortcomings of less reliable methods, which 

may lead to misinformed decisions, non-optimum use of funds, and the implementation of less 

effective treatments. The fourth section summarizes the data requirements to employ the 

various methods. The final section describes available tools and resources to support 

countermeasure selection. 

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RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

1. INTRODUCTION TO COUNTERMEASURE SELECTION

The roadway safety management process is a six-step process as shown in Figure 1 and outlined 

in the Highway Safety Manual.(1) The objectives of this process are as follows. 

1. Network Screening: Identify locations that could benefit from treatments to improve

safety performance (i.e., reduce crash frequency and severity). Refer to the Reliability of

Safety Management Methods: Network Screening for further discussion of network

screening and related methods.

2. Diagnosis: Understand collision patterns and crash contributing factors. Agencies

identify crash trends and patterns based on past reported crashes, assess the crash

types and severity levels, and study other elements that characterize the crashes, the

environment, the behaviors of drivers and other road users, the emergency services,

and infrastructure elements such as road geometry and traffic control devices. The

result of diagnosis is a list of contributing factors associated with historical and potential

future crashes. Refer to the Reliability of Safety Management Methods: Diagnosis for

further discussion of diagnosis and related methods.

3. Countermeasure Selection: Identify, assess and select appropriate countermeasures

to target crash contributing factors and reduce crash frequency and severity at identified

locations. The countermeasures should directly target the crash contributing factors,

and may include engineering, education, enforcement, and EMS-related measures (i.e.,

the 4E approach). Refer to the next sections of this information guide for further

information and considerations related to countermeasure selection.

4. Economic Appraisal: Estimate the economic benefit and cost associated with a

particular countermeasure or set of countermeasures. There is not a separate guide for

economic appraisal in the Reliability of Safety Management Methods series because it

involves policy-level decisions such as appropriate crash costs, discount rates, selected

economic method, and non-monetary local considerations. Refer to chapter 7 of the

Highway Safety Manual for further information and considerations related to economic

appraisal.

(1) 

5. Project Prioritization: Develop a prioritized and optimal list of projects to improve

the safety performance (i.e., reduce crash frequency and severity) of the road network,

considering available resources. There is not a separate guide for project prioritization

in the Reliability of Safety Management Methods series because it involves policy-level

decisions such as overall agency goals. Refer to chapter 8 of the Highway Safety Manual

for further information and considerations related to project prioritization.

(1) 

6. Safety Effectiveness Evaluation: Evaluate how a particular treatment (or group of

treatments) has affected the safety performance (crash frequency and severity) of the

treated locations and the system. Refer to the Reliability of Safety Management

Methods: Safety Effectiveness Evaluation for further discussion of evaluation and related

methods.

1 
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

2 

Figure 1. Diagram. Schematic of roadway safety management process. 

The roadway safety management process is an integral part of the roadway infrastructure cycle 

and project development process. While the six-step roadway safety management process 

focuses on safety performance, it provides information for system planning, project planning, 

design and construction, and operations and maintenance of a transportation system. Figure 2 

illustrates the roadway safety management process and how it supports decision-making 

processes, policy-making, and practices throughout the life cycle of a roadway. 

Countermeasure selection is the third step in the roadway safety management process. The 

objective of countermeasure selection is to identify potential countermeasures and select the 

most effective countermeasure(s) to address or mitigate the underlying safety issues identified 

in step 2 (diagnosis) at the specific site. The outcome of the fifth step (project prioritization) 

feeds into the project development process (i.e., system planning, project planning, project 

design and construction, and system operations and maintenance). The results enable decisionmakers, planners, highway designers, and traffic engineers to consider safety-motivated projects 

in conjunction with resurfacing, rehabilitation, reconstruction, and expansion projects. 

Following implementation of a countermeasure or project, it is necessary to evaluate the safety 

effects. The sixth step of the roadway safety management process (safety effectiveness 

evaluation) develops evidence-based safety information to support future decisions. In some 

cases, this evaluation leads to a crash modification factor (CMF). 
 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

CMFs are fundamental in the roadway 

safety management process as well as in 

the project development process. 

Analysts can use CMFs to estimate the 

change in crashes associated with a given 

countermeasure. Step 4 of the safety 

management process (economic appraisal) 

relies on the estimate of the change in 

crashes to determine the cost 

effectiveness of a countermeasure. 

Analysts can also use CMFs to compare 

the safety performance of alternatives 

such as planning-level alternatives, 

individual design elements, and traffic 

control devices. 

A CMF is a multiplicative factor that indicates 

the expected change in crashes associated 

with a countermeasure. If you implement a 

countermeasure with a CMF of 1.0, you would 

not expect a change in associated crashes at 

the particular location. If you implement a 

countermeasure with a CMF less than 1.0, you 

would expect a reduction in associated 

crashes at the particular location. If you 

implement a countermeasure with a CMF 

greater than 1.0, you would expect an increase 

in associated crashes at the particular location. 

Figure 2. Diagram. Integrating the roadway safety management and project 

development process 

3 
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

2. OVERVIEW OF COUNTERMEASURE SELECTION METHODS

During step 3 of the roadway safety management process (countermeasure selection), analysts 

identify and assess potential countermeasures and select the most effective countermeasure(s) 

to address the contributing factors identified in step 2 (diagnosis). The first part of 

countermeasure selection is to develop countermeasures to target the underlying safety issues. 

Analysts can use tools such as the Haddon Matrix and resources such as the NCHRP Report 

500 series to identify targeted countermeasures to address or mitigate underlying contributing 

factors. 

The Haddon Matrix is a tool originally developed for injury prevention, but is directly applicable 

to highway safety in both diagnosis and countermeasure selection. 

(2) For diagnosis, the Haddon 

Matrix is useful to gain a comprehensive understanding of crash contributing factors. Analysts 

can use the Haddon Matrix to identify human, vehicle, and roadway factors contributing to the 

frequency and severity of crashes prior to, during, and after the crash event. Then, analysts can 

identify targeted reactive and proactive countermeasures to address or mitigate the underlying 

contributing factors for the given site. This guide demonstrates the value of using the Haddon 

Matrix as part of a comprehensive approach to identify underlying crash contributing factors 

and targeted countermeasures. Chapter 6 (page 6.2) of the Highway Safety Manual provides 

further discussion of the Haddon Matrix.(1) Refer to the Reliability of Safety Management 

Methods: Diagnosis for further details related to the use of the Haddon Matrix in identifying 

crash contributing factors. 

After identifying potential countermeasures to target 

the underlying issues, there is a need to estimate the Contraindications are negative 

safety impact of countermeasures, individually and in outcomes measured or observed 

combination. Analysts consider positive and negative for specific site conditions after 

safety impacts, including contraindications under the implementation of a given measure. 

specific site conditions (e.g., noise from transverse 

rumble strips near residences). Note countermeasures may produce different results when 

implemented at different sites with different geometric and operational characteristics. 

Subsequent steps of the roadway safety management process (economic appraisal and project 

prioritization) include the consideration of parameters such as constructability, environmental 

impacts, and cost. The following is a summary of three methods, listed in order of increasing 

reliability, for comparing countermeasures: judgment-based, data-driven behavioral-based, and 

data-driven crash-based. 

• Judgment-based: Professional judgment is critical for all aspects of safety management,

including the selection of countermeasures. Based on experience, a person can select

appropriate countermeasures to address or mitigate a given issue. While judgment is

important, a purely judgment-based method is the least reliable for assessing

countermeasures because it is limited by personal experience and susceptible to

personal bias and experience that may be relatively limited. Note the significant benefit

of using a multidisciplinary team to diagnose issues and select countermeasures. Such a

team brings the combined experience, limits the influence of personal bias, and leads to

a multidisciplinary-based selection of countermeasures.

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RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

• Data-driven behavior-based: A data-driven behavior-based method is more reliable

than judgment-based only when assessing countermeasures. This method draws on

results from research studies of previous applications. While the results do not provide

a direct estimate of the expected change in crashes, the performance measures help to

determine the expected effect on road user behavior. Data-driven behavioral-based

performance measures may include speed, conflicts, lane keeping, and compliance with

traffic control devices.

• Data-driven crash-based: A data-driven crash-based method is the most reliable for

assessing countermeasures. This method draws on the results from research studies of

previous applications and provides a direct estimate of safety performance. Analysts use

CMFs to quantify and compare the safety effects of potential countermeasures for the

specific site conditions. While data-driven crash-based methods are the most reliable for

assessing countermeasures, there are a number of considerations to improve the

reliability of results. The following are four specific considerations related to the

application of CMFs in countermeasure assessment.

o Expected versus Observed Crashes: To apply CMFs, there is a need to

estimate future crashes without treatment (i.e., the do-nothing alternative). This

guide demonstrates the value of using the Empirical Bayes (EB) method to

develop a more reliable estimate of future crashes without treatment compared

to historical observed crashes alone (i.e., recorded crash counts). Specifically, the

EB method combines historical observed crashes with predicted crashes from a

safety performance function (SPF) to overcome issues related to the variability in

crashes and changes in traffic volume over time.

o Standard Errors of CMFs: CMFs provide a point estimate of the safety effects

of a countermeasure. This guide demonstrates the value of incorporating the

standard error of the CMF to understand the potential range of safety effects

rather than using the point estimate alone.

o Applicability of CMFs: It is important to assess potential countermeasures in

the context of the given site characteristics to determine their applicability and

anticipated safety effectiveness. Given multiple CMFs for a countermeasure, it is

more appropriate to use an applicable CMF compared to a less applicable, or

generic CMF. A generic CMF is a CMF derived from an evaluation of the

application of a given countermeasure at a mixture of sites such as urban,

suburban, and rural all-way stop controlled intersections. This guide

demonstrates the value of using more applicable CMFs to estimate the safety

benefit of a potential countermeasure compared to generic CMFs.

o Reliability of CMFs: Given multiple CMFs for a specific countermeasure, it is

more appropriate to use a higher quality CMF compared to a lower quality CMF.

CMF quality is determined by the relevant evaluation’s study design, data

attributes, evaluation method and potential biases, sample size, and standard

error. Note the standard error indicates the accuracy and precision of the

resulting CMF. This guide demonstrates the value of using higher quality CMFs

to estimate the safety benefit of a potential countermeasure compared to lower

quality.

5 
 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

The following section describes the use of the Haddon Matrix and data-driven methods in 

countermeasure selection. It begins with a discussion and example of the Haddon Matrix 

because this is the first step in selecting countermeasures. Next, the section provides a 

discussion of data-driven crash-based methods to apply CMFs in countermeasure selection. 

Examples illustrate the potential difference in results by using more reliable methods to apply 

CMFs in a data-driven crash-based method. The section concludes with a discussion and 

example of the data-driven behavior-based method, which is necessary when CMFs are not 

available for the countermeasure(s) of interest. The use of more reliable, data-driven 

countermeasure selection methods will generally lead to the selection of targeted, effective, and 

defensible countermeasures to mitigate specific safety issues. 

6 
 

 

 

 

 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

3. DEMONSTRATING THE VALUE OF MORE RELIABLE METHODS

This section demonstrates the value of applying more reliable methods in countermeasure 

selection to identify targeted countermeasures, quantify the effectiveness of potential 

countermeasures, and consider the range in the effectiveness of potential countermeasures. 

Empirical examples highlight the shortcomings of less reliable methods, which may lead to less 

reliable results and conclusions. Note the examples illustrate the general comparative results of 

the methods. Different scenarios and different data will produce different results. In general, the 

examples demonstrate the value of applying more reliable countermeasure selection methods. 

THE HADDON MATRIX 

The Haddon Matrix is a comprehensive tool to identify 

suitable and effective countermeasures to mitigate a 

substantiated safety issue. During step 2 of the safety 

management process (diagnosis), the multidisciplinary 

analysis team (namely, the Team) reviews past crash 

data in conjunction with attributes of the road users, 

adjacent land use, geometry, and traffic operations of 

the study location. This includes the identification of target crash types and crash contributing 

factors, which provides the foundation for the identification and selection of appropriate 

countermeasures. 

Using the Haddon Matrix will 

help you select more effective 

countermeasures suitable to the 

site of interest. 

The Haddon Matrix supports a comprehensive approach to understanding crash contributing 

factors as shown in Table 1. The Team would complete the Haddon Matrix for each target 

crash type identified during diagnosis. Following the completion of the Haddon Matrix, the 

Team can identify potential countermeasures to directly target underlying contributing factors. 

Less effective countermeasures may result if the Team does not consider the site-specific and 

multiple factors contributing to past or potential crashes. 

The Haddon Matrix is comprised of nine cells to identify human, vehicle, and roadway factors 

contributing to the target crash type or severity outcome before, during, and after the crash. 

Examples of human factors include distraction, fatigue, and seat belt use. Examples of vehicle 

factors include worn brakes, headrest design, and airbag operation. Examples of roadway 

factors include wet pavement, snow-limiting visibility, signal coordination, and steep grade. 

7 
 

 

 

 

Example 1: Application of the Haddon Matrix 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Table 1. Haddon Matrix. 

Period Human 

Factors 

Vehicle 

Factors 

Roadway 

Factors 

Before (causes of hazardous situation) 

During (causes of crash severity) 

After (factors of crash outcome) 

The contributing factors originate from careful review of police crash reports and summary 

data (e.g., collision diagrams), review of design drawings and traffic operations, and observations 

during field investigations. The following is an example application of the Haddon Matrix. 

An agency performed network screening to identify sites that warrant further investigation 

based on their potential for safety improvement. One site is a four-leg signalized intersection in 

an urban area (see Figure 3). During diagnosis, a multidisciplinary team identified right-angle and 

left-turn crashes as target crash types for further consideration. Note one team member 

suggested a red-light-running camera to mitigate right-angle crashes prior to a comprehensive 

investigation. 

The Team then conducted a comprehensive investigation, including the completion of the 

Haddon Matrix based on the data and information collected for right-angle and left-turn 

crashes. Table 2 presents their results. 

After careful study of the Haddon Matrix, the Team summarized their conclusions. First, novice 

drivers accessing the high school located in the southwest quadrant of the intersection have 

inadequate sight distance to southbound vehicles due to the vertical and horizontal alignments. 

Further, left turn movements require experience in checking for pedestrians as well as judging 

gaps (i.e., assessing the approach distance and speed of oncoming vehicles). The combination of 

limited sight distance to and from the intersection, and high approach speeds on the 

southbound approach, contributed to many of the severe right-angle and left-turn crashes. A 

similar safety issue occurs when senior drivers attend community events at the school in the 

evening. Figure 3 presents a diagram of the study intersection with identified issues. 

8 
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 3. Diagram. Diagram of study intersection for example 1. 

9 
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Table 2. Example 1 Haddon Matrix for right-angle and left-turn crashes. 

Period Human Factors Vehicle Factors Roadway Factors 

Before (causes • Drivers running red light • Bald tires • Combination of

of hazardous 

situation) 

- inadequate sight

distance and issues

processing information

(gap judgment)

• Drivers turning left

(northbound-west) ­

inadequate sight distance

and issues processing

information (gap

judgment)

• High speed corridor

leading to high

intersection approach

speeds (downgrade on

southbound approach)

• High school and

community center near

intersection - novice

drivers (daytime hours)

and senior drivers

(evening hours)

horizontal and

vertical alignment

(southbound

approach)

• Limited sight

distance for left-turn

drivers

• Limited sight

distance to

intersection

(southbound

approach)

• Permissive left-turn

phase only

During (causes 

of crash 

severity) 

• Vulnerability to injury • Side impact

• No airbags

• Utility pole on the

sidewalk

After (factors 

of crash 

outcome) 

• Age • No factors

identified

• Emergency response

time

10 
 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Based on the identified target crash types and crash contributing factors, the Team selected the 

following infrastructure-related countermeasures for further consideration. 

• Signal phase modification (short-term): Modify the permissive left-turn phase to a

protected left-turn phase and install a nearside supplemental traffic signal head for

southbound drivers approaching the intersection. Note there is a dedicated left-turn

lane on all approaches.

• Advance warning flashers (short-term): Install an advance warning sign (‘signal

ahead’ or ‘be prepared to stop’) with continuous flashers on the southbound approach.

• Roundabout (long-term): Convert the signalized intersection to a two-lane modern

roundabout with additional alignment improvements to the north leg of the intersection.

In comparison, the Team determined a red-lightrunning camera system is not suitable for this 

intersection. Following the comprehensive 

review, the Team identified limited sight distance 

and high approach speeds as factors contributing 

to right-angle crashes, not driver disobedience. 

Therefore, red-light cameras would not have 

treated the target crash type as demonstrated by 

the comprehensive and holistic method using the 

Haddon Matrix. In conclusion, preventing the 

installation of red-light cameras at this location 

demonstrates the value of using this more 

comprehensive and reliable analysis via the 

Haddon Matrix. 

The agency would pay $30,000 to 

modify the signal phases and $500 for 

the advance warning flashers. Both of 

these improvements are expected to 

mitigate the contributing factors to the 

right-angle and left-turn target crashes. 

In comparison, the agency would pay 

$75,000 for a red-light camera 

installation, which is not likely to 

mitigate the target crashes in this 

particular scenario. This example 

demonstrates the value of using more 

reliable methods to identify targeted 

countermeasures. 

11 
 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

DATA-DRIVEN CRASH-BASED METHOD 

The data-driven crash-based method is the most reliable countermeasure selection method 

because it provides a direct indication of safety performance and minimizes the influence of 

personal bias. This method relies on CMFs to quantify and compare the safety effects of 

potential countermeasures. While CMFs provide a direct comparison of safety performance, 

the following are four considerations related to the application of CMFs. As detailed in the 

remainder of this section, proper consideration of these factors helps to improve the reliability 

of results. 

• Expected versus observed crashes.

• Standard Errors of CMFs.

• Applicability of CMFs.

• Reliability of CMFs.

Expected versus Observed Crashes 

This section demonstrates the value of using expected 

crashes as compared to observed crash history to 

estimate future crashes without the implementation of 

a potential countermeasure (i.e., the do-nothing 

alternative at a future time). This provides a more 

reliable baseline for applying an appropriate, highquality CMF to estimate the safety effectiveness of a 

contemplated countermeasure. More reliable 

estimations of the benefits or disbenefits in terms of 

change to future expected crashes (by type and 

severity) for potential countermeasures will provide 

more defensible selection of countermeasures. Note 

these benefit or disbenefit estimations are also fundamental to a more reliable economic 

appraisal (step 4 of the road safety management process) and subsequent project prioritization 

(step 5 of the road safety management process). A less rigorous alternative to using the 

expected crashes is to estimate future crashes without treatment based on the short-term 

observed crash history. An example is provided later in this section. 

During the past decades, safety professionals studied historical crash records with the intent of 

learning about crash trends. In doing so, they realized the volatility of annual reported crashes 

(i.e., observed crash frequency) and short-term averages did not represent the long-term safety 

performance of a facility.

(3) Figure 4 illustrates this volatility where the short-term average is 

much different from the long-term expected average safety performance. Further, researchers 

determined it is necessary to consider the changes in annual traffic volume when estimating the 

long-term safety performance of a facility. It became evident there was a potential to combine 

this information (i.e., observed crashes and traffic volume) to improve the estimate of the long

term safety performance. As a result, researchers adapted the EB method to meet the need for 

a reliable safety analysis. 

You can achieve a more stable 

measure and overcome the 

volatility of short-term crash 

counts by estimating the longterm safety performance of the 

location of interest. The longterm safety performance is 

expressed as the expected 

average crash frequency. 

­

12 
 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 4. Chart. Short-term (observed) and long-term (expected) average crash 

frequency. 

The Highway Safety Manual and Hauer present the details of the EB method.(1,3) In general, the 

EB method combines the short-term observed crash history with the predicted crashes at the 

site of interest. An equation, namely a safety performance function (SPF), is used to compute 

the predicted crash frequency (by crash severity and by crash type) for a given traffic volume. 

The SPF is a best-fit model relating annual observed crashes to the annual traffic volume for a 

group of sites with similar attributes as shown in Figure 5. In Figure 5, the points represent 

observed crashes at specific traffic volumes for individual sites, and the solid line represents the 

best-fit model (i.e., the SPF). The homogeneity of the individual sites and their combined 

historical data help to overcome the limitations of short-term crashes for any one site. Similar 

sites are typically grouped by the respective facility type such as rural two-lane roads, urban 

four-legged signalized intersections, and rural multilane divided highways. For example, an 

agency may group 300 rural, two-lane road segments to create one site subtype, and separately 

group 60 urban, four-legged, signalized intersections to create another site subtype. 

13 
 

 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 5. Graph. Example SPF relating crash frequency and traffic volume. 

The expected crash frequency estimated with the EB method provides a better estimate of the 

long-term average safety performance than the observed crash history alone.(3) Further, analysts 

can calculate the expected crash frequency with a high level of accuracy even with one or two 

years of recorded crashes for the site(s) of interest. The EB method also allows for the 

estimation of crashes in future years using forecasted traffic volumes, and assuming no other 

changes to the site(s). Analysts can apply CMFs to these more reliable estimates of expected 

crash frequency to facilitate more reliable decision-making. In summary, location-specific traffic 

and crash data, and a relevant SPF, facilitate the use of more reliable methods (e.g., the EB 

method) to estimate crash frequency and severity. Hauer provides evidence of the applicability 

and reliability of the EB method with supporting data-driven examples.

(3) 

The following example demonstrates the value of using SPFs and the EB method to estimate the 

safety impacts of a potential countermeasure. The example also demonstrates the potential 

shortcomings of using short-term crash counts for individual locations, which is also an issue 

when analyzing multiple locations. 

Example 2: Application of SPFs and the EB Method to Estimate Expected Crashes 

This example illustrates the use of SPFs and the EB method for a rural two-lane roadway 

segment of 1.152 mi and an annual average daily traffic (AADT) volume of 4,825 vehicles per 

day. Over the five-year study period, there were 15 fatal and injury (F+I) and 55 property 

damage only (PDO) crashes recorded. An agency developed the SPFs and over-dispersion 

parameters (k) shown in Figure 6 and Figure 7 for rural, two-lane roadway segments. 

Figure 6. Equation. SPF for fatal and injury crashes. 

14 
 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 7. Equation. SPF for PDO crashes. 

where: 

NSPF (F+I) = predicted number of fatal and injury crashes.
 

NSPF (PDO) = predicted number of PDO crashes.
 

L = segment length (mi).
 

AADT = traffic volume (vehicles per day).
 

k = over-dispersion parameter.
 

Figure 8 and Figure 9 show the calculations of predicted crash frequencies for the study period, 

substituting the segment length (1.152 mi) and traffic volume (4825 vehicles per day). 

Figure 8. Equation. Example calculation using SPF for fatal and injury crashes. 

Figure 9. Equation. Example calculation using SPF for PDO crashes. 

Figure 10 shows the equation to compute the weight adjustment for use in the EB method. 

Figure 10. Equation. Weight adjustment for EB method. 

where: 

w = weight adjustment for the predicted crashes. 

All other terms as previously defined. 

Figure 11 and Figure 12 show the weight adjustment factor calculations for F+I and PDO 

crashes. Note the study period is five years and the traffic volume represents the average over 

the five-year period (i.e., predicted crashes are the same for each of the five years). 

Figure 11. Equation. Weight adjustment for fatal and injury crashes. 

Figure 12. Equation. Weight adjustment for PDO crashes. 

Figure 13 shows the equation for the EB method, which is applied to compute the expected 

crashes (Nexpected) for the study period. 

15 
 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 13. Equation. EB method. 

Figure 14 and Figure 15 show the calculations of the expected F+I and PDO crashes. Note the 

computations include crashes per year. As such, the equations include the predicted crashes 

(NSPF) per year (0.555 F+I crashes per year and 1.378 PDO crashes per year) and the observed 

crashes (Nobserved) per year (15/5 = 3.0 F+I crashes per year and 55/5 = 11.0 PDO crashes per 

year). 

Figure 14. Equation. Example calculation of expected fatal and injury crashes. 

Figure 15. Example calculation of expected PDO crashes. 

To estimate the expected F+I and PDO crashes, the SPF is used with the average traffic volume 

for the five-year study period (i.e., 4,825 vehicles per day). For this example, the analysts 

assume the traffic volume will increase in the next few years to an average of 6,500 vehicles per 

day. Assuming the SPF will remain relevant to the following years of crash and traffic data for 

this type of rural two-lane roadway, the predicted F+I and PDO crash frequencies will increase 

proportionally to the increase in traffic volume. Figure 16 and Figure 17 show the computations 

to adjust the predicted F+I and PDO crash frequencies based on the proportion of future to 

present traffic volumes. 

Figure 16. Equation. Example calculation of future predicted fatal and injury 

crashes. 

Figure 17. Equation. Example calculation of future predicted PDO crashes. 

Assuming the same proportional increase, Figure 16 and Figure 17 show the computations to 

estimate the expected crash frequencies for the future period. 

Figure 18. Equation. Example calculation of future expected fatal and injury 

crashes. 

Figure 19. Equation. Example calculation of future expected PDO crashes. 

16 
- -

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Table 3 shows the differences between the five-year observed average crash frequencies based 

on crash counts alone and the expected average crash frequencies from the EB method. 

Table 3. Difference between observed and expected crashes. 

Crash Type Observed Crashes 

(past 5 year period) 

Expected Crashes 

(past 5 year period) 

Expected Crashes 

(future period) 

F+I crashes 3.0 1.76 2.03 

PDO crashes 11.0 8.18 9.44 

This example illustrates the differences that would be carried into estimations of treatment 

effectiveness and economic analyses. The five-year (short-term) average observed PDO crashes 

are 34 percent greater than the five-year average expected (long-term) PDO crashes; the 

average observed F+I crashes are 70 percent greater than the expected. Thus, had the 

observed values been used in the analysis, the calculations would be based on a considerable 

overstating of the severity and frequency of crashes. The use of the state-of-the-art EB method, 

and the resulting expected crashes provides a more reliable (and stable) basis to estimate 

changes in safety performance. The remainder of this example demonstrates how these 

differences propagate through the economic appraisal step and contribute to potential 

misleading estimations. 

During the comprehensive diagnosis step, the crash summaries show 64 percent of all crashes 

recorded along this rural, two-lane road segment were single-vehicle run-off-road (SVROR) 

crashes, with a high proportion of F+I crashes. This proportion is quite higher than the default 

value of 52.1 percent shown in the Highway Safety Manual (Table 10-4), which is based on data 

from the State of Washington.(1) Thus, the Team identified SVROR as the target crash type for 

consideration in countermeasure selection. Further, the Team noted some crash-involved 

drivers might have been fatigued due to the long commuting trips along this corridor. After 

confirming there were no residential or commercial buildings within 1,000 feet, the Team 

suggested shoulder rumble strips, among other countermeasures, to address SVROR crashes. 

While searching the Highway Safety Manual and CMF Clearinghouse for relevant CMFs, the 

Team identified a study by Torbic et al. in the CMF Clearinghouse.(4) The Team downloaded the 

full report via the CMF Clearinghouse and, after reviewing the extensive research study, 

decided to use the CMFs recommended by the researchers for inclusion in the Highway Safety 

Manual. The shoulder rumble strip CMF for all SVROR crashes (all severities) is 0.84 with a 

standard error of 0.08. [Note the following section provides a detailed discussion of the 

standard error of a CMF and an example application.] The shoulder rumble strip CMF for F+Irelated SVROR crashes is 0.64 with a standard error of 0.10. Note these CMFs reflect data 

from similar rural two-lane roadways, with traffic volumes from approximately 900 to 9,000 

vehicles/day. 

17 
 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

To illustrate the calculations, future expected SVROR crashes without treatment are estimated 

from the total future expected crashes without treatment (i.e., the sum of future expected F+I 

and future expected PDO crashes), and assuming a similar proportion of SVROR collisions as 

the observed crash history (i.e., 64 percent of all crashes). The Team then applied the applicable 

CMFs to the future expected SVROR crashes without treatment to estimate the future 

expected SVROR crashes with treatment. Figure 20 through Figure 22 show the computations 

to demonstrate this process. 

Figure 20. Equation. Example calculation of future expected total crashes without 

treatment. 

Figure 21. Equation. Example calculation of future expected SVROR crashes 

without treatment. 

Figure 22. Equation. Example calculation of change in expected SVROR crashes 

with treatment. 

Using the expected crashes from the EB method, the result is an estimated savings of 1.20 

SVROR crashes per year (on average). The estimated safety effects would be different if the 

Team used the average observed crash history to estimate future crashes without treatment. 

Specifically, Figure 23 through Figure 25 show the estimated savings is 1.43 SVROR crashes per 

year (on average) when using the observed crash history. This leads to a relatively larger value 

of safety benefits compared to the use of expected crashes. Specifically, the difference is 1.43 – 

1.20 = 0.23 SVROR crashes/year, as shown below: 

Figure 23. Equation. Example calculation of future total crashes without treatment 

based on observed crashes. 

Figure 24. Equation. Example calculation of future SVROR crashes without 

treatment based on observed crashes. 

Figure 25. Equation. Example calculation of change in SVROR crashes with 

treatment based on observed crashes. 

18 
The results from step 3 (countermeasure selection) of the roadway safety management process 

are brought forward to the next step (economic appraisal) to determine the benefit-cost of 

such a project. It is important to use accurate estimates of the benefit for the economic 

analysis; otherwise, the Team may incorrectly estimate the benefit-cost ratio, and incorrectly 

support or refute the implementation of a contemplated countermeasure. 

In summary, the use of SPFs and the state-of-the-art EB method to estimate expected crashes 

helps to account for the random variation in crashes (i.e., volatility) and changes in traffic 

volume over time. Using the observed crash history, the estimate of SVROR crashes without 

treatment (8.96 crashes/year) is 1.45 crashes/year (19 percent) higher than the expected 

SVROR estimate of 7.51 crashes/year. This is overstating of the annual SVROR estimate 

represents the short-term average crash count without accounting for the regression-to-the

mean, as noted in Hauer.

(3) On a network level, such differences are even more important. 

In conclusion, the use of expected crashes 

provides a more reliable estimate of future You can overcome the issue of shortcrashes without treatment compared to the use term variability of crash counts to 

of observed crashes. Further, the use of expected develop a more reliable estimate of 

crashes provides more reliable estimates of the countermeasure effectiveness by using 

safety benefit, and leads to more defensible the EB method to estimate the 

decision-making regarding whether to implement expected crash frequency and severity 

a countermeasure or not. It is noted the Team for an intersection or a road section. 

can achieve even more reliable estimates of 

expected crashes by applying the EB method for each individual year and then aggregating the 

results (as opposed to using average traffic volume and observed crashes over a multiyear study 

period) as shown in the Highway Safety Manual Part A, Chapter 3, and Part C, Appendix A.

(1) 

­

Standard Errors of CMFs 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

This section demonstrates the value of using a CMF and the associated standard error to 

estimate the range of possible safety benefits versus using the point estimate of the CMF to 

estimate a single value. 

The Highway Safety Manual provides the standard error of the CMF along with the point 

estimate of the CMF.(1) It reads (page 3-22): 

"The standard error of an estimated value serves as a measure of the reliability of that estimate. The 

smaller the standard error, the more reliable (less error) the estimate becomes...Standard error can 

also be used to calculate a confidence interval for the estimated change in expected average crash 

frequency." 

Figure 26 shows the equation used to compute the confidence interval. 

19 

Figure 26. Equation. Confidence interval. 
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Analysts establish the desired level of confidence (e.g., 90 or 95 percent) by selecting the 

probability that the true value of the safety impact of a given countermeasure is within the 

confidence interval. Table 3-3 of the Highway Safety Manual indicates the following values for 

determining confidence intervals using the standard error.

(1) 

• Use a multiplier of 1.0 for a 65 to 70 percent confidence level. • Use a multiplier of 2.0 for a 95 percent confidence level. • Use a multiplier of 3.0 for a 99 percent confidence level. 

Note these multipliers differ slightly from standard statistical texts and provide approximations 

of the confidence interval. The following example demonstrates the value of using the 95 

percent confidence interval, as chosen by the Team, to estimate the range of potential benefits 

when selecting countermeasures. 

Example 3: Application of the Standard Error of a CMF to Estimate Variability 

Continuing the example illustrated in Example 2 above, the CMF for installing shoulder rumble 

strips is 0.84 with a standard error of 0.08. This CMF applies to all single-vehicle run-off-road 

(SVROR) crashes (all severities). Figure 27 shows the computation of the 95 percent confidence 

interval. 

Figure 27. Equation. Example calculation of confidence interval. 

This confidence interval implies, with 95 percent certainty, the true value of the safety effect of 

installing shoulder rumble strips is within the values of 0.68 to 1.0. This range of values indicates 

shoulder rumble strips may reduce future SVROR crashes by up to 32 percent or have no 

impact at all (i.e., CMF of 1.0 implies no change in future crash frequency). 

The Team carried the range of estimates for a selected confidence interval forward to step 4 of 

the safety management process (economic appraisal). Continuing with Example 2, and using the 

EB method, they considered the scenario with 7.51 expected crashes per year without 

treatment. Figure 28 and Figure 29 show the computation of the range of potential effects for 

installing shoulder rumble strips based on the 95 percent confidence interval. 

Figure 28. Equation. Example calculation of lower bound of expected change in
 

SVROR crashes.
 

Figure 29. Equation. Example calculation of upper bound of expected change in 

SVROR crashes.



Assuming an average cost of $100,000 per SVROR crash, the annual benefit of installing 

shoulder rumble strips along this corridor of Example 2 may range from $0 to $240,000. 

[Note in the absence of agency-specific crash costs, analysts may refer to FHWA’s Crash Cost 

Estimates by Maximum Police-Reported Injury Severity within Selected Crash Geometries.](5)

20 
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

To estimate the benefit-cost ratio, convert the annual benefit to a present value and compare 

to the present value of construction and annual maintenance costs. Using the range of annual 

benefits, the Team computed the associated range in benefit-cost ratio. Refer to the Highway 

Safety Manual or FHWA’s CMFs in Practice: Quantifying Safety in the Roadway Safety Management 

Process for further discussion of computing benefit-cost ratios.(1,6) 

The standard error of a CMF provides decisionThe standard error of a CMF for a 

makers with a broader understanding of the given countermeasure determines the potential effects of contemplated countermeasures. interval of possible estimates of the Specifically, the standard error is necessary to safety effect (with the selected level 

estimate the potential range of safety effects and of confidence) after implementation associated crash costs for a given countermeasure. of the given countermeasure. 

Using this information, agencies can calculate the

range of economic returns (positive and negative). 

Alternatively, using only the point estimate (average value) of the CMF results in a single value 

of the potential safety effect, hence, ignoring the potential range of effects, which may even 

indicate a potential increase in crashes, for the countermeasure under certain site conditions. 

Applicability of CMFs 

This section demonstrates the value of using an applicable CMF as compared to a less applicable 

CMF to estimate the safety benefit of a potential countermeasure. The anticipated safety effect 

of a contemplated countermeasure is one factor that influences the decision to implement or 

not implement the countermeasure at a specific site. Note when the safety effectiveness of a 

particular countermeasure can be quantified along with other non-safety parameters, such as 

operational performance, environmental impacts, and cost, these factors are jointly considered 

in the decision-making process. 

Several countermeasures that mitigate contributing factors of a given crash type may be suitable 

and may be considered for different roadway environments and site attributes. Such an example 

is the installation of shoulder rumble strips along rural two-lane highways, multilane highways, 

or freeways. Another example is the conversion of a rural all-way stop-controlled intersection 

or an urban four-leg signalized intersection to a modern roundabout. The safety effect of such 

countermeasures, however, may not be equal in different settings (e.g., the safety effect of 

converting an intersection to a roundabout will likely differ for urban and rural applications as 

well as applications to stop-controlled and signalized intersections). 

 CMFs are the result of safety studies that evaluate the effects of a given countermeasure (or 

combination of measures) under specific site conditions. These site conditions determine the 

applicability of the resulting CMF for use at other similar locations when considering the 

implementation of the given countermeasure. For example, assume a safety evaluation study to 

develop a CMF for shoulder rumble strips includes sites with the following characteristics: 

 • Facility type: two-lane rural highways.

• Traffic volumes: up to 20,000 vehicles per day.

• Shoulder widths: four to six feet. 

21 
 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

The resulting CMF is applicable when considering the installation of shoulder rumble strips for 

similar site conditions. Users should not assume such a CMF is applicable to the installation of shoulder rumble strips on four-lane freeways with traffic volumes up to 45,000 vehicle/day, and shoulder widths of eight feet. Thus, it is important for the analysts to identify the applicability of 

the CMF to their specific project/s. 

Part D of the Highway Safety Manual presents CMFs along with attributes to determine 

applicability.(1) These attributes include the base condition prior to the treatment, the setting (e.g., urban or rural, signal or stop-controlled, etc.), the traffic volume range, and the target crash type and severity. At times, the Highway Safety Manual does not provide details about one or more of these attributes due to missing information in the original study documentation. The CMF Clearinghouse also provides these and other details to help users determine the 

applicability of CMFs. 

The following example demonstrates the value of considering the specific site attributes and 

applicability when selecting and applying a CMF for a potential countermeasure. 

Example 4: Application of Applicable CMFs to Improve Reliability of Estimates 

An agency has selected an intersection as a site with high potential for improvement during 

network screening. The following are specific site attributes. 

• Area type: urban.

• Geometry: four-leg intersection.

• Traffic control: all-way stop-controlled. • Total entering traffic volume: 5,500 vehicles per day.

Subsequently, a multidisciplinary team performed a comprehensive diagnostic investigation, 

using the five-year crash history to identify target crash types, and using the Haddon Matrix to 

identify factors contributing to these crashes. The Team recommended the conversion of the 

stop-controlled intersection to a single-lane roundabout as a potential countermeasure. 

 The Team proceeded to search for suitable CMFs. Part D of the Highway Safety Manual (Table 

14-4) does not include a CMF specifically for such intersection attributes.(1) Instead, there is a

CMF of 1.03 with a standard error of 0.2 (relevant to all crash types and severities combined)

which is applicable to converting all-way stop-controlled intersections to roundabouts for all

settings combined (i.e., urban or rural, one- or two-lane intersections, unspecified traffic

volumes, and all crash types and severities).

(7) This is an example of a non-specific (generic)

CMF.

The CMF Clearinghouse provides other CMFs for the same conversion and includes additional 

site attributes. It also provides separate CMFs for specific site characteristics. Table 4 presents 

the CMFs and associated standard errors for the conversion of all-way stop-controlled 

 intersections to a single-lane roundabout. Table 4 indicates the CMFs vary by the site 

conditions and target crash. In general, the results indicate greater reductions in urban areas 

and for fatal and injury crashes. 

22 
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

For total crashes (all types and severities), the CMFs indicate a 72 percent reduction for 

urban conversions and a 58 percent reduction for rural conversions. For fatal and injury 

crashes, the CMFs indicate an 88 percent reduction for urban conversions, and an 82 percent 

reduction for rural conversions. 

Table 4. CMFs for converting all-way stop-controlled intersection to single-lane 

roundabout.(8) 

Crash Type Crash Severity Area Type CMF Standard 

Error 

All All Urban 0.28 0.11 

All All Rural 0.42 0.13 

All Fatal and Injury Urban 0.12 0.14 

All Fatal and Injury Rural 0.18 0.16 

Based on the specific site characteristics and the results from Table 4, the most applicable CMF 

for total crashes is 0.28±0.11 (at two standard errors, the effect is estimated to be within the 

range of 50 to 94 percent reduction in total crashes) and the most applicable CMF for fatal and 

injury crashes is 0.12±0.14 (at two standard errors, the effect is estimated to be within the 

range of 60 to 100 percent reduction in fatal and injury crashes). Thus, the Team concluded the 

conversion of the all-way stop-controlled intersection to a single-lane roundabout would likely, 

at a 95 percent confidence level, lead to a large reduction in future total crashes and fatal and injury crashes. In comparison, the non-specific (generic) CMF of 1.03±0.2 from the Highway Safety Manual indicates at two standard errors, the effect ranges from a 37 percent reduction 

to a 43 percent increase in future total crashes. 

In conclusion, the CMF for the specific site 

attributes (i.e., conversion of an urban, all-way When you are searching for the estimate 

stop-controlled intersection to a single-lane of the safety effect (CMF) of implementing 

roundabout) is quite distinct to a different a potential countermeasure, the reliability 

setting (e.g., rural or combined settings). The of your estimates and decision making will 

use of non-specific (generic) CMFs could lead increase when using applicable CMFs 

to deferring the option of a roundabout at this developed for the specific characteristics 

site, or lead to less accurate estimations of the of your site of interest. 

safety effectiveness. These estimations will

also affect the subsequent economic appraisal and, project prioritization steps, and the eventual 

project decision-making. The significant positive impact in reduction of total crashes, and most 

significantly in fatal and injury crashes, in the example application demonstrates the value of 

using the applicable CMFs. 

23 
 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Reliability of CMFs 

This section demonstrates the value of using a higher quality CMF as compared to a lower 

quality CMF to estimate the safety effect of a potential countermeasure. 

Two popular sources of CMFs are the Highway Safety Manual and the web-based CMF 

Clearinghouse.(1) The following is a brief summary of the Highway Safety Manual and CMF 

Clearinghouse review and inclusion process. 

• Highway Safety Manual Publication Review Process: Part D of the Highway

Safety Manual includes only the best available research-based CMFs. Prior to accepting

or rejecting a CMF for potential inclusion in Part D, reviewers considered study

characteristics such as the underlying data, the method used to evaluate the safety

effect, and the accuracy and precision of the CMF based on its standard error. The

Highway Safety Manual review process applies an adjustment factor to the study's

originally published CMF to correct for regression-to-the-mean and traffic volume bias,

as needed. The review process also applies a method correction factor to the standard

error of the study's CMF to correct for undesirable study characteristics related to the

study design, sample size, and confounding factors identified during the critical review of

each study. For a detailed description of this procedure and the Highway Safety Manual

inclusion process, refer to the Transportation Research Circular E-C124, Methodology

for the Development and Inclusion of Crash Modification Factors in the First Edition of the

Highway Safety Manual.

(9) 

• CMF Clearinghouse Publication Review Process: The CMF Clearinghouse

includes all crash-based CMFs related to engineering countermeasures; there is no

inclusion criteria. Instead, the CMF Clearinghouse includes a star-quality rating to

indicate the quality of each CMF, when possible. At times, study reviewers were not

able to assess the quality of some CMFs due to the lack of details about the evaluation

study. In these cases, the CMF Clearinghouse marks the quality as “cannot be rated.”

The website describes the star rating based on a set of criteria: “The star quality rating

indicates the quality or confidence in the results of the study producing the CMF. While the

reviewers applied an objective as possible set of criteria, the star quality rating still results from

an exercise in judgment and a degree of subjectivity. The star rating is based on a scale (1 to

5), where a 5 indicates the highest or most reliable rating. The review process to determine the

star rating judges the accuracy and precision as well as the general applicability of the study

results. Reviewers considered five categories for each study — study design, sample size,

standard error, potential bias, and data source — and judged each CMF according to its

performance in each category.” For a detailed description of this process, refer to the

CMF Clearinghouse. 

In conclusion, the analyst should seek applicable higher quality CMFs developed from sites 

similar to the site of interest. An analysis using higher quality CMFs will obtain more reliable 

estimates as shown in the example below. 

24
 

 

 

 

 

 

- - -

 

Crash Type Crash 

Severity 

Highway Safety 

Manual(7) 

CMF 

Clearinghouse 

CMF 

Clearinghouse 

4 Star QRS(10) 4* and 2 

Star** QRS(11) 

All All 0.79-1.19 0.69-0.93 1.78-2.06* 

All Fatal and 

Injury 

0.20-0.60 0.15-0.43 0.52-1.80** 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Example 5: Application of High Quality CMFs to Improve Reliability of Estimates 

Continuing with Example 1 for the four-leg signalized intersection, the Team considered the 

conversion of the signalized intersection to a two-lane modern roundabout. The Team 

identified multiple potential CMFs to estimate the safety effects of the conversion. 

The Highway Safety Manual provides a CMF for converting an urban four-leg signalized 

intersection to a modern roundabout where the number of lanes and traffic volume are 

unspecified (Table 14-3).(1) For total crashes (all crash types and all severities), the CMF is 0.99 

with a standard error of 0.1. For fatal and injury crashes (all crash types with injury severity), 

the CMF is 0.40 with a standard error of 0.1.(7) 

The CMF Clearinghouse provides more recent CMFs estimated from similar installations to the 

site of interest (e.g., urban and suburban, two-lane roundabouts, traffic volumes between 5,300 

and 52,000 vehicles/day). The CMFs scored a 4-star quality rating score (QRS) [Score details: 

study design score: excellent; sample size score: excellent; standard error score: excellent; 

potential bias score: fair; data source score: excellent.] For total crashes (all crash types and all 

severities), the CMF is 0.81 with a standard error of 0.06. For fatal and injury crashes (all crash 

types with injury severity), the CMF is 0.29 with a standard error of 0.07.(10) 

Table 5 presents the ranges of CMFs within two standard errors of the point estimates based 

on the two studies.

(7,10) [Note: refer to Example 3 for additional information about standard 

errors.] 

Table 5. Potential range of CMFs for converting signalized intersections to 

roundabouts. 

Based on the results from Table 5 (Highway Safety Manual(1,7) and 4-Star QRS(10)

), the Team 

concluded that the conversion of the signalized intersection to a modern two-lane roundabout 

would very likely, at a 95 percent confidence level, lead to a large reduction in future fatal and 

injury crashes. The CMFs from the Highway Safety Manual seem to indicate, despite the large 

reduction in fatal and injury crashes (between 40 and 80 percent), the new roundabout may not 

result in any change in the overall frequency of crashes (CMF = 1 is included in the range of 

25
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

possible values) or even an increase up to 19 percent representing an increase in non-injury 

crashes. The CMFs from the 4-Star QRS study found in the CMF Clearinghouse indicate a 

similar large reduction in fatal and injury crashes (between 57 and 85 percent) but also a 

reduction in all crashes (between 7 and 31 percent) including non-injury crashes. 

In comparison, as also shown in Table 5, the 

CMF Clearinghouse provides another non- When you are searching for the estimate 

specific (generic) CMF that shows a different of the safety effect (CMF) of implementing 

safety effect of converting three- or four-leg a potential countermeasure, the 

urban signalized intersections to a modern applicability and reliability of the CMFs 

single- or multi-lane roundabout (traffic may have a decisive role in your decision 

volumes 1,900 to 32,900 vehicles/day).(11)As to implement or not a given 

shown in Example 4, though this study gets a countermeasure. CMFs of higher quality 

4-Star QRS for total crash CMF, it is a generic, with small standard errors and derived 

non-specific CMF for total crashes (i.e., data from more applicable site conditions will 

were combined for single- and multi-lane provide more reliable estimates of future 

roundabouts and three- and four-legged crashes after implementation. 

intersections. The CMF is 1.92±0.07. This 

same study derived the fatal and injury crash CMF which scored a 2-star quality rating for fatal 

and injury crashes. The CMF is 1.16±0.32. [2-star Score details: study design score: excellent; 

sample size score: poor; standard error score: poor; potential bias score: fair; data source 

score: fair.]

(11) It is clear this lower quality CMF indicates the conversion to a roundabout could 

result in more severe crashes; the opposite conclusion reached by other higher quality 

evaluation studies. Using this lower quality CMF could have led to the conclusion of deferring 

the implementation of a two-lane roundabout as an effective countermeasure for this signalized 

intersection. 

26
DATA-DRIVEN BEHAVIOR-BASED METHOD 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

This section demonstrates the value of using a data-driven behavior-based method to select the 

most suitable treatments when CMFs are unavailable or unacceptable. This comprehensive and 

holistic approach supplements the conventional, subjective engineering approach to safety. 

In the process of identifying potential 

countermeasures to mitigate crash contributing When you do not find appropriate 

factors identified during the comprehensive CMFs for potential countermeasures 

analysis (refer to Example 1), CMFs may not be you would like to consider, you may 

available for all contemplated countermeasures or find data-driven behavioral studies can 

available CMFs may not be of sufficient quality. provide you with reliable information 

Countermeasures lacking defensible and sound to support your recommendations. 

CMFs will need a different approach to evaluate 

their suitability and potential safety impact. In the absence of acceptable CMFs for a given 

countermeasure, the application of state-of-the-art human factors in road safety knowledge 

could provide an alternative data-driven evidence-based method. In these scenarios, the analyst 

will need to consider the site and road user attributes when evaluating the potential treatment. 

Module 2 of AASHTOWare Safety AnalystTM provides examples of the holistic approach that 

guides the analyst through a series of engineering, road safety, and human factors questions and 

considerations to reach a list of potential countermeasures. 

Example 6: Application of Data-Driven Evidence-based Human Factors Road Safety 

Knowledge 

The results of a network screening of an urban jurisdiction indicated a higher than expected 

frequency and severity of crashes at numerous stop-controlled, three-leg intersections. 

Subsequently, a multidisciplinary team performed a comprehensive diagnostic investigation, 

using the five-year crash history to identify target crash types, and using the Haddon Matrix to 

identify factors contributing to these target crashes. The Team noted the following contributing 

factors during their diagnosis. 

• Most of the severe collisions involved pedestrians or bicycles hit by vehicles approaching

the crossing with a major arterial and exiting the minor road.

• Most drivers were turning right though some were turning left.

• The cross-sections of the minor roads are relatively wide (36-ft roadways with 8-ft

sidewalks on each side), there are no pavement markings to designate the lanes, and

there are typically no parked vehicles.

• There are relatively few driveways and the 85th percentile vehicle speed is 44 mph

along the minor roads.

In the process of completing the Haddon Matrix, the Team focused on two factors of note: 1) 

the vehicle speeds approaching the intersection from the minor road are much higher than the 

posted speed limit (85th percentile speed is 44 mph compared to the 25 mph posted speed), 

and 2) the police crash reports documented that several drivers noted they had not seen the 

bicyclist or pedestrian at all or in time to prevent the collision. 

27
 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

The Team recommended strategies to slow down the approaching vehicles and increase driver 

expectancy and improve driver visual search for pedestrians and bicyclists. The Team suggested 

two strategies: raised pedestrian crosswalk at the intersections, and speed humps along the 

local streets. 

The Team searched sources for CMFs for the two strategies: 

1. Raised pedestrian crosswalk: The Highway Safety Manual does not provide a CMF

for a raised pedestrian crosswalk.(1) The Team searched the CMF Clearinghouse for

relevant CMFs. The only CMFs found in the CMF Clearinghouse scored a 1-star quality

rating; they are CMF = 0.64±0.5 for all crash types that resulted in injury; and CMF =

0.55 ± 0.95 for injury vehicular-pedestrian collisions (in urban and suburban local

streets).(12) Given the low quality rating associated with the CMFs, the Team decided to

search for other data-driven studies to support their suggestions.

2. Speed humps: The installation of speed humps has been evaluated along road

segments, and CMFs were found; CMF = 0.6±0.16 for all crash types that resulted in

injury (in urban and suburban local streets).(12) The Highway Safety Manual (Table 13-48)

presents this CMF. This CMF resulted from a meta-analysis evaluation and scored a 4

star quality rating by the CMF Clearinghouse reviewers. The Team accepted the CMF

for speed humps along road segments, but also considered the effect of speed humps on

vehicle speeds at the intersection. As such, the Team proceeded to search for additional

data-driven information about speed reduction and driver visual search for pedestrian

and bicyclists at intersections.

­

The Team consulted Chapter 2 of the Highway Safety Manual.

(1) This chapter introduces 

elements of human factors related to the interaction between driver, pedestrian, bicyclists, and 

between the road users and the road design and operations. In Section 2.5, the Highway Safety 

Manual identifies inadequate visual search as one of the errors contributing to collisions: 

"Drivers turning right may concentrate their visual search only on vehicles coming from the left and fail 

to detect a bicyclist or pedestrian crossing from the right..." (page 2-14). 

The Team continued their information search by consulting the 3rd edition of Human Factors in 

Traffic Safety.

(13) Chapter 17 describes visual search in the context of traffic control devices. 

Two studies mentioned in the chapter researched driver visual search of pedestrians and 

bicyclists when approaching and turning at intersections. Empirical data and sound data 

collection of road user behavior supported the results of the research studies. The Team 

obtained a copy of both publications aiming to review the studies and their findings. If 

applicable, the findings could provide evidence-based support for countermeasure selection. 

The following is a description of the findings from these publications: 

Robinson et al. observed drivers at three-leg, stop-controlled intersections in an urban area.

(14) 

The visual searches were recorded using video cameras during turning movements. The 

processing of the field recording data showed most visual searches by the drivers were carried 

out within a region of 43 feet from the edge of the main road to 6.6 feet into the main road, as 

drivers were slowing to a stop. Depending of the presence of traffic to the left and right, mean 

28
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

total search times ranged from 7.4 to 10.4 seconds for right turns and 6.7 to 16.9 seconds for 

left turns, including search time while in motion on the approach. Average individual glances 

lasted from 1.1 to 2.6 seconds, with the shortest and fewer glances to the right when drivers 

were making right turns. The search time increased when there was traffic on both directions 

on the main road. Some drivers making right turns did not look to the right at all, focusing 

solely on traffic coming from the left, raising the risk of a collision with a pedestrian or bicyclist 

coming from the right. 

Similar to Robinson et al., Summala et al. recorded the empirical field data with two video 

cameras at three-leg, uncontrolled intersections.(15) The researchers used these data to measure 

the head movements of the approaching drivers and the speed and distance from the crosswalk. 

In this first phase of the study, the data confirmed drivers making a right turn focus their 

attention on the traffic coming from the left, and fail to see approaching bicyclists from the 

right. Drivers making a right or left turn already started to search traffic coming from the left at 

approximately 100 ft from the crosswalk. The drivers making a left turn continue searching for 

traffic approaching from left or right even after entering the main road. Drivers making a right 

turn stopped searching for traffic approaching from left at approximately 10 ft from the 

crosswalk, and the few and short glances to the right completely stopped at approximately 20 ft 

from the crosswalk. 

The researchers devised another phase to the study driver behavior after implementation of 

the countermeasures. The results of the second phase were then compared with the results of 

the first phase. Countermeasures included speed humps and a raised crosswalk. For each 

countermeasure, the researchers collected data to measure head movements of approaching 

drivers and the speed and distance from the crosswalk. The researchers concluded speed 

humps effectively reduced the speed of approaching drivers and consequently provided drivers 

more time to focus on each traffic direction. The raised crosswalk increased the number of 

drivers that glanced to the right. This well-designed, data-driven evaluation demonstrated the 

two countermeasures modified driver behavior in a positive manner after implementation. 

The Team concluded that the evidence based on these data-driven behavioral studies is reliable 

and applicable to the characteristics of the study locations (i.e., urban, three-leg, stop-controlled 

intersections). [Note the applicability of results from data-driven behavioral studies is just as 

important as the applicability of CMFs for estimating potential safety effects.] Given the 

availability and applicability of data-driven evidence to support the countermeasures, the Team 

recommended the installation of speed humps and raised crosswalks at the urban, three-leg, 

stop-controlled intersections. This approach overcame the lack of CMFs for speed humps and 

raised crosswalks for the specific conditions of urban, three-leg, stop-controlled intersections. 

SUMMARY OF METHODS FOR SELECTING COUNTERMEASURES 

This guide demonstrates the value of applying more reliable methods in countermeasure selection. 

More reliable methods result in the identification of targeted, effective, and defensible 

countermeasures. These methods help to understand how to incorporate crash contributing 

factors into the countermeasure selection process, identify targeted countermeasures, quantify the 

29
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

safety effects, and consider the range in safety effects. The following is a brief summary of more 

reliable methods in countermeasure selection. 

• The Haddon Matrix: The Haddon Matrix is a useful tool to understand crash contributing

factors and identify targeted countermeasures for the study location. The first example

demonstrates the value of using the Haddon Matrix as part of a comprehensive approach to

identify focus crash types, underlying crash contributing factors, and targeted countermeasures.

• Data-Driven Crash-Based Method: After identifying potential countermeasures to target

the underlying issues, there is a need to assess the safety impact of potential countermeasures.

Analysts can use CMFs to quantify and compare the safety effects of multiple potential

countermeasures. Examples 2 – 5 demonstrate the value of using CMFs to quantify the safety

effects of contemplated countermeasures. The following are specific considerations related to

the application of CMFs in countermeasure selection.

o Expected versus Observed Crashes: To apply CMFs, there is a need to estimate

future crashes without treatment (i.e., the do-nothing alternative). The second example

demonstrates the value of using the EB method to develop a more reliable estimate of

future crashes without treatment compared to historical crash records only. Specifically,

the EB method combines historical observed crashes with predicted crashes from an SPF

to overcome issues related to the variability in crashes and traffic volume over time.

o Standard Errors of CMFs: CMFs provide a point estimate of the safety effects of a

countermeasure. The third example demonstrates the value of incorporating the standard

error of the CMF to understand the potential range in safety effects.

o Applicability of CMFs: Given multiple CMFs for a specific countermeasure, it is more

appropriate to use an applicable CMF compared to a less applicable, or generic CMF. The

fourth example demonstrates the value of using more applicable CMFs to estimate the

safety effect of potential countermeasures.

o Reliability of CMFs: Given multiple CMFs for a countermeasure, it is more appropriate

to use a higher quality CMF compared to a lower quality CMF. The fifth example

demonstrates the value of using more reliable CMFs to estimate the effect of potential

countermeasures.

• Data-Driven Behavior-Based Method: The data-driven behavior-based method is useful

to select the most suitable treatments when applicable CMFs are not available. This is an

alternative to the conventional, subjective engineering approach to safety. The sixth example

demonstrates the value of a comprehensive and holistic approach when CMFs are not available

for the potential countermeasure or specific site conditions of interest.

In summary, the use of more reliable countermeasure selection methods will lead to the 

identification and selection of targeted, effective, and defensible countermeasures to mitigate 

specific safety issues at given sites. 

30
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

4. DATA REQUIREMENTS FOR COUNTERMEASURE SELECTION

The following is a brief overview of the data requirements for countermeasure selection using 

the more reliable state-of-the-art methods described in this information guide. 

• Crash Data: three to five years of police crash reports for the study location. Review

of individual crash reports to identify contributing factors as reported by the police. This

information supports the completion of the Haddon Matrix for each target crash type. 

• Traffic Volume Data: three to five years of traffic volume data for the study location,

as well as forecasted traffic volumes for the projected service life of contemplated

countermeasures. As a minimum, there is a need for at least one historical estimate and

one future estimate that represents a potential implementation year.

• Roadway Data (Site Conditions): From the diagnosis step, carry forward the

observations from site visits (field investigations), including traffic operations, design

elements, adjacent land use, driver demographics, and other elements. This information

supports the completion of the Haddon Matrix for each target crash type. A preliminary

office review of the site using aerial images and video or photo logs can support a more

efficient field investigation.

• SPFs: Obtain appropriate SPFs for the study location. SPFs may be available for a given

jurisdiction or borrowed from another jurisdiction with sites of similar conditions to the

study location. In either case, there is a need to calibrate SPFs using local data to reflect

local and current conditions. [Note analysts may use FHWA’s calibration tool, The

Calibrator, and related user guide to calibrate SPFs.]

• Crash Costs: To complete an economic appraisal, there is a need for average crash

costs by crash type and severity level. [Note in the absence of agency-specific crash

costs, analysts may refer to FHWA’s Crash Cost Estimates by Maximum PoliceReported Injury Severity within Selected Crash Geometries.](5) 

• Countermeasure Details: For each potential countermeasure, there is a need to

identify the expected safety effects post implementation (for the specific site conditions)

and reported contraindications (e.g., noise of rumble strips near residential

developments). The Highway Safety Manual and CMF Clearinghouse provide CMFs and

associated standard errors for various countermeasures. These sources indicate the

applicability of the CMFs with respect to site condition, crash type, and crash severity.

• Transport Research International Documentation (TRID): Access TRID for a

centralized searchable database of literature related to road safety. This is useful to

identify full text reports or publications related to contributing factors or

countermeasures of interest. For example, analysts may search for CMF-related

publications to understand the context of the original study. When applicable, highquality CMFs are unavailable, analysts may search for data-driven human factors

research in road safety as an alternative evidence-based approach to countermeasure

selection.

31
 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

5. TOOLS AND RESOURCES FOR COUNTERMEASURE SELECTION

Tools and resources are available to support countermeasure selection, including guides and 

software. Some guides provide a discussion of the countermeasure selection process, while 

other tools relate to specific components of the process. For example, tools such as the 

National Cooperative Highway Research Program (NCHRP) Report 500 series can help users 

identify appropriate countermeasures for a given safety issue. The FHWA CMF Clearinghouse 

and related resources can help users identify and apply CMFs. 

The FHWA Roadway Safety Data and Analysis Toolbox is a web-based repository of safety data 

and analysis tools. Use the Toolbox to identify an appropriate tool for your countermeasure 

selection needs. A Primer is available to understand the overall scope and functionality of the 

Toolbox as well as the roles, responsibilities, and tasks supported by tools in the Toolbox. 

USING THE ROADWAY SAFETY DATA AND ANALYSIS TOOLBOX 

There are two primary options for searching the Toolbox. The first is a predefined query using 

the four large icons in the upper right of Figure 30 (Manage, Analyze, Collect, and Research). 

The second is an advanced search option where users can search keywords and apply filters to 

customize their search as shown in the lower left of Figure 30. 

Figure 30. Graphic. Screenshot of Roadway Safety Data and Analysis Toolbox. 

The following is a brief demonstration of the stepwise process to identify an appropriate tool 

to support countermeasure selection. 

1. Click the ‘Advanced Search’ icon, highlighted in the lower left of Figure 30.

2. From the advanced search page (Figure 31), leave the keyword blank and click the

search button. This returns a list of all tools in the Toolbox.

32
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

Figure 31. Graphic. Screenshot of advanced search feature. 

Click the ‘Show/Hide Filters’ button, highlighted in the upper left of Figure 32. This reveals a list 

of filters to refine the general search. Use the ‘Safety Management Process’ filter to select 

‘Countermeasure Selection’ as the primary area of interest as shown in Figure 32. Apply 

additional filters as needed to refine the results. For example, apply the ‘Tool Type’ filter to 

narrow the list of tools to application guides, information guides, software, information sources, 

or databases. 

Figure 32. Graphic. Screenshot of filter options from advanced search page. 

Using the stepwise process described in this section, the Toolbox returns guides such as 

Proven Safety Countermeasures, Countermeasures That Work, CMFs in Practice, and Human 

Factors Guidelines for Road Systems. The CMFs in Practice series describes the use of CMFs in 

specific aspects of the project development process. Other related tools from the Toolbox 

include the CMF Clearinghouse and AASHTOWare Safety AnalystTM. 

33
RELIABILITY OF SAFETY MANAGEMENT METHODS: COUNTERMEASURE SELECTION
 

REFERENCES
 

1. American Association of State Highway and Transportation Officials (AASHTO), Highway Safety

Manual, First Edition, Washington, DC, 2010.

2. Haddon, W., Jr. (1972). A logical framework for categorizing highway safety phenomena and

activity. Journal of Trauma 12: 193–207.

3. Hauer, E. (1997), Observational Before-After Studies in Road Safety, Pergamon, Elsevier Science

Ltd.

4. Torbic, D.J., Hutton, J.M., Bokenkroger, C.D., Bauer, K.M., Harwood, D.W., Gilmore, D.K., Dunn,

D.K., Ronchetto, J.J., Donnell, E.T., Sommer III, H.J., Garvey, P., Persaud, B., and Lyon, C. (2009),

NCHRP Report 641: Guidance for the Design and Application of Shoulder and Centerline

Rumble Strips, Transportation Research Board, Washington, DC.

5. Council, F., Zaloshnja, E., Miller, T., and Persaud, B. Crash Cost Estimates by Maximum PoliceReported Injury Severity within Selected Crash Geometries. Publication FHWA-HRT-05-051,

Federal Highway Administration, McLean, VA, 2005.

6. CMFs in Practice: Quantifying Safety in the Roadway Safety Management Process. Federal

Highway Administration, Report FHWA-SA-13-010, Washington, DC, 2013. Available online:

http://safety.fhwa.dot.gov/tools/crf/resources/cmfs/management.cfm 

7. Rodegerdts, L., Blogg, M., Wemple, E., Myers, E., Kyte, M., Dixon, M., List, G., Flannery, A.,

Troutbeck, R., Brilon, W., Wu, N., Persaud, B., Lyon, C., Harkey, D., and Carter, D. (2007),

NCHRP Report 572: Applying Roundabouts in the United States. Transportation Research Board,

Washington, DC.

8. Persaud, B. N., Retting, R. A., Garder, P. E., and Lord, D. (2001), Observational Before-After

Study of the Safety Effects of U.S. Roundabout-Conversions Using the Empirical Bayes Method,

Transportation Research Record, No. 1751, Transportation Research Board, National Research

Council, Washington, DC.

9. Bahar, G. Methodology for the Development and Inclusion of Crash Modification Factors in the

First Edition of the Highway Safety Manual. Transportation Research E-Circular, Issue E-C142,

Transportation Research Board, Washington, DC, 2010.

10. Gross, F., Lyon, C., Persaud, B., and Srinivasan, R. (2013), Safety Effectiveness of Converting

Signalized Intersections to Roundabouts, Accident Analysis and Prevention, Volume 50, January,

pp. 234-241.

11. Russo, B.J., Davis, A., Frazier, S., Savolainen, P.T., and Gates, T.J. (2014), Evidence of Mixed Safety

Performance among Roundabouts Converted from Signalized Intersections, Presented at the 93rd

Annual Meeting of the Transportation Research Board, Paper No. 14-0046, Washington, DC.

12. Elvik, R. and Vaa, T. (2004), Handbook of Road Safety Measures, Elsevier Science Ltd.

13. Smiley, A. (2015), Human Factors in Traffic Safety, Third Edition, Lawyers and Judges Publishing

Company, Inc.

14. Robinson, G.H., Erickson, D.J., Thurston, G.L, and Clark, R.L. (1972), Visual Search by Automobile

Drivers, Human Factors, 14 (4), pp. 314-323.

15. Summala, H., Pasanen, E., Rasanen, M., and Sievanen, J. (1996), Bicycle Accidents and Drivers'

Visual Search at Left and Right Turns, Accident Analysis and Prevention, Volume 28, No. 2, pp.

147-153, Elsevier Science Ltd.

34
For More Information: 

Stuart Thompson 

Stuart.Thompson@dot.gov 

202-366-8090

Federal Highway Administration 

Office of Safety 

1200 New Jersey Avenue SE 

Washington, DC 20590 

http://safety.fhwa.dot.gov/ 

Publication # FHWA-SA-16-039