UNIT 4: Solving Safety Problems

Road Safety 

Fundamentals

Concepts, Strategies, and Practices that 

Reduce Fatalities and Injuries on the Road
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1. Report No.

FHWA-SA-18-003

4. Title and Subtitle

Road Safety Fundamentals:

Concepts, Strategies, and Practices that Reduce Fatalities and Injuries

on the Road

7. Author(s)

Lead Editor: Daniel Carter, P.E., Senior Research Associate

Unit Authors: 

Unit 1: Dan Gelinne, Program Coordinator, UNC Highway Safety 

Research Center 

Unit 2: Bevan Kirley, Research Associate, UNC Highway Safety Research 

Center 

Unit 3: Carl Sundstrom, P.E., Research Associate, UNC Highway Safety 

Research Center 

Unit 4: Raghavan Srinivasan, Ph.D., Senior Transportation Research 

Engineer; Daniel Carter 

Unit 5: Daniel Carter; Jennifer Palcher-Silliman, Communications 

Manager, UNC Highway Safety Research Center

Layout and Graphics: Graham Russell, Graphic Designer, UNC Highway 

Safety Research Center

Technical Editors: Jennifer Palcher-Silliman; Patty Harrison, 

Communications Specialist, UNC Highway Safety Research Center; 

Caroline Mozingo, Senior Manager of Communications, Education and 

Outreach, UNC Highway Safety Research Center

9. Performing Organization Name

and Address

University of North Carolina,

Highway Safety Research Center

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety

1200 New Jersey Ave., SE

Washington, DC 20590

14. Sponsoring Agency Code

15. Supplementary Notes

The project manager for the textbook was Felix H. Delgado, P.E., FHWA Office of Safety

16. Abstract

This book provides an introduction to the fundamental concepts of road safety. The book’s goal is to equip

the reader with a broad base of knowledge about road safety. Thus, the focus is in communicating concepts

rather than providing instruction in detailed analysis procedures.

The audience for this book is two-fold. First, this is intended for those whose job addresses some aspect of 

road safety, particularly in a public agency setting. Second, this book is intended for professors and 

students in a university setting.

This book seeks to lay the foundation of road safety knowledge regardless of a particular discipline. 

Professionals with a background in engineering, planning, public health, law enforcement, and other 

disciplines will benefit from the concepts presented here.

17. Key Words

road safety, road safety management process, site-level management, system

level management, human behavior, safety performance measures, road safety

data, multidisciplinary approaches

18. Distribution Statement

No restrictions

19. Security Classif.

(of this report)

Unclassified

20. Security Classif.

(of this report)

Unclassified

21. No. of Pages

188

22. Price

FREE

5. Report Date

November 2017

6. Performing Organization Code

8. Performing Organization Report No.

10. Work No. (TRAIS)

11. Contract or Grant No.

13. Type of Report and Period Covered

Text book

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

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

TABLE OF CONTENTS

ROAD SAFETY FUNDAMENTALS i

UNIT 4 Solving Safety Problems Chapter 10: Road Safety Management Process

 Chapter 11: Site-Level Safety Management

 Chapter 12: System-Level Safety Management

UNIT 5 Implementing Road Safety Efforts

 Chapter 13: Who Does What

 Chapter 14: Road Safety Research

 Chapter 15: Strategic Communications

 Chapter 16: Advancing Road Safety

UNIT 1 Foundations of Road Safety

 Chapter 1: Context of Road Safety

 Chapter 2: Road Safety Through the Years

 Chapter 3: Multidisciplinary Approaches

 Chapter 4: Road Users

UNIT 2 Human Behavior and Road Safety

 Chapter 5: Understanding Human Behavior

 Chapter 6: Changing Human Behavior

UNIT 3 Measuring Safety

 Chapter 7: Importance of Safety Data

 Chapter 8: Types of Safety Data

 Chapter 9: Improving Safety Data Quality
4-b UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

After reading the chapters and completing 

exercises in Unit 4, the reader will be able to:

J IDENTIFY three major components 

of road safety management

J DEFINE the process of conducting 

site-level and system-level 

safety management

J USE safety data to identify safety 

issues and develop strategies to 

solving those issues

LEARNING OBJECTIVES

Solving Safety 

Problems

UNIT 4
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-1

Road Safety Management

Road safety management refers to 

the process of identifying safety 

problems, devising potential 

strategies to combat those safety 

problems, and selecting and 

implementing the strategies. 

Effective safety management is 

also proactive and looks for ways 

to prevent safety problems before 

they arise. High quality safety data 

should be used to determine the 

nature of the road safety problems 

and how best to solve them. As 

discussed in Unit 3, the clearest and 

most readily available indicators of 

road safety problems are crash data. 

These data can be used to identify 

safety problems on a large or a small 

scale. Other data, such as roadway 

characteristics, traffic volume, 

citations, and driver history, can be 

integrated with crash data to assist 

in identifying safety trends and high 

priority locations.

Data quality issues should not 

prevent a data-driven process

Every transportation agency will 

acknowledge that it does not have 

perfect data. All data have issues 

related to accuracy, coverage, 

timeliness, and other factors. One 

agency’s crash data may have an 

incomplete record of low severity 

crashes. Another agency may 

have very little data on the traffic 

volume on low volume rural roads. 

However, data quality issues should 

not prevent a transportation agency 

from using the data to drive its 

safety management efforts. Even 

while the agency strives to improve 

its data, the data on hand should be 

used in the process of identifying 

safety problems and devising 

solutions to those problems.

Data needs for safety analysis

High quality safety analysis 

demands high quality data. 

Unfortunately, poor data availability 

and low quality limit the types of 

analyses that can be conducted. 

The data requirements depend on 

the type of analysis and what safety 

questions are being asked. Table 

4-1 provides examples of various 

categories of safety analysis and lists 

the data that would be needed to 

conduct them.1

CHAPTER 10 UNIT 4: SOLVING SAFETY PROBLEMS

Applying Safety 

Data and Analysis to 

Performance-Based 

Transportation 

Planning, 

e-Guidebook, 

FHWA, http://safety.

fhwa.dot.gov/tsp/

fhwasa15089/

appb.cfm

1
4-2 UNIT 

4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

TABLE 4-1: Safety analysis categories, questions, tools and data needs. SAFETY ANALYS

QUESTIONDTA NEED

BENCHMARKI

How many fatalities 

and serious injuries are 

occurring in my area? 

How does this 

compare to other 

areas of my State?

Total crashes

Total fatalities and serious injuries

High-level roadway data — roadway ownership, 

functional classification Agency geographic boundary informatio

CRASH TRENDS ANCONTRIBUTING FACTO

What type of road users 

are involved in crashes? 

When are the 

crashes occurring? 

What are the major 

contributing factors 

to crashes?

Crash severity — fatality, injury type, 

property damage only

Crash incidence data — time of day, day, 

month, weather, etc. 

Crash type — road departure, intersection, 

head-on, angle, etc. 

Contributing factors — age, impairment, 

seatbelt usage, speed, etc.

SITES FOR SAFETY IMPROVEMEN

What locations 

(intersections or 

segments) show the 

most potential for 

safety improvements?

Crash severity

Crash location

Roadway and roadside characteristics — intersection 

control, number of lanes, presence and type of 

shoulder, presence and type of median, posted 

speed, horizontal and vertical alignment, etc. 

Traffic volume data — intersection total entering 

traffic volume, roadway segment volume per 

million vehicle miles. 

Calibrated safety performance functions, 

if predictive methods are used

SAFETY RISK FACTO

What are the common 

characteristics of 

locations with crashes? 

What are the 

countermeasures 

to address these 

characteristics? 

How should we prioritize 

system-wide 

implementation?

Crash severity

Crash location

Roadway and roadside characteristics — intersection 

control, number of lanes, presence and type of 

shoulder, presence and type of median, posted 

speed, horizontal and vertical alignment, etc. 

Traffic volume data — intersection total entering 

traffic volume, roadway segment volume per 

million vehicle miles.
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-3

Safety data as 

performance measures

A transportation agency has many 

types of data at its disposal for 

identifying safety problems, but the 

agency must select which type(s) 

of data will be the performance 

measures used to identify the road 

safety emphasis areas. Federal 

legislation has focused increasingly 

on fatal crashes and serious injury 

crashes as performance measures 

for road safety.

Table 4-2 provides examples of 

performance measures developed 

by the National Highway Traffic 

Safety Administration (NHTSA) 

and the Governors Highway Safety 

Association (GHSA) that could be 

used to identify safety priorities.2

The sources of the data could be 

State crash data files, the Fatality 

Analysis Reporting System (FARS), 

surveys conducted by the State, 

or grant applications from law 

enforcement and other departments. 

The section on Network Screening 

in Chapter 11 presents a more 

detailed discussion of crash-based 

performance measures and how they 

can be used to identify sites that are 

high priority for safety treatment. 

TABLE 4-2: Safety performance measures and data sources (Source: NHTSA 2007)

DESCRIPTION SOURCES

Number of traffic fatalities (three-year or five-year moving averages) FARS 

Number of serious injuries in traffic crashes State crash 

data files

Fatalities/VMT (including rural, urban, and total fatalities) FARS, FHWA 

Number of unrestrained passenger vehicle occupant fatalities, seat positions FARS 

Number of fatalities in crashes involving a driver or motorcycle operator 

with a blood alcohol concentration of .08 g/dL or higher FARS 

Number of speeding-related fatalities FARS 

Number of motorcyclist fatalities FARS 

Number of unhelmeted motorcyclist fatalities FARS 

Number of drivers 20 or younger involved in fatal crashes FARS 

Number of pedestrian fatalities FARS 

Observed seat belt use for passenger vehicles, front seat outboard occupants Survey 

Number of seat belt citations issued during grant-funded 

enforcement activities

Grant activity 

reporting 

Number of impaired-driving arrests made during grant-funded 

enforcement activities

Grant activity 

reporting 

Number of speed citations issued during grant-funded activities Grant activity 

reporting 

National Highway 

Traffic Safety 

Administration 

(NHTSA). 2007. 

Performance 

Measures 

Discussion. 408 

Team Document 

#005, October 

29, 2007. 

National Highway 

Traffic Safety 

Administration.

2

Performance 

measure

A numerical 

metric used 

to monitor 

changes 

in system 

condition and 

performance 

against 

established 

visions, goals, 

and objectives.
4-4 UNIT 

4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

Components of 

safety management

The safety management process 

can be viewed in three general 

components. These components 

are carried out by the agency (or 

agencies) responsible for managing 

the safety of the road system: J Identifying safety problems

– The 

agency uses crash data and other 

safety data to identify road safety 

problems or problem locations.

J Developing potential safety 

strategies – The agency develops 

potential strategies to address 

the identified safety problems. 

These strategies might also be 

referred to as countermeasures 

or treatments.

J Selecting and implementing 

strategies – The agency weighs 

the potential strategies and 

decides which ones to implement.

Levels of safety management

Although all road safety 

management follows the same 

three general components listed 

above, the specific steps of the 

safety management process will be 

different depending on the scope. 

The process might be intended to 

address specific site-level issues, 

such as crash patterns at high 

priority intersections, curves, or 

corridors. On a larger scale, the 

process might be intended to 

address system-level issues, such as 

problems that can be addressed by 

policies, design standards, or broad 

ranging campaigns of education or 

enforcement. The following chapters 

will discuss safety management for 

these two levels: Chapter 11 presents 

site-level safety management; 

Chapter 12 presents system-level 

safety management.
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-5

Site-level safety management 

is the process of identifying and 

addressing safety issues at high 

priority sites. This contrasts with 

safety issues that are addressed for 

an entire transportation system (i.e., 

all roads in a city, county, or State). 

System-level safety management is 

covered in Chapter 12.

Agencies responsible for road 

safety often conduct some form 

of site-level safety management. 

They identify particular sites of 

concern and determine how best 

to address the safety problems at 

these priority sites. The methods 

of identifying priority sites and 

the safety strategies used to treat 

the sites differ according to the 

type of agency. A department of 

transportation (DOT) may install 

a sign or pavement marking; a law 

enforcement agency might increase 

enforcement in the area of the site. 

Regardless of the type of agency, it 

is important to conduct site-level 

safety management in a manner that 

uses good analysis methods driven 

by safety data.

Chapter 10 presented road safety 

management in terms of three 

general components: 

J Identifying safety problems

J Developing potential 

safety strategies

J Selecting and 

implementing strategies

When discussing site-level 

safety management, these three 

components can be further divided 

into six distinct steps. This sixstep process is common to the 

engineering discipline and is 

presented in Part B of the first 

edition of the Highway Safety 

Manual3

 (HSM). The process, shown 

in Figure 4-1, will be the framework 

for the discussion of site-level safety 

management in this chapter. The 

material presented in this chapter is 

based on the guidance presented in 

the HSM and material from a series 

of documents entitled “Reliability 

of Safety Management Methods” 

published by FHWA. These FHWA 

Site

A narrowly 

defined 

location of 

interest for 

safety analysis, 

such as an 

intersection, 

road section, 

interchange, 

or midblock 

crossing.

Highway Safety 

Manual, First 

edition, American 

Association of 

State Highway 

Transportation 

Officials, 2010.

3

Site-Level Safety Management

CHAPTER 11 UNIT 4: SOLVING SAFETY PROBLEMS

1. NETWORK SCREENING

2. DIAGNOSIS

3. COUNTERMEASURE 

 SELECTION

4. ECONOMIC APPRAISAL

5. PROJECT PRIORITIZATION

6. SAFETY EFFECTIVENESS 

 EVALUATION

FIGURE 4-1: Schematic Illustrating the 

Steps of Site-level Safety Management
4-6 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

documents provide in-depth 

guidance and examples on the 

following topics:

J Network screening – The network 

screening guide describes various 

methods and the latest tools to 

support network screening.4

J Diagnosis - The diagnosis 

information guide describes 

various methods and the latest 

tools to support diagnosis.5

J Countermeasure selection - 

The countermeasure selection 

information guide describes 

various methods and the 

latest tools to support 

countermeasure selection.6

J Safety effectiveness evaluation

- The safety effectiveness 

evaluation guide describes 

various methods and the 

latest tools to support safety 

effectiveness evaluation.7

J Systemic safety programs - The 

systemic safety programs guide 

describes the state-of-the-practice 

and the latest tools to support 

systemic safety analysis.8

The six steps of the site-level safety 

management process relate to the 

three general components of safety 

management as shown in Table 4-3. 

Each step is presented in more detail 

through the following sections 

in this chapter. 

SAFETY

MANAGEMEN

COMPONENTS STEPS OF SITE-LEVEL SAFETY MANAGEM

IDENTIFY 

SAFETY

PROBLEM

Step 1. Network screening: Identify locations that could benefit 

from treatments to reduce crash frequency and severity.

Step 2. Diagnosis: Identify crash trends and patterns based on 

reported crashes, assess the crash types and severity levels, 

and study other elements that characterize the crashes.

DEVELOP 

POTENTIAL

SAFETY

SOLUTIONS

Step 3. Countermeasure selection: Identify appropriate 

countermeasures to target crash contributing factors and 

reduce crash frequency and severity at identified locations. 

Step 4. Economic appraisal: Estimate the economic benefit and 

cost associated with implementing a particular countermeasure 

or set of countermeasures. 

SELECT AND

IMPLEMENT 

STRATEGIE

Step 5. Project prioritization: Develop a prioritized list of 

safety improvement projects, considering available resources.

Step 6. Safety effectiveness evaluation: Evaluate how a particular 

countermeasure (or group of countermeasures) has affected 

crash frequency and severity where it was installed. 

TABLE 4-3: Steps of the Site-level Safety Management Process

Srinivasan, R., F. Gross, 

G. Bahar (2016), 

Reliability of Safety 

Management Methods: 

Safety Effectiveness 

Evaluation, Report No. 

FHWA-SA-16-040, 

Federal Highway 

Administration, 

Washington, D.C.

Srinivasan, R., G. 

Bahar, F. Gross (2016), 

Reliability of Safety 

Management Methods: 

Diagnosis, Report No. 

FHWA-SA-16-038, 

Federal Highway 

Administration, 

Washington, D.C.

See next page.

Bahar, G. R. Srinivasan, 

F. Gross, (2016), 

Reliability of Safety 

Management Methods: 

Countermeasure 

Selection, Report No. 

FHWA-SA-16-039, 

Federal Highway 

Administration, 

Washington, D.C.

Srinivasan, R., F. Gross, 

B. Lan, G. Bahar 

(2016), Reliability of 

Safety Management 

Methods: Network 

Screening, Report No. 

FHWA-SA-16-037, 

Federal Highway 

Administration, 

Washington, D.C. 

7

5

8

6

4
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-7

Step 1. Network screening

Network screening refers to the 

process of selecting high priority 

sites that need safety treatment, 

often through an analysis of crash 

data. There are many ways in 

which an agency can use crash data 

to prioritize sites, ranging from 

simplistic methods, which are easy 

to understand and implement but 

can be inaccurate or ineffective, 

to more advanced methods, which 

require statistical expertise and 

more data but provide a better 

prioritization of sites.

For many years, the most prevalent 

methods for ranking specific sites 

for safety improvements were based 

on historical crash data alone. Many 

agencies still use these methods 

to allocate their road safety funds. 

Agencies that prioritize sites by 

historical crash frequency identify 

those sites that have the highest 

number of crashes in a certain 

time period (typically three to five 

years). This serves to assist agencies 

in addressing the magnitude of 

the problem, that is, attempting 

to address the highest number of 

crashes. By its nature, this method 

typically identifies sites that have 

high amounts of traffic (either 

vehicles, pedestrians, or other road 

users). However, this method may 

miss abnormally hazardous sites 

that do not present a relatively 

large number of crashes. Another 

variation of the crash frequency 

method uses crash severity, in 

which agencies weight the crash 

frequency by giving greater weight 

to higher severity crashes. This 

method counteracts some of the 

bias in the crash frequency method. 

For example, a general high crash 

frequency may prioritize a busy 

intersection that has many crashes, 

but a closer examination reveals 

that most crashes are low speed, low 

severity rear-end crashes. The crash 

severity method would lower the 

priority of this intersection in favor 

of other sites where more serious 

crashes occur. 

Some agencies prioritize sites 

by the historical crash rate. This 

method incorporates traffic volume 

to augment the crash data. The 

crash frequency at a site is divided 

by the traffic volume – either the 

annual average daily traffic (for road 

segments), total entering volume 

(for vehicle traffic at intersections), 

or other volumes, such as pedestrian 

crossing volume. The typical unit 

for this method is crashes per 

100 million vehicle miles traveled 

for road segments or crashes per 

100 million entering vehicles for 

intersections. Crash rate in these 

units is calculated as:

(from previous page)

Gross, F., T. Harmon, 

G. Bahar, K. Peach 

(2016), Reliability of 

Safety Management 

Methods: Systemic 

Safety Programs, 

Report No. 

FHWA-SA-16-041, 

Federal Highway 

Administration, 

Washington, D.C.

8

Crash 

frequency

The number 

of observed 

crashes 

per year.

Crash 

severity

The level of 

injury severity 

of the crash 

as an event, 

typically 

determined 

by the highest 

severity injury 

of any person 

involved in 

the crash.

Crash rate

The number 

of observed 

crashes 

per unit of 

traffic volume 

passing through 

the location.

Crash rate per 

100 million vehicle 

miles traveled

= (C×100,000,000)

(V×365×N×L)

C = Number of crashes in the study period

V = Traffic volumes using average annual 

daily traffic (AADT) volumes

N = Number of years of data

L = Length of the roadway segment in miles

This approach of prioritizing sites by 

crash rate serves to counteract the 

bias of crash frequency that overly 

prioritizes sites with high volume, 

since higher volume decreases 

the crash rate. However, it may 

inefficiently prioritize sites with 

very low volumes.
4-8 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

Agencies might use a combination 

of these two methods. They may set 

a minimum crash rate to generate 

an initial list of priority sites and 

then prioritize that group by crash 

frequency or severity. Regardless, 

these simplistic methods are known 

to have potential biases. One of 

the most prevalent biases is that 

the crash history used to prioritize 

sites with these methods usually 

reflects only the short-term trend 

of crashes. Given that the yearto-year occurrence of crashes at 

a location is random, it can be 

the case that a short-term crash 

history (one to three years) may 

be relatively high, but in the long 

run (ten years), the crashes would 

return to a lower amount, even if 

no safety improvements were done. 

This effect creates selection bias or 

regression-to-the-mean (RTM) bias in 

the safety analysis of this location.

As the years progressed, many 

transportation safety professionals 

recognized that while these 

simplistic methods did identify 

sites that benefited from safety 

improvement, they were not the 

locations where safety funds could 

be spent the most effectively. The 

selection of high crash sites was 

subject to RTM bias. Also, sites 

with high numbers of crashes were 

typically complex and required 

expensive reconstruction in order 

to reduce crashes appreciably. The 

question became, “How could road 

safety funds be spent in a way that 

provided the biggest bang for the 

buck?”

As the science of road safety 

advanced, researchers developed 

more advanced approaches 

for prioritizing sites for safety 

improvements. Dr. Ezra Hauer 

pushed forward a movement to 

identify “sites with promise.”9

 The 

main idea was to identify sites that 

experienced more crashes than 

would be expected from a site with 

that particular set of characteristics. 

In many cases, these abnormally 

performing sites could be addressed 

with low cost safety treatments, 

such as larger signs or pavement 

markings with greater visibility. This 

approach uses statistical regression 

models that predict crashes for a 

given set of characteristics. These 

models demonstrate the advantage 

of bringing together different types 

of safety data, which in this case 

could include crash data, roadway 

characteristics, and traffic volume.

Regressionto-the-mean

The fact that 

a short term 

examination of 

crash history 

at a location is 

likely inaccurate 

(e.g., lower or 

higher than 

its true safety 

performance). 

When a longer 

time period of 

crash history is 

examined, the 

crash frequency 

will “regress” 

to its “mean” 

and provide a 

better picture 

of the long term 

average crash 

frequency.

Comparing road segments by 

crash frequency and rate

Road Segment A: A three-mile section of 

road that has had four crashes over five 

years and has a traffic volume of 4,000

vehicles per day.

Road Segment B: A three-mile section 

of road that has had 10 crashes over five 

years and has a traffic volume of 12,000

vehicles per day.

If an agency is comparing these 

segments based on crash frequency, 

they would prioritize road segment B 

for having 10 crashes compared to road 

segment A which had four crashes.

If comparing these segments based on 

crash rate, the agency would calculate 

the crash rate of road segment A as 

(4 crashes x 100,000,000) / (4,000 

vehicles per day x 365 x 5 years x 3 miles) 

= 18.2 crashes per 100 million vehicle 

miles traveled. Following the same 

calculation, road segment B has a rate of 

15.2 crashes per 100 million vehicle miles 

traveled. According to crash rate, the 

agency would prioritize road segment A. 

The prioritization of these two segments 

changes when traffic volume is taken 

into account.

Hauer, E. (1997), 

Observational Before 

After Studies in Road 

Safety, Elsevier 

Science, New York.

9
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-9

The most basic of these regression 

methods calculates predicted 

crashes. This method requires 

information about certain geometric 

and operational characteristics, such 

as traffic volume, number of lanes, 

and type of road.

An SPF is developed or calibrated 

using data from an entire 

jurisdiction or State, so it is 

independent of the crash history 

of the specific site. This means 

that the predicted crash value is 

unaffected by the bias caused by 

RTM. Using SPFs, transportation 

agencies can predict crash values for 

many sites and prioritize the sites 

according to the highest predicted 

values. Another use of the predictive 

method is in systemic safety 

treatments, presented in Chapter 12 

under Risk Based Prioritization.

Predicted 

crashes

The frequency 

of crashes per 

year that would 

be predicted 

for a site based 

on the result 

of a crash 

prediction 

model, called 

a safety 

performance 

function (SPF).

TABLE 4-4: Performance Measures for Network Screening (Source: Highway Safety Manual, 1st ed.)

PERFORMANCE MEASU

ACCOUNTS

FOR TRAFFIC

VOLUME

ACCOUNTS

FOR 

RTM BI

ACCOUNTS

FOR CRASH

SEVERITY

1. Average crash frequency No No Not explicitly

2. Crash rate Yes No Not explicitly*

3. Equivalent property damage only 

(EPDO) average crash frequency No No Yes

4. Relative severity index No No Yes

5. Critical rate Yes No Not explicitly*

6. Excess predicted average crash 

frequency using method of moments No No Not explicitly*

7. Level of service of safety Yes No Not explicitly*

8. Excess predicted average crash 

frequency using SPFs Yes No Not explicitly*

9. Probability of specific crash types 

exceeding threshold proportion No Not affected 

by RTM bias** Not explicitly*

10. Excess proportion of 

specific crash types No Not affected 

by RTM bias** Not explicitly*

11. Expected average crash frequency 

with empirical Bayes adjustments Yes Yes Not explicitly

12. EPDO average crash frequency 

with EB adjustment Yes Yes Ye

13. Excess expected average crash 

frequency with EB adjustment Yes Yes Not explicitly

* While these measures do not explicitly 

mention severity, analysts can adapt any of the 

measures to consider any severity level.

** These two measures will not be affected by 

RTM only if they are based on data from a 

long time period.
4-10 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

The first edition of the HSM lists 

several benefits of the predictive 

method, including:

J RTM bias is addressed as the 

method concentrates on longterm expected average crash 

frequency rather than shortterm observed crash frequency.

J Reliance on availability of 

limited crash data for any one 

site is reduced by incorporating 

predictive relationships based on 

data from many similar sites.

J The method accounts for the 

fundamentally nonlinear 

relationship between crash 

frequency and traffic volume.

Agencies can also use the predicted 

crashes in combination with actual 

crash history at the site of interest to 

calculate expected crashes. A method 

called empirical Bayes (EB) brings 

these two values together to reflect 

a crash frequency that incorporates 

the general crash prediction from the 

SPF with the real world experience 

of crash history at the site to provide 

an accurate estimation of how many 

crashes should be expected at the 

site (see more detailed discussion 

of the EB method later in this step). 

Some agencies may also calculate 

excess crashes as a measure for site 

prioritization. This is the difference 

between the expected crashes and the 

observed crash frequency at the site.

Performance measures 

in network screening

The key to effective network screening 

is selecting an appropriate performance 

measure. Network screening 

methods should appropriately 

account for three major factors that 

can affect the screening outcome:

J Differences in traffic volumes

J Possible bias due to RTM

J Crash severity

Table 4-4 lists the thirteen 

performance measures discussed in 

the HSM with an indication of their 

ability to account for these major 

factors. While some measures directly 

account for crash severity (e.g., 

relative severity index), analysts 

can adapt any of the measures to 

account for crash severity.

SPFs can be obtained in two ways:

1) SPFs can be developed from scratch 

using crash, roadway, and traffic volume 

data from roads and intersections in the 

State. This requires significant data to be 

collected on hundreds of sites. A statistical 

expert must use these data to develop SPFs 

that are tailor made for that State.

2) SPFs can be obtained from national 

resources, such as the HSM; then 

calibrated for the particular State of 

interest. This requires data to be collected 

on a smaller number of sites than is 

required for developing a new SPF. 

The crashes predicted by the SPF are 

compared to the crashes observed on the 

State’s roads, and an analyst calculates 

a calibration factor to adjust the SPF 

prediction appropriately for the State.

SPF development or calibration is typically 

handled by the State DOT. FHWA provides 

guidance on deciding between developing 

a new SPF or calibrating an existing one.10 

States that decide to develop new SPFs 

can refer to guidance in a related FHWA 

publication.11 NCRHP provides guidance 

for those who decide to calibrate 

existing SPFs.12

Where can I get safety performance functions for my State?

Bahar, G (2014), 

User’s Guide to 

Develop Highway 

Safety Manual 

Safety Performance 

Function Calibration 

Factors, HR 20-

7(332), National 

Cooperative 

Highway Research 

Program, American 

Association of 

State Highway and 

Transportation 

Officials, Standing 

Committee on 

Traffic Safety, 

Washington, DC.

R. Srinivasan and 

K. Bauer (2013), 

Safety Performance 

Development 

Guide: Developing 

Jurisdiction-Specific 

SPFs, Report 

FHWA-SA-14-005, 

Federal Highway 

Administration, 

Washington, DC

Srinivasan, R., 

D. Carter, and 

K.Bauer (2013), 

Safety Performance 

Function Decision 

Guide: SPF 

Calibration vs SPF 

Development, 

Report No. 

FHWA-SA-14-004, 

Federal Highway 

Administration, 

Washington, DC.

12

11

10

Expected 

crashes, 

excess 

crashes

See next page.
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-11

Accounting for differences 

in traffic volumes

As discussed earlier, analysts have 

traditionally used crash rates to 

account for differences in traffic 

volume among sites. Crash rate 

is the ratio of crash frequency to 

exposure, which is typically the 

traffic volume. Crash rates implicitly 

assume a linear relationship 

between crash frequency and traffic 

volume; however, many studies 

have shown that the relationship 

between crashes and traffic volume 

is nonlinear, and the shape of this 

relationship depends on the type 

of facility. Nonlinear relationships, 

such as SPFs, are more appropriate 

than linear relationships, such as 

crash rates to account for differences 

in traffic volume among sites.

SPFs are a more reliable method to 

account for differences in traffic 

volume among sites because they 

reflect the nonlinear relationship 

between crash frequency and traffic 

volume. The SPF is an equation that 

represents a best-fit model that 

relates annual observed crashes to 

the site characteristics including 

annual traffic volume and other 

site characteristics. Typically, SPFs 

are estimated for a particular crash 

type for a type of facility (e.g., 

run-off-road crashes on rural two 

Predicted Crash Frequency

10

5

0 Traffic Volume (AADT) 100,000

Predicted Crash Frequency

40

30

20

10

0 Traffic Volume (AADT) 100,000

Excess 

crashes

The difference 

between the 

expected 

crashes and the 

observed crash 

frequency at 

the site.

Expected 

crashes

The frequency 

of crashes 

per year that 

represents the 

combination of 

the predicted 

crashes and 

the observed 

crashes that 

actually 

occurred at 

the site.

FIGURE 4-3: Example of SPF for single-vehicle crashes on rural, 4-lane freeways

FIGURE 4-2: Example of SPF for multi-vehicle crashes on rural, 4-lane freeways
4-12 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

lane roads) using data from an 

entire jurisdiction or State. Figure 

4-2 and Figure 4-3 show example 

SPFs where the points represent 

observed crashes at specific traffic 

volumes for individual sites, and 

the solid line represents the bestfit model (i.e., the SPF). If the 

relationship between exposure and 

crash frequency were linear, then 

the solid line would be a straight 

line instead of a curve. These two 

figures also demonstrate the nature 

of SPFs – each curve is different. For 

the rural, four lane freeways used in 

this example, multi-vehicle crashes 

rise exponentially with more traffic 

volume (Figure 4-2) but singlevehicle crashes behave differently; 

they level off with increasing levels 

of traffic volume (Figure 4-3). 

An SPF produces the average number 

of crashes that would be predicted 

for sites with a particular set of 

characteristics. By comparing a 

site’s observed number of crashes 

with the predicted number of 

crashes from an SPF, it may be 

possible to identify sites that 

experience more crashes than one 

would expect from a site with that 

particular set of characteristics. 

Sites where the observed number 

of crashes is larger than the 

predicted number of crashes from 

an SPF warrant further review and 

diagnosis. Two measures in Table 

4-4, level of service of safety (LOSS) 

and the excess predicted average 

crash frequency using SPFs, use 

the observed crash frequency and 

predicted frequency from an SPF to 

identify sites with promise. 

Ideally, SPFs should be estimated 

using data from the same 

jurisdiction as the site(s) being 

studied.13 However, that may 

not always be possible due to 

the availability of data or lack of 

statistical expertise. In that case, 

the SPFs developed from another 

jurisdiction could be calibrated using 

data from the jurisdiction with the 

study sites.15 

Avoiding bias due to 

regression-to-the-mean

As previously discussed, RTM 

describes the situation when 

periods with relatively high crash 

frequencies are followed by periods 

with relatively low crash frequencies 

simply due to the random nature of 

crashes. Figure 4-4 illustrates RTM, 

comparing the difference between 

short-term average and long-term 

average crash history.16 Due to RTM, 

the short-term average is not a 

reliable estimate of the long-term 

crash propensity of a particular 

site. If an agency selects sites based 

SPF Example 1

Some States use Safety Analyst, a 

software tool from AASHTO, to identify 

sites that may benefit from a safety 

treatment.14 The following is an SPF 

from Safety Analyst that predicts 

the total number of crashes on rural 

multilane divided roads:

P = L × e-5.05 × (AADT)0.66

P is the total number of crashes in one 

year on a segment of length L. 

This is a relatively simple SPF where the 

predicted number of crashes per mile 

is a function of just AADT. For example, 

if the AADT is 45,000, then the predicted 

number of crashes for a one mile segment 

based on the SPF will be the following:

P = 1 × e-5.05 × 450000.66

=7.55 crashes per year

R. Srinivasan and 

K. Bauer (2013), 

Safety Performance 

Development 

Guide: Developing 

Jurisdiction-Specific 

SPFs, Report 

FHWA-SA-14-005, 

Federal Highway 

Administration, 

Washington, DC.

G. Bahar and E. 

Hauer (2014), Users 

Guide to Develop 

HSM SPF Calibration 

Factors, NCHRP 

Project 20-7(332).

Susan Herbel, 

Lorrie Laing, 

Colleen McGovern 

(2010), Highway 

Safety Improvement 

Program (HSIP) 

Manual, FHWASA-09-029, 

Federal Highway 

Administration, 

Washington, DC.

http://www.

safetyanalyst.org/

13

15

16

14
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-13

on high short-term average crash 

history, crashes at those sites may 

be lower in the following years due 

to RTM, even if the agency does not 

install countermeasures at those 

sites. 

If RTM is not properly accounted for, 

sites with a randomly high count 

of crashes in the short term could 

be incorrectly identified as having 

a high potential for improvement, 

and vice versa. In this case, scarce 

resources may be inefficiently used 

on such sites while sites with a truly 

high potential for cost effective 

safety improvement remain 

unidentified.

One approach to address RTM bias 

is to use the EB method. The EB 

method is a statistical method 

that combines the observed crash 

frequency (obtained from crash 

reports) with the predicted crash 

frequency (derived from the 

appropriate SPF) to calculate the 

expected crash frequency for a site of 

interest. This method pulls the crash 

count towards the mean, accounting 

for the RTM bias.

The EB method is illustrated in 

Figure 4-5, which illustrates how 

the observed crash frequency is 

combined with the predicted crash 

frequency based on the SPF.18 The 

Bauer and Harwood17 provide a more 

complex SPF for fatal and injury crashes on 

rural two lane roads. This model provides 

a crash prediction that is more tailored to 

characteristics of the site, such as curve 

radius and vertical grade of the road:

NFI = fatal-and-injury crashes 

per mile per year

AADT = annual average daily traffic 

(vehicles/day)

G = absolute value of percent grade; 0% 

for level tangents; ≥ 1% otherwise

R = curve radius (ft); missing for tangents

I

HC = horizontal curve indicator: 

1 for horizontal curves; 0 otherwise

LC = horizontal curve length (mi); 

not applicable for tangents

ln = natural logarithm function

Bauer, K. and 

Harwood, D., Safety 

Effects of Horizontal 

Curve and Grade 

Combinations on 

Two-Lane Highways, 

Federal Highway 

Administration, 

Report No. FHWAHRT-13-077, January 

2014.

Susan Herbel, 

Lorrie Laing, 

Colleen McGovern 

(2010), Highway 

Safety Improvement 

Program (HSIP) 

Manual, FHWASA-09-029, 

Federal Highway 

Administration, 

Washington, DC.

17

18

SPF Example 2 Observed Crash Frequency

Years

Short Term 

average

Short Term 

average

Long Term

average

FIGURE 4-4: Chart to illustrate RTM phenomenon (Source: HSIP Manual, 2010)

NFI = exp [-8.76+1.00×ln(AADT)+0.044×G+0.19×ln(2×5730/R)×IHC

+4.52×(1/R)(1/Lc)×IHC ]
4-14 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

EB method is applied to calculate 

an expected crash frequency 

or corrected value, which lies 

somewhere between the observed 

value and the predicted value from 

the SPF.

Mathematically, the expected 

number of crashes can be written 

as a function of the predicted value 

from the SPF and the observed 

crashes in the following manner:

average crash frequency are provided 

in Part B of the HSM. For example, 

if the observed crash frequency in a 

particular site was nine crashes per 

year, the predicted crash frequency 

from the SPF was 6.4 crashes per 

year, and the w was 0.3, then Nexpected

will be as follows:

Crash Frequency

aaDT

Predicted number from SPF

Expected number using Eb

Observed number

SPF

Nexpected = expected average crash frequency 

for a certain study period

w = weighted adjustment to be placed 

on the SPF prediction (0 < w < 1)

Npredicted = predicted average crash frequency 

predicted using an SPF for the study 

period under the given conditions

Nobserved = observed crash frequency at the 

site over the study period

The weight w is a function of the 

predicted crash frequency (Npredicted) 

and a statistical parameter called the 

overdispersion parameter of the SPF. 

Procedures to estimate the expected 

=

=

w× Npredicted+(1 -w) × Nobserved

0.3 × 6.4+(1 - 0.3) × 9

Nexpected

Nexpected

Equation 1 We can prioritize sites by calculating 

the difference between the EB 

expected crashes at a particular 

site and the predicted crashes from 

an SPF. By comparing EB expected 

crashes at a particular site instead 

of observed crashes, we account for 

possible bias due to RTM.

The first eight measures presented 

in Table 4-4 do not account for 

possible bias due to RTM. Measure 9 

(probability of specific crash types 

exceeding threshold proportion) and 

measure 10 (excess proportion of 

specific crash types) are not affected 

by RTM unless they are based on 

short-term crash history. Measure 

11 (expected average crash frequency 

with EB adjustments), measure 

12 (EPDO average crash frequency 

= 8.22 crashes per year

FIGURE 4-5: Schematic to illustrate the empirical Bayes method (Source: HSIP Manual, 2010)
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-15

with EB adjustment), and measure 

13 (excess expected average crash 

frequency with EB adjustment) 

account for possible bias due to RTM 

using the EB adjustments.

Accounting for crash severity

The severity of crashes at a location 

can (and should) have a bearing on 

the priority of the site for safety 

treatment. Three of the measures in 

Table 4-4, measure 3 (EPDO average 

crash frequency), measure 4 (relative 

severity index), and measure 12 

(EPDO average crash frequency with 

EB adjustment), directly account for 

crash severity. Measures 3 and 12 use 

the EPDO method, which converts 

all crashes to a common unit, 

namely property damage only (PDO) 

crashes. Using these measures, the 

analyst assigns points to each crash 

based on its crash severity level. 

A PDO crash typically receives one 

point and the points increase as the 

severity of the crash increases. 

While other measures do not 

explicitly mention severity, analysts 

can adapt any of the measures to 

consider any severity level. For 

example, an analyst could use 

crash frequency and focus on the 

frequency of fatal and severe injury 

crashes to priority rank sites. It is 

important to note that the severity 

distribution of crashes may be a 

function of site characteristics 

including AADT. For example, 

sections with higher AADT values 

may be associated with lower speeds 

and consequently fewer severe 

crashes.

Step 2. Diagnosis

Diagnosis is the second step in 

the roadway safety management 

process, following network 

screening. Diagnosis is the process 

of further investigating the sites 

and issues identified from network 

screening. The intent of diagnosis 

is to identify crash patterns and the 

factors that contribute to crashes 

at the identified sites. Thorough 

diagnosis can also identify potential 

safety issues that have not yet 

manifested in crashes. Diagnosis 

often involves a review of the 

crash history, traffic operations, 

and general site conditions. While 

safety professionals could review 

these data from the office, a field 

visit provides the opportunity to 

observe road user behavior and 

site characteristics that are not 

available in the data. Sometimes, 

safety professionals may also 

conduct a field review at night or 

at other times that crash history 

has indicated to be of concern. It 

is important to diagnose the cause 

of the problem before developing 

potential countermeasures, just 

as a doctor examines symptoms 

to diagnose an underlying disease 

before formulating a prescription. 

Otherwise, resources may be 

misallocated if a countermeasure 

that does not target the underlying 

issues is selected and implemented. 

The Haddon Matrix is a framework to 

identify possible contributing factors 

(e.g., driver, vehicle, and roadway/

environment) which are crossreferenced against possible crash 

conditions before, during, and after 

a crash to identify possible reasons 

for events. This comprehensive 

understanding of crash contributing 

factors is important for the diagnosis 

of safety problems. An example of 

the Haddon Matrix is presented later 

under Countermeasure Selection on 

page 4-20. 
4-16 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

The HSM recommends that 

diagnosis include the following 

parts:

J A review of safety data

J An assessment of supporting 

documentation

J An assessment of field conditions

Safety data review

An analyst can conduct a detailed 

review of the crash data from police 

reports to identify patterns. This 

could involve reviewing the crash 

type, severity, sequence of events, 

and contributing circumstances. 

Different visualization tools, such 

pie charts, bar charts, or tabular 

summaries, can be used to display 

various crash statistics. In addition 

to reviewing descriptive statistics, 

analysts can use various methods 

to identify underlying safety issues 

based on the recognition of crash 

patterns. 

One method would be to identify 

locations that have a proportion of 

a specific collision type relative to 

the total collisions that is higher 

than some average or threshold 

proportion value for similar road 

types. Kononov found that looking 

at the percentage distribution of 

collisions by collision type can reveal 

the “existence of collision patterns 

susceptible to correction” that may 

or may not be accompanied by the 

overrepresentation in expected 

or expected excess collisions.19 

Heydecker and Wu originally 

proposed this method.20 The method 

is identical for different location 

types. However, only similar 

location types should be analyzed 

together because collision patterns 

will naturally differ. For example, 

the collision patterns are different 

for stop-controlled intersections, 

signalized intersections, and twolane roads, so the method would be 

applied separately to the three types 

of facilities and separately for urban 

and rural environments. Another 

method would be to investigate 

sites that experience a gradual or 

sudden increase in mean collision 

frequency.21

Following the detailed review of 

the crash data, the analyst can 

create collision diagrams, condition 

diagrams, and crash maps to 

summarize the crash information 

by location. A collision diagram 

is a tool to identify and display 

crash patterns. Many resources, 

including the HSM, provide guidance 

on developing collision diagrams. 

Examples of collision diagrams are 

shown in Figure 4-6 and Figure 4-7. 

Each crash at the site is displayed 

according to where it occurred, 

what type of crash it was, how 

severe it was, and various other 

characteristics. An analyst uses 

symbols to visually represent many 

of these characteristics.

Condition diagrams include a 

drawing with information about 

the site characteristics including 

information about the roadway 

(e.g., number of lanes, presence 

of medians, pedestrian and bicycle 

facilities, shoulder information), 

surrounding land uses, and 

pavement conditions. Condition 

diagrams can be overlaid on top of 

collisions diagrams to gain further 

insight to the crash patterns. 

Crash mapping involves the use of 

geographic information systems 

(GIS) to integrate information 

from the roadway network with 

information from geocoded crash 

data. If the geocoded crash data are 

Heydecker, B. J., and 

J. Wu (1991), Using 

the Information 

in Road Accident 

Records Proc., 19th 

PTRC Summer 

Annual Meeting, 

London.

Hauer, E. (1996), 

Detection of Safety 

Deterioration 

in a Series of 

Accident Counts. 

Transportation 

Research Record 

1542, 38-43. 

Hauer, E. (1996), 

Statistical Test 

of the Difference 

between Expected 

Accident 

Frequencies, 

Transportation 

Research Record 

1542, 24-29.

Kononov, J. 

(2002), Identifying 

Locations 

with Potential 

for Collision 

Reductions: 

Use of Direct 

Diagnostics and 

Pattern Recognition 

Methodologies, 

Transportation 

Research Record 

1784, pp. 153-158.

20

21

19
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-17

accurate, then crash mapping can 

provide valuable insights into crash 

locations and crash patterns. 

Assess supporting 

documentation

This step involves a review of 

documented information about 

the site along with interviews of 

local transportation professionals 

to obtain additional perspectives 

on the safety data review from 

the previous step. Examples of 

supporting documentation include 

traffic volumes, construction plans 

and design criteria, photos and 

maintenance logs, weather patterns, 

and recent traffic studies in the area.

Assess field conditions

Field observations are useful for 

supplementing crash data and can 

help the analyst understand the 

behavior of drivers, pedestrians, 

and bicyclists. The first stage of 

the field investigation should 

be an on-site examination of a 

road user’s experience. Those 

conducting the assessment should 

travel through the site at different 

times of the day using different 

modes of transportation (e.g., 

driving, walking, and bicycling). 

Assessors should observe the mix 

of vehicle traffic and other road 

users. They should also observe 

traffic movements, conflicts, and 

FIGURE 4-7: 

Example 

Collision 

Diagram at 

a Ramp

FIGURE 4-6: 

Example 

Collision 

Diagram at an 

Intersection
4-18 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

operating speeds. Those conducting 

the field review could determine 

whether the road and intersection 

characteristics are consistent 

with driver expectation and if 

roadside recovery zones are clear 

and traversable.

Road safety audits

One method to assess field 

conditions is a road safety audit 

(RSA). This is the formal safety 

performance examination of 

an existing or future road or 

intersection by an independent, 

multidisciplinary team. An RSA 

qualitatively estimates and reports 

on existing and potential road safety 

issues and identifies opportunities 

for safety improvements for all 

road users. FHWA encourages 

States, local jurisdictions and tribal 

governments to integrate RSAs into 

the project development process for 

new roads and intersections and to 

conduct RSAs on existing ones.

The purpose of an RSA is to answer 

the following questions: 

J What elements of the road may 

present a safety concern, and to 

what extent, to which road users, 

and under what circumstances? 

J What opportunities exist to 

eliminate or mitigate identified 

safety concerns?

The multidisciplinary audit team 

consists of people who represent 

different areas of expertise, such as 

engineering (e.g., design, traffic, 

and maintenance), law enforcement, 

safety educators, public officials, 

community traffic safety advocates, 

and others. Any phase of project 

development (planning, preliminary 

engineering, design, construction) 

and any sized project from minor 

intersection and roadway retrofits 

to mega-projects are eligible for 

an RSA.

Most State DOTs have established 

safety review processes. However, 

RSAs and a traditional safety reviews 

are different. Table 4-5 shows the 

difference between an RSA and a 

traditional safety review.22

“Road Safety Audits 

(RSA),” accessed 

August 7, 2013, 

http://safety.fhwa.

dot.gov/rsa/

22

TABLE 4-5: Differences between Road Safety Audit and Traditional Road Safety Review 

(Source: FHWA)

ROAD SAFETY AUDIT TRADITIONAL SAFETY 

Independent, multi-disciplinary team Safety review team within the project team 

with only safety and/or design experience

Considers all potential road users (pedestrians, 

bicyclists, motor vehicles, transit users) Often concentrates only on motor vehicles

Accounts for road user capabilities 

and limitations

Safety reviews do not normally 

consider human factor issues

Always generates a formal report Often does not generate a formal report

Always generates a formal response report Often does not generate a formal 

response report
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-19

Step 3. Countermeasure 

selection

After diagnosing the safety 

issues at the site, analysts select 

countermeasures to address 

the contributing factors for 

observed crashes. The first part 

of countermeasure selection is 

to identify countermeasures to 

target the underlying safety issues. 

Analysts can use tools like the 

Haddon Matrix and resources like 

the NCHRP Report 500 series to 

identify targeted countermeasures 

to address or mitigate underlying 

contributing factors. 

Identifying contributing factors

The Haddon Matrix is a tool 

originally developed for injury 

prevention, but it is directly 

applicable to highway safety in 

both diagnosis and countermeasure 

selection.24 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. Chapter 6 of the 1st edition of 

the HSM provides further discussion 

of the Haddon Matrix.

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. Precrash factors speak to the factors 

or actions prior to the crash that 

contributed to the occurrence of the 

crash. Crash factors speak to those 

factors or actions that occurred at 

the moment of the crash. Post-crash 

factors speak to factors that come 

into play after the crash that affect 

the severity of the injuries or speed 

of response. Examples of human 

factors include fatigue, inattention, 

age, and failure to wear a seat belt. 

Vehicle factors include bald tires, 

airbag operations, and worn brakes. 

Examples of roadway factors include 

pavement friction, weather, grade, 

and limited sight distance.

Table 4-6 is an example application 

of the Haddon Matrix from the 

Highway Safety Improvement 

Program (HSIP) Manual for crashes 

in an urban area.25 The top-left 

cell identifies driver behaviors or 

characteristics that may contribute 

to the likelihood or the severity of 

a collision, such as poor vision or 

reaction time, alcohol consumption, 

speeding, and risk taking. These 

http://www.irap.net/

http://toolkit.irap.

org/

Susan Herbel, 

Lorrie Laing, 

Colleen McGovern 

(2010), Highway 

Safety Improvement 

Program (HSIP) 

Manual, FHWASA-09-029, 

Federal Highway 

Administration, 

Washington, DC.

Haddon, W., Jr. 

(1972). A logical 

framework for 

categorizing 

highway safety 

phenomena and 

activity. Journal of 

Trauma 12: 193–207.

23

25

24

International Road 

Assessment Programme

The International Road Assessment 

Programme (iRAP) conducts safety 

inspections on high-risk roads in more 

than 70 countries. The iRAP inspectors 

perform a detailed road survey, focusing 

on road attributes that are known to be 

associated with crash risk. These include 

intersection design, number of lanes, 

roadside hazards, and provisions for 

pedestrian crossings. The inspectors 

use these data to develop a star rating, 

which reflects the level of safety of the 

road, and provide detailed feedback to 

the government agency in the form of an 

assessment report. iRAP also provides 

a Road Safety Toolkit, which helps 

engineers, planners, and policy makers 

develop safety plans for all road users.23
4-20 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

factors should be considered when 

selecting countermeasures. For 

example, based on these human 

factors, successful countermeasures 

may be those that improve visibility 

or reduce speeding. The matrix in its 

entirety provides a range of potential 

issues that can be addressed through 

a variety of countermeasures 

including education, enforcement, 

engineering, and emergency 

response solutions.

Countermeasure 

resources and tools

Diagnosing a roadway safety 

problem and identifying effective 

countermeasures is a skill 

developed through education, 

training, research, and experience. 

Many resources are available to 

help transportation professionals 

analyze and develop 

countermeasures. Since the 

transportation field continuously 

generates new knowledge and 

countermeasure approaches, it is 

important to stay informed of the 

available resources and tools.26

Some of the most useful resources 

and tools for countermeasure 

guidance and selection are listed 

below (alphabetically):

J Bicycle Safety Guide and 

Countermeasure Selection System 

(BIKESAFE, www.pedbikesafe.

org/bikesafe) – This resource 

provides practitioners with the 

latest information available 

for improving the safety and 

mobility of those who bike. 

The online tools provide the 

user with a list of possible 

engineering, education, or 

enforcement treatments to 

improve bicycle safety and/or 

mobility based on user input 

about a specific location.

J Countermeasures That Work: A 

Highway Safety Countermeasure 

Guide for State Highway Safety 

Offices – This document serves 

as a basic reference to help state 

Highway Safety 

Improvement 

Program Manual. 

Federal Highway 

Administration, 

Washington, D.C., 

2010, Chapter 3

26

TABLE 4-6: Haddon Matrix for crashes in an urban area (Source: HSIP Manual)

PERIOD HUMA

VEHICLE/

EQUIPMENT

PHYSICAL

ENVIRONMENT

SOCIOECONOMIC

PRECRASH Poor vision or

reaction time, 

alcohol, speeding, 

risk taking

Failed brakes, 

missing lights, 

lack of warning 

systems

Narrow 

shoulders, 

ill-timed signals

Cultural norms 

permitting 

speeding, red 

light running, DUI

CRASH Failure to

use occupant 

restraints

Malfunctioning 

safety belts, 

poorly engineered 

air bags

Poorly designed 

guardrails

Lack of 

vehicle design 

regulations

POSTCRASH High

susceptibility, 

alcohol

Poorly designed 

fuel tanks

Poor emergency 

communication 

systems

Lack of support 

for EMS and 

trauma systems
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-21

highway safety offices (SHSOs) 

select effective, evidence-based 

countermeasures for traffic 

safety problem areas related 

to user behaviors, such as 

alcohol-impaired and drugged 

driving, seat belts and child 

restraints, and aggressive 

driving and speeding.27

J Crash Modification Factors 

Clearinghouse (www.

cmfclearinghouse.org) – This 

website offers transportation 

professionals a central, online 

repository of crash modification 

factors (CMFs) that indicate the 

safety effect on crashes due to 

infrastructure improvements. 

The website also provides 

additional information and 

resources related to CMFs. 

This site is funded by FHWA.

J FHWA Proven Countermeasures 

(safety.fhwa.dot.gov/

provencountermeasures) – 

FHWA regularly compiles a list 

of countermeasures that have 

been shown to be effective in 

reducing crashes but have yet 

to be widely applied on a 

national basis.

J Handbook for Designing Roadways 

for the Aging Population (safety.

fhwa.dot.gov/older_users/

handbook) – This FHWA guide 

provides practitioners with a 

practical information source that 

links aging road user performance

 to highway design, operational, 

and traffic engineering features. 

This handbook supplements 

existing standards and guidelines 

in the areas of highway 

geometry, operations, and 

traffic control devices.28

J Highway Safety Manual (www.

highwaysafetymanual.org) – 

This document provides 

science-based knowledge 

and tools to conduct safety 

analyses, allowing for safety 

to be quantitatively evaluated 

alongside other transportation 

performance measures, such 

as traffic operations, 

environmental impacts, 

and construction costs. 

J National Cooperative Highway 

Research Program (NCHRP) 

Report 500 Series (safety.

transportation.org/guides.aspx)

– This resource is a collection 

of 23 reports in which relevant 

information is assembled into 

single concise volumes, each 

pertaining to specific types 

of highway crashes (e.g., 

run-off-the-road, head-on) 

or contributing factors (e.g., 

aggressive driving) related 

to behaviors, vehicles, and 

roadways. Countermeasures 

are categorized as proven, 

tried, and experimental.

J Pedestrian Safety Guide and 

Countermeasure Selection System 

(PEDSAFE, www.pedbikesafe.

org/pedsafe) – This resource 

provides practitioners with the 

latest information available 

for improving the safety and 

mobility of those who walk. 

The online tools provide the 

user with a list of possible 

engineering, education, or 

enforcement treatments to 

improve pedestrian safety and/

or mobility based on user input 

about a specific location.

Goodwin, A., 

Thomas, L., Kirley, 

B., Hall, W., O’Brien, 

N., & Hill, K. (2015). 

Countermeasures 

that work: A 

highway safety 

countermeasure 

guide for State 

highway safety 

offices, Eighth 

edition. (Report No. 

DOT HS 812 202). 

Washington, DC: 

National Highway 

Traffic Safety 

Administration. 

Accessed February 

2017 at www.nhtsa.

gov/staticfiles/

nti/pdf/812202-

Countermeasures

ThatWork8th.pdf

Brewer, M., D. 

Murillo, A. Pate 

(2014). Handbook 

for Designing 

Roadways for the 

Aging Population, 

Report No.

FHWA-SA-14-015, 

Federal Highway 

Administration, 

Washington, D.C. 

27

28
4-22 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

Identifying and selecting 

countermeasures

After identifying potential 

countermeasures to target 

the underlying issues, safety 

professionals must estimate the 

safety impact of countermeasures, 

individually and in combination. It 

is important to consider positive and 

negative safety impacts. Subsequent 

steps of the roadway safety 

management process (i.e., economic 

appraisal and project prioritization) 

include the consideration of other 

parameters, such as constructability, 

environmental impacts, and cost. 

The agency that will be making the 

final decision on countermeasure 

selection should make sure to 

coordinate with other safety 

partners to ensure that the 

countermeasure is appropriate for all 

parties. For example, a DOT should 

coordinate with law enforcement 

and emergency response to make 

sure that a proposed engineering 

installation will interfere with 

enforcement activities or impede 

emergency responders.

For infrastructure improvements, 

CMFs associated with different 

countermeasures provide a 

mechanism for determining 

the safety effect of different 

countermeasures. A CMF is a 

multiplicative factor used to 

compute the expected number of 

crashes after implementing a given 

countermeasure at a specific site. 

J If the CMF for a particular 

treatment is less than 1.0, then 

that countermeasure is expected 

to reduce crashes. 

J If the CMF for a particular 

treatment is greater than 1.0, 

then that countermeasure is 

expected to increase crashes. 

J A CMF of 1.0 implies that a 

countermeasure will not have 

any effect on safety. 

For example, if the expected number 

of crashes without a countermeasure 

is 5.6 crashes per year, and the CMF 

for the particular countermeasure 

is 0.8, then the expected number of 

crashes with the countermeasure is:

5.6 crashes per year x 0.8 = 

4.48 crashes per year

It is important to recognize that 

some countermeasures may decrease 

some types of crashes but increase 

other types. For example, installing 

a traffic signal would be expected to 

decrease severe collisions, such as 

right angle and left turn crashes, but 

it would be expected to increase less 

severe crashes, such as rear ends. 

The CMF Clearinghouse and the first 

edition of the HSM provide CMFs 

for a variety of countermeasures.29

Only those CMFs that passed a set 

of inclusion criteria based on quality 

and reliability were included in the 

HSM. The CMFs in the clearinghouse 

Guidance on CMF application

FHWA provides an extensive selection 

of guidance on selecting and applying 

CMFs through the CMF Clearinghouse 

(www.cmfclearinghouse.org). They 

present answers to frequently asked 

questions, such as “How can I apply 

multiple CMFs?” and “How do I choose 

between CMFs in my search results that 

have the same star rating but different 

CMF values?” The website also houses 

an archive of annual webinars in which 

experienced CMF users talk about 

issues related to applying CMFs in 

real world situations. 

www.

cmfclearinghouse.

org

29
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-23

are provided for any published 

study, regardless of quality, and 

are continuously updated based on 

the latest research. The CMFs in 

the clearinghouse are reviewed and 

given a star quality rating ranging 

from one to five stars, based on the 

quality of the study. Higher stars 

imply a better quality CMF.

CMFs should be applied to situations 

that closely match those from 

which the CMF was developed. 

Several variables can be used to 

match a CMF to a given scenario 

including roadway type, area type, 

segment or intersection geometry, 

intersection traffic control, and 

traffic volume. However, it is 

critical for practitioners to use 

engineering judgment when a CMF 

is not available for the situations 

encountered as there are some cases 

for which a CMF that was developed 

for different conditions might be the 

best available.

Step 4. Economic appraisal

An economic appraisal of alternative 

countermeasures should be 

conducted to ensure that safety 

funds are being used as efficiently 

as possible. This appraisal helps 

transportation agencies achieve 

their desired safety performance the 

fastest and at the lowest possible 

cost. An agency can compare 

Rodegerdts et al., 

“NCHRP Report 

572: Applying 

Roundabouts in 

the United States.” 

Washington, D.C., 

Transportation 

Research Board, 

National Research 

Council, (2007)

30

A city has a stop-controlled intersection 

with an expected crash frequency of 10 

crashes per year, consisting of one A-injury 

crash, one B-injury crash, two C-injury 

crashes, and six PDO crashes.

The city is considering installing a 

roundabout at the intersection. Based on 

a search of the FHWA CMF Clearinghouse, 

they decide that they will use a CMF of 0.19 

in the calculation of the crash reduction 

benefit.30 This CMF applies only to serious 

and minor injury crashes, so they do not 

use it to estimate any reduction to fatal or 

PDO crashes (see note).

They multiply the CMF by the expected 

crashes before roundabout installation 

to determine the expected crashes after 

installation:

Thus, the benefit of a roundabout 

installation is expected to be a reduction of 

0.81 A-injury crashes, 0.81 B-injury crashes, 

and 1.62 C-injury crashes per year. 

NOTE: A roundabout would also likely bring 

a reduction to fatal and PDO crashes (i.e., 

additional CMFs could be incorporated), but 

the example has been simplified to a single 

CMF for illustration purposes.

Calculating benefits due to crash reduction

CRASH SEVERITY FA

A

INJURY

B

INJURY

CINJURY PDO

I Expected Crashes per Year 

before Roundabout 0 1 1 2 6

II CMF N/A 0.19 0.19 0.19 N/A

III Expected Crashes per Year 

after Roundabout (I x II) 0 0.19 0.19 0.38 6

IV Crash reduction benefit (I 

minus III) 0 0.81 0.81 1.62 0
4-24 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

the benefits expected from the 

countermeasure to the estimated 

costs of the countermeasure.

Some safety countermeasures have 

a higher-cost value than others. 

Geometric improvements to the 

road, such as straightening a tight 

curve to reduce run-off-road 

crashes, tend to be very expensive. 

Installing a curve warning sign 

and in curve delineation may 

address the same problem, but 

at a much lower cost. Although 

both countermeasures address the 

same problem, the actual safety 

benefit may not be the same. Safety 

professionals take the relative costs 

and benefits into consideration 

when prioritizing among 

countermeasures. Part of calculating 

the cost of a countermeasure 

is considering how those costs 

vary over time, while taking into 

consideration any maintenance 

costs and long term effectiveness. 

Estimating benefits

The primary benefit of a 

countermeasure is a reduction 

in crash frequency or severity. 

To estimate the safety benefits, 

a safety professional should use 

CMFs, such as those discussed 

in the countermeasure selection 

step. CMFs can be applied to the 

actual crashes or expected crashes 

based on the EB method. Expected 

crashes are preferred because they 

account for possible bias due to RTM. 

The estimated change in crashes 

represents the expected benefit from 

the countermeasure. 

For each proposed countermeasure, 

the change in crash frequency and/

or severity needs to be converted 

to monetary value, based on the 

monetary value of the type of 

crashes reduced. This monetary 

value is also called the crash cost. 

Crash costs are based on costs to 

society, such as lost productivity, 

medical costs, legal and court costs, 

emergency service costs, insurance 

administration costs, congestion 

costs, property damage, and 

workplace losses.31

The benefit from the 

countermeasure is the sum of the 

crash costs for crashes prevented by 

the countermeasure. Assigning costs 

to crashes is a topic that is under 

constant discussion and revision 

nationwide. States differ widely 

in the dollar amount that they 

assign to crashes, though all States 

apply higher values to more severe 

crashes. The CMF Clearinghouse 

provides a synthesis of crash costs 

that are used by various States.32

Additionally, the first edition of the 

HSM provided a list of crash costs by 

severity level (Table 4-7). However, 

since the publication of the first 

HSM in 2010, the USDOT has issued 

periodic recommendations that 

dramatically raised the values. For 

instance, the monetary value of a 

TABLE 4-7: Crash Costs by Severity Level 

in the Highway Safety Manual, 1st ed.

INJURY 

SEVERITY LEVEL

COMPREHENSIVE 

CRASH COS

Fatality (K) $4,008,900

Disabling Injury (A) $216,000

Evident Injury (B) $79,000

Fatal/Injury (K/A/B) $158,200

Possible Injury (C) $44,900

PDO (O) $7,400

Blincoe, L. J., Miller, 

T. R., Zaloshnja, E., 

and Lawrence, B. 

A. The economic 

and societal 

impact of motor 

vehicle crashes, 

2010. (Revised), 

National Highway 

Traffic Safety 

Administration, 

Report No. DOT HS 

812 013, Washington, 

DC, May 2015.

31

http://www.

cmfclearinghouse.

org/resources_

servlifecrash

costguide.cfm

32
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-25

fatal crash was listed as $4 million in 

the HSM, but recommended as over 

$9 million in a 2013 policy memo 

from USDOT.33

Although countermeasures are 

primarily expected to reduce 

crashes, there might be other 

benefits, including reduced travel 

times or lower fuel consumption. For 

example, a roundabout can decrease 

total delay at an intersection if 

applied and configured properly. 

An AASHTO publication provides 

guidance on estimating these other 

non-safety benefits.34

Estimating costs

The costs of the proposed 

countermeasure include the startup 

cost and the ongoing operational and 

maintenance costs. These costs can 

usually be estimated based on costs 

of materials, labor cost per personhour, cost of additional right-ofway, and past experience with 

similar countermeasures. Table 4-8 

illustrates the types of startup and 

ongoing costs that would be incurred 

for various countermeasures.

Service life

Another important consideration 

when calculating the benefits 

and costs of a countermeasure 

is the length of time that the 

countermeasure will last. This 

is referred to as the service life. 

Countermeasures, such as road 

edgelines or pavement reflectors, 

will have a much shorter service 

life (e.g., three to five years) than 

countermeasures, such as traffic 

signal installation or sidewalk 

construction (e.g., 20 years or 

more). Many States have a standard 

list of the service life values used 

for common countermeasures. 

The CMF Clearinghouse provides a 

survey of service life values used by 

various States for many different 

countermeasures.35

Trottenberg, 

Polly, and Robert 

Rivkin, “Revised 

Departmental 

Guidance 2013: 

Treatment of the 

Value of Preventing 

Fatalities and 

Injuries,” 

USDOT Office of 

the Secretary of 

Transportation, 2013.

User and Non-User 

Benefit Analysis for 

Highways, American 

Association of 

State Highway 

Transportation 

Officials, September 

2010

http://www.

cmfclearinghouse.

org/resources_

servlifecrash

costguide.cfm

33

34

35

The previous example showed that a city 

calculated a crash savings of 0.81 A-injury 

crashes, 0.81 B-injury crashes, and 1.62 

C-injury crashes per year by installing 

a roundabout. The city has examined 

guidance from the HSM, guidance from 

USDOT, and experiences of other cities 

and States and determined a standard set 

of crash costs they will use for all benefit/

cost calculations. They apply these costs 

to determine the monetary benefit of the 

expected crash reductions:

Thus, the city expects a total monetary benefit of $324,000+$81,000+$97,200 = 

$502,000 per year due to reduction in crashes.

Calculating monetary benefit of crash reduction

CRASH SEVERITY FA

A

INJURY

B

INJURY

CINJURY PDO

IV Crash Reduction Benefit 0 0.81 0.81 1.62 0

V This City’s Standard Crash Cost $5 mil $400,000 $100,000 $60,000 $10,000

VI Monetary benefit of crash 

reduction (IV x V) 0 $324,000 $81,000 $97,200 0
4-26 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

i×(1+i) y

The service life is used in the 

calculation of the present value 

of the benefits and costs of the 

proposed countermeasure. The 

calculation of present value includes 

a discount rate that reflects the time 

value of money (i.e., present dollars 

are worth more than future dollars). 

Present value of countermeasure 

benefits is calculated as follows:

costs, the final present value must 

also include the startup cost in the 

year of installation (see examples 

in Table 4-8).

Methods for 

economic appraisal

There are several methods for 

using the values of estimated 

benefits and costs to evaluate the 

economic effectiveness of safety 

improvement projects at a particular 

site. In particular, these methods 

TABLE 4-8: Examples of Countermeasure Costs

COUNTERMEASURE STARTUP CO

ONGOING COST 

DURING SERVICE LIFE

Install curve 

warning sign

Low – sign material, minimal 

labor for installation None

Install roundabout

High – Design plan, purchase 

of additional right-of-way, 

material, labor, traffic control 

during construction

Low – maintenance of 

grass and decorative 

vegetation

Install traffic signal

High – Timing plan, material, 

labor for installation, traffic 

control during construction

Moderate – electricity, 

bulb replacements, repairs, 

modifications to timing

PV = present value of benefits 

A = annual benefit (i.e., monetary value of 

crashes prevented)

i = discount rate

y = service life of countermeasure

Calculating present value in this way 

assumes a uniform annual benefit. 

The HSIP Manual demonstrates 

how to calculate present value if the 

benefits or costs each year are not 

the same.36

The present value of annual costs 

(i.e., operational and maintenance 

costs) can be calculated in the same 

manner as for benefits. However, for 

(1+i) y-1

PV = A ×

Herbel, Susan, 

Lorrie Laing, Colleen 

McGovern, Highway 

Safety Improvement 

Program 

(HSIP) Manual, 

Federal Highway 

Administration, 

FHWA-SA-09-029, 

January 2010

36

Calculating the present value 

of a crash reduction benefit

From the previous example, the city 

plans to install a roundabout and 

expects to see a benefit from crash 

reductions resulting in savings of 

$502,000 per year. They estimate that the 

roundabout will have a service life of 20 

years and they determine that a discount 

rate of 5% is appropriate. They calculate 

the present value of benefits as:

0.05×(1+0.05)

20

(1+0.05)

20-1

PV = $502,000 ×

= $6,256,030
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-27

are useful in situations where a 

safety professional is considering 

several alternatives and desires to 

choose the countermeasure with the 

greatest benefit for the cost. 

The HSIP Manual contains guidance 

on three methods - net present 

value, benefit/cost ratio, and cost 

effectiveness index.37 Net present 

value (NPV) is generally regarded 

as the most economically 

appropriate method, though the 

other two methods have certain 

advantages, as discussed below. 

The following sections provide 

quoted guidance from the HSIP 

Manual on economic appraisal.

Net Present Value

The NPV method, also called the 

net present worth (NPW) method, 

expresses the difference between the 

present values of benefits and costs 

of a safety improvement project. 

The NPV method has two basic 

functions: 1) determining which 

countermeasure(s) is/are most 

cost efficient based on the highest 

NPV and 2) determining whether 

a countermeasure’s benefits are 

greater than its costs (i.e., the 

project has a NPV greater than zero). 

The formula for NPV is: 

NPV = PVB − PVC

PVB = Present value of benefits

PVC = Present value of costs 

A countermeasure will result in a 

net benefit if the NPV is greater than 

zero. Table 4-9 summarizes the 

NPV calculations of four alternative 

countermeasures.

For Alternative A, the NPV can be 

calculated as follows: 

NPV = $1,800,268 − $500,000

= $1,300,268

The same calculation is performed 

for the other three countermeasure 

alternatives, and rank each 

countermeasure based on its NPV. 

As shown, all four alternatives 

are economically justified with a 

NPV greater than zero. However, 

Alternative B has the greatest NPV 

for this site based on this method. 

Benefit/Cost Ratio and Analysis

The benefit/cost ratio (BCR) is 

the ratio of the present value of a 

project’s benefits to the present 

value of a project’s costs.

Herbel, Susan, 

Lorrie Laing, Colleen 

McGovern, Highway 

Safety Improvement 

Program 

(HSIP) Manual, 

Federal Highway 

Administration, 

FHWA-SA-09-029, 

January 2010.

37

ALTERNATIV

COUNTERMEASUR

PRESENT 

VALUE OF

BENEFITS (I

PRESENT 

VALUE OF

COSTS (II)

NET 

PRESENT 

VALUE (I-II

ALTERNATIV

RAN

A $1,800,268 $500,000 $1,300,268 

B $3,255,892 $1,200,000 $2,055,892 

C $3,958,768 $2,100,000 $1,858,768 2

D $2,566,476 $1,270,000 $1,296,476 4

TABLE 4-9: Net Present Value (Source: HSIP Manual, Chapter 4)
4-28 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

The formula for BCR is: 

BCR = PVB / PVC

PVB = Present value of benefits

PVC = Present value of costs

Table 4-10 shows an example 

of using BCR to prioritize four 

alternatives.

A project with a BCR greater than 1.0 

indicates that the benefits outweigh 

the costs. However, the BCR is not 

applicable for comparing various 

countermeasures or multiple 

projects at various sites; this requires 

an incremental benefit/cost analysis.

An incremental benefit/cost analysis 

provides a basis of comparison of the 

benefits of a project for the dollars 

invested. It allows the analyst to 

compare the economic effectiveness 

of one project against another; 

however, it does not consider budget 

constraints. Optimization methods 

are best for prioritizing projects 

based on monetary constraints. 

An in-depth explanation of 

incremental benefit/cost analysis 

and an example is provided in 

Chapter 4 of the HSIP Manual. 

When conducting a benefit/

cost analysis, transportation 

professionals compare all of 

the benefits associated with 

a countermeasure (e.g., crash 

reduction), expressed in monetary 

terms, to the cost of implementing 

the countermeasure. A benefit/cost 

analysis provides a quantitative 

measure to help safety professionals 

prioritize countermeasures or 

projects and optimize the return 

on investment. 

Cost-Effectiveness Index

In situations where it is not 

possible or practical to monetize 

countermeasure benefits, 

transportation professionals can 

use the cost-effectiveness index 

method in lieu of the NPV or BCR. 

Cost-effectiveness is simply the 

amount of money invested divided 

by the crashes reduced. The result 

is a number that represents the 

cost of the avoided crashes of 

a certain countermeasure. The 

countermeasure with the lowest 

value is the most cost-effective and 

therefore ranked first. 

Cost-Effectiveness Index = PVC/CR 

PVC = Present value of project cost

ALTERNATIV

COUNTERMEASUR

PRESENT 

VALUE OF

BENEFITS (I

PRESENT 

VALUE OF

COSTS (II)

BENEFIT

COST 

RATIO (I/II

ALTERNATIV

RAN

A $1,800,268 $500,000 3.6 

B $3,255,892 $1,200,000 2.7 

C $3,958,768 $2,100,000 1.9 4

D $2,566,476 $1,270,000 2.0 3

TABLE 4-10: Example of Benefit/Cost Ratio Prioritization (Source: HSIP Manual, Chapter 4)
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-29

CR = Total crash reduction

The Cost-Effectiveness Index 

is a simple and quick method 

that provides an indication of a 

project’s value. Transportation 

professionals can use this formula 

and compare its results with other 

safety improvement projects. The 

Cost-Effectiveness Index method, 

however, does not account for value 

differences between reductions in 

fatal crashes compared to injury 

crashes, and whether a project is 

economically justified.38

Table 4-11 summarizes the 

calculations using the costeffectiveness index method to rank 

alternative countermeasures, given 

the present value of the costs and 

the total crash reduction.

For Alternative A, calculate the costeffectiveness index as follows: 

Cost-effectiveness index = 500,000/43 

= 11,628

Calculate the Cost-Effectiveness 

Index for the remaining alternatives 

and rank each countermeasure based 

on its Cost-Effectiveness Index 

value. With this method, the lowest 

index is the highest priority and 

therefore ranked first. Alternative 

A is ranked first, since it has the 

lowest cost associated with each 

crash reduction. 

The above example uses the number 

of crashes to determine the costeffectiveness index. Transportation 

professionals can use this same 

method using EPDO crash numbers, 

which has the advantage of 

considering severity.

Step 5. Project prioritization

If a transportation agency 

is considering installing 

countermeasures at one or more 

sites out of a group of potential 

sites, they will need to prioritize 

which projects they will implement. 

Ideally, the agency would implement 

all projects that bring a safety 

benefit (e.g., all those with a NPV 

greater than zero or a BCR greater 

than one). However, all agencies 

work within a limited budget and 

must prioritize where safety funds 

are spent.

The agency can use steps 1 through 

4 of this process to determine 

which countermeasure(s) would be 

used at each potential treatment 

ALTERNATIV

COUNTERMEASUR

PRESENT 

VALUE OF

COSTS

TOTAL

CRAS

REDUCTION

COSTEFFECTIVE- NESS INDEX

ALTERNATIV

RAN

A $500,000 43 $11,628 

B $1,200,000 63 $19,048 

C $2,100,000 70 $30,000 4

D $1,270,000 73 $17,397 2

TABLE 4-11: Cost-Effectiveness Index (Source: HSIP Manual, Chapter 4)

Highway Safety 

Improvement 

Program Manual.

Federal Highway 

Administration 

(Washington D.C., 

2010), Chapter 4

38
4-30 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

site and to conduct an economic 

appraisal of the expected effect of 

the countermeasure. The next step 

is to determine project priorities. 

The HSM discusses how projects 

can be prioritized by economic 

effectiveness, incremental 

benefit/cost analysis, or various 

optimization methods.

Prioritizing by 

economic effectiveness

Projects can be prioritized by ranking 

projects or project alternatives 

by the economic appraisal values 

produced in step 4. An agency 

might select those projects with the 

highest NPV, the highest BCR, or 

the highest cost effectiveness index. 

When using NPV the goal of a safety 

professional should be to implement 

all projects that have an NPV 

greater than zero, since each one 

brings a safety benefit. However, 

this is not possible since funds are 

limited, thus the goal should be to 

implement the group of projects 

that have the greatest combined 

NPV when added together (NPV is 

an additive property). Maximizing 

the NPV of a group of projects is 

different from prioritizing projects 

with high NPV. In other words, it 

may be best to implement numerous 

low cost projects with low NPV than 

one high cost project with a high 

NPV – but not higher than the NPV 

of all the low cost projects added up.

Prioritizing by incremental 

benefit/cost analysis

This method involves ranking all 

projects with benefit cost ratio 

greater than 1.0 in increasing order 

of their estimated cost. An analyst 

calculates an incremental BCR 

as such:

If the incremental BCR is greater 

than 1.0, the project with the 

higher cost is compared to the next 

project on this list; however, if the 

incremental BCR is less than 1.0, 

the project with the lower cost is 

compared to the next project on the 

list. This process is repeated and the 

project selected in the last pairing 

is the considered the best economic 

investment.

Prioritizing by 

optimization methods

Optimization methods take into 

account certain constraints when 

prioritizing projects. Linear 

programming, integer programming, 

and dynamic programming (refer 

to Chapter 8, Appendix A, HSM, 

2010) are optimization methods 

consistent with an incremental 

benefit/cost analysis, but they also 

account for budget constraints in 

the development of the project 

list. These optimization methods 

are more likely to be incorporated 

into a software package, rather 

than manually calculated. Multiobjective resource allocation is 

another optimization method. 

It incorporates nonmonetary 

elements (including decision factors 

not related to safety) into the 

prioritization process.

Safety professionals may use 

software applications to select 

and rank countermeasures. The 

SafetyAnalyst tool from AASHTO 

includes economic appraisal 

and priority ranking tools.39 The 

economic appraisal tool calculates 

(Cost of Project A − Cost of Project B) 

(Benefit of Project A − Benefit of Project B) 

Incremental BCR =

http://www.

safetyanalyst.org/

39
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-31

the BCR and other metrics for a set 

of countermeasures. The priorityranking tool ranks proposed 

improvement projects based on 

the benefit and cost estimates 

from the economic appraisal tool. 

The priority-ranking tool can also 

determine an optimal set of projects 

to maximize safety benefits.

Step 6. Safety 

effectiveness evaluation 

Once a countermeasure has been 

implemented at a site, or group of 

sites, it is important to determine 

whether it was effective in 

addressing the safety problem. For 

a safety professional to evaluate the 

countermeasure, he or she must 

determine how the countermeasure 

affected the frequency, type, and 

severity of crashes. For example, 

did the installation of a roundabout 

reduce the frequency of angle 

crashes? If so, by how much? Did 

it cause an increase to any other 

types of crashes? A countermeasure 

evaluation can result in a CMF 

for the countermeasure, which 

quantifies the effect on crashes (see 

CMF discussion in Step 4). 

Two documents entitled A Guide 

to Developing Quality Crash 

Modification Factors40 (from FHWA) 

and Recommended Protocols for 

Developing Crash Modification 

Factors41 (from NCHRP) provide 

guidance on the different methods 

for conducting evaluations. The 

following is an overview of study 

designs and methods for conducting 

evaluations. 

Categories of Study Designs

Study designs fall into two broad 

categories - experimental and 

observational. Experimental studies 

are conducted when sites are 

selected at random for treatment. 

There is general consensus that 

experimental studies are the most 

rigorous way to establish causality.42

In contrast, observational studies 

are conducted when sites are not 

selected as part of an experiment but 

selected for other reasons including 

Carter, D., R. 

Srinivasan, F. 

Gross, and F. 

Council (2012), 

Recommended 

Protocols for 

Developing Crash 

Modification 

Factors, Prepared 

as part of NCHRP 

Project 20-07 (Task 

314), Washington, 

D.C. Available 

at http://www.

cmfclearinghouse.

org/resources_

develop.cfm. 

Accessed July 2016.

Elvik, R. (2011a), 

Assessing Causality 

in Multivariate 

Accident Models, 

Accident Analysis 

and Prevention, Vol. 

43, pp. 253-264.

Gross, F., B. 

Persaud, and 

C. Lyon (2010), 

A Guide for 

Developing Quality 

Crash Modification 

Factors, Report 

FHWA-SA-10-032, 

Federal Highway 

Administration, 

Washington, 

D.C. Available 

at http://www.

cmfclearinghouse.

org/resources_

develop.cfm. 

Accessed July 2016.

41

42

40
4-32 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

safety. Truly experimental studies 

are not common in road safety 

partly because of potential liability 

considerations (i.e., a random 

selection may result in an agency 

being held liable for failing to treat 

some sites that have demonstrated 

high crash history). Observational 

studies are more common in 

countermeasure evaluations 

because most transportation 

agencies prioritize installation sites 

based on some kind of past safety 

performance (see Step 1, Network 

Screening). 

Observational studies of 

countermeasures can be broadly 

classified into cross-sectional 

studies and before-after studies. In 

cross-sectional studies, an analyst 

compares a group of sites with a 

certain feature to a group of sites 

without that feature. For example, 

an analyst might compare the 

safety performance of a group of 

stop-controlled intersections to 

that of a group of yield-controlled 

intersections to determine the effect 

of the type of traffic control on 

crashes. Cross-sectional studies can 

also be thought of as “with/without” 

studies. In before-after studies, an 

analyst takes a group of sites and 

compares the safety performance in 

the period before a countermeasure 

is implemented to the period after 

the countermeasure is implemented. 

For example, in a before-after study, 

an analyst could evaluate the effect 

of converting a stop-controlled 

intersection to a roundabout by 

comparing safety data before the 

roundabout conversion to the safety 

data afterwards. 

CMFs that result from crosssectional studies are not considered 

to be as robust as those resulting 

from a before-after study. In a 

typical before-after study, an 

analyst deals with same roadway 

unit located in a particular place, 

most likely used by the same road 

users during the before and after 

period. Since most of these factors 

can be assumed to be constant or 

almost constant in the before and 

after periods, they are less likely 

to cause significant biases. On 

the other hand, “cross-sectional 

studies compare different roads, 

used by different road users, located 

at different places and subject 

to different weather conditions. 

Besides, these roads will differ in 

very many other ways that are not 

measured.”43 However, there are 

issues in both types of studies that 

need to be addressed, and they are 

briefly discussed below.

Cross sectional studies

Analysts use cross-sectional studies 

to compare the safety of a group of 

sites with a feature with the safety of 

a group of sites without that feature. 

The resulting CMF can be derived by 

taking the ratio of the average crash 

frequency of sites with the feature 

to the average crash frequency of 

sites without the feature. For this 

method to work, the two groups 

of sites should be similar in their 

characteristics except for the 

feature. In practice, this is difficult 

to accomplish and multiple variable 

regression models are used. These 

cross-sectional models are also 

called SPFs. The coefficients of the 

variables from these equations are 

used to estimate the CMF associated 

with a treatment.

Guidance from FHWA on developing 

CMFs says that “the basic issue 

with the cross-sectional design is 

Elvik, R. (2011a), 

Assessing Causality 

in Multivariate 

Accident Models, 

Accident Analysis 

and Prevention, Vol. 

43, pp. 253-264.

43
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-33

that the comparison is between two 

distinct groups of sites. As such, 

the observed difference in crash 

experience can be due to known or 

unknown factors, other than the 

feature of interest. Known factors, 

such as traffic volume or geometric 

characteristics, can be controlled 

for in principle by estimating a 

multiple variable regression model 

and inferring the CMF for a feature 

from its coefficient. However, the 

issue is not completely resolved 

since it is difficult to properly 

account for unknown, or known 

but unmeasured, factors. For 

these reasons, caution needs to 

be exercised in making inferences 

about CMFs derived from crosssectional designs. Where there are 

sufficient applications of a specific 

countermeasure, the before-after 

design is clearly preferred.”45

Gross, F., B. 

Persaud, and 

C. Lyon (2010), 

A Guide for 

Developing Quality 

Crash Modification 

Factors, Report 

FHWA-SA-10-032, 

Federal Highway 

Administration, 

Washington, 

D.C. Available 

at http://www.

cmfclearinghouse.

org/resources_

develop.cfm. 

Accessed July 2016.

45

A CMF can be obtained from a cross 

sectional model. Suppose the intent is 

to estimate the CMF for shoulder width 

based on the following SPF, which was 

estimated to predict the number of crashes 

per mile per year on rural two-lane roads in 

mountainous roads with paved shoulders 

(Appendix B of Srinivasan and Carter, 201144):

Where, AADT is the annual average daily 

traffic and SW is the width of the paved 

shoulder in feet. If the intent is to estimate 

the CMF of changing the shoulder width 

from three to six feet, then the CMF can 

be estimated as the ratio of the predicted 

number of crashes when the shoulder 

width is six feet to the predicted number 

of crashes when the shoulder width is 

three feet:

This ratio simplifies to: 

CMF = exp [-0.0164 × (6-3) ] = 0.952

This CMF of 0.952 indicates that changing 

the shoulder width from three to six feet 

would be expected to reduce crashes (since 

the CMF is less than 1.0). Specifically, the 

expected change in crashes would be a 

4.8% reduction (1.0 – 0.952 x 100 = 4.8).

However, it is important to recognize that 

this CMF of 0.952 is the midpoint in a range 

of possible values (i.e., the confidence 

interval). This range can be calculated by 

using the standard deviation of the CMF. In 

order to estimate the standard deviation, 

the standard error of the coefficient of SW 

is needed, which was reported to be 0.0015 

in the original study. The high and low ends 

of the confidence interval are calculated 

using -0.0164+0.0015, and then using 

-0.0165-0.0015, and the difference between 

the two is divided by two. The equation is 

given below:

The approximate 95% confidence 

interval for the CMF is (0.952-1.96×0.004, 

0.952+1.96×0.004), which translates to a 

range of 0.944 to 0.960. Since the entire 95% 

confidence interval is below 1.0, the CMF is 

statistically significant, thereby indicating 

that widening the shoulder from three to six 

feet is very likely to reduce crashes. 

Srinivasan, R. and 

D. Carter (2011), 

Development of 

Safety Performance 

Functions for North 

Carolina, Report 

FHWA/NC/2010-

09, Submitted to 

NCDOT, December 

2011.

44

Using cross-sectional modeling to calculate a CMF for widening shoulders

Y = exp [ 0.8727 + 0.4414 × ln( ) + 0.4293 × ( ) -0.0164 × SW ]

StDev(CMF) = = 0.004

CMF = 

exp [ 0.8727 + 0.4414 × ln( ) + 0.4293 × ( ) -0.0164 × 6 ]

exp [ 0.8727 + 0.4414 × ln( ) + 0.4293 × ( ) -0.0164 × 3 ]

AADT

10000

AADT

10000

AADT

10000

AADT

10000

AADT

10000

AADT

10000

exp [-0.0164+0.0015 × (6-3) ] - exp [-0.0164-0.0015 × (6-3) ]

2
4-34 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

Holmes, W.M., (2013). 

Using Propensity Scores 

in Quasi-Experimental 

Designs. SAGE 

Publications.

Wood, J., Porter, R., 

(2013). Safety impacts 

of design exceptions 

on non-freeway 

segments. Transport. 

Res. Rec.: J. Transport.

Res. Board 2358, 29–37.

Gooch, J.P., Gayah, 

V.V., and Donnell, E.T. 

(2016), Quantifying 

the Safety Effects of 

Horizontal Curves 

on Two-Way, TwoLane Rural Roads, 

Accident Analysis 

and Prevention, Vol. 

92, pp. 71-81.

47

48

46

One way to account for some of 

the limitations of cross-sectional 

regression models is to use the 

propensity scores-potential 

outcome method. This method 

uses the “individual traits of a 

site to calculate its propensity 

score, defined as a measure of the 

likelihood of that site receiving a 

specific treatment. Sites with and 

without the treatment are then 

matched based on their propensity 

scores.”46 The matched data are 

then used to estimate a cross 

sectional regression model. The 

propensity score method has been 

shown to reduce selection bias by 

accounting for the non-random 

assignment of treatment sites.47

Recently, the propensity score 

method is starting to be used in 

place of traditional cross-sectional 

methods to conduct evaluations.48

Other types of cross-sectional 

methods include case control and 

cohort methods. “Case-control 

studies select sites based on outcome 

status (e.g., crash or no crash) and 

then determine the prior treatment 

(or risk factor) status within each 

outcome group.”49 Another critical 

component of many case-control 

studies is the matching of cases with 

controls in order to control for the 

effect of confounding factors. In 

cohort studies, sites are assigned to 

a particular cohort based on current 

treatment status and followed over 

time to observe exposure and event 

frequency. One cohort may include 

the treatment and the other may be 

a control group without the 

treatment. The time to a crash in 

these groups is used to determine a 

relative risk, which is the percentage 

change in the probability of a crash 

given the treatment.50

See next page.

Gross, F., B. Persaud,

and C. Lyon (2010), 

A Guide for 

Developing Quality 

Crash Modification 

Factors, Report 

FHWA-SA-10-032, 

Federal Highway 

Administration, 

Washington, D.C. 

Available at http://

www.cmfclearing 

house.org/

resources_ develop.

cfm. Accessed July 

2016.

50

49
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-35

Before after studies

An analyst can use a before-after 

study to evaluate a countermeasure 

by comparing the crashes before the 

countermeasure was installed to the 

crashes after installation. This study 

design is advantageous because 

the only change that has occurred 

at the site is the countermeasure 

installation (assuming the analyst 

has researched the site histories 

to discard any sites at which other 

significant changes occurred).

There are issues for consideration 

with this study design as well. 

The analyst must know when the 

countermeasure was installed and 

must have data, such as crash and 

traffic volume, available in the before 

and after periods. For high-cost, 

high-profile countermeasures, such 

as road widening or traffic signal 

installation, the installation records 

will be readily available. However, for 

low-cost countermeasures, such as 

sign installations, there may be little 

to no documentation on when they 

were installed.

The analyst might simply compare 

the number of crashes per year 

before the countermeasure to the 

number of crashes per year after the 

countermeasure, known as a simple 

or naïve before-after evaluation. 

Although a simple before-after 

evaluation can be done easily 

using only crash data, it is prone to 

significant bias. One of the most 

influential biases for this method 

is the possible bias due to RTM. As 

discussed earlier, RTM describes a 

situation in which crash rates are 

artificially high during the before 

period and would have been reduced 

even without an improvement to 

the site. Programs focused on highhazard locations are vulnerable to 

the RTM bias. This potential bias 

is greatest when sites are chosen 

because of their extreme value (e.g., 

high number of crashes or crash 

rate) in a given time period. A simple 

before-after evaluation has a high 

likelihood of showing a much greater 

benefit from the safety treatment 

than actually occurred.

As discussed earlier under the 

network screening section, the EB 

method is one of the methods that 

has been found to be effective in 

dealing with the possible bias due to 

RTM. The following steps are needed 

to conduct an EB before-after 

evaluation:

1. IDENTIFY a reference group of 

sites without the treatment, but 

similar to the treatment sites 

in terms of the major factors 

that affect crash risk including 

traffic volume and other site 

characteristics. One way to 

identify a reference group that 

is similar to the treatment is to 

use the propensity score method 

discussed earlier under crosssectional studies.

2. Using data from the reference 

site, ESTIMATE SPFs using data 

from the reference sites relating 

crashes to independent variables, 

such as traffic volume and other 

site characteristics. As discussed 

in the following steps, SPFs 

are used in the EB method to 

predict the average number of 

crashes based on AADT and site 

characteristics. By selecting 

the reference group to be 

similar to the treatment group 

in terms of the major risk 

factors, we can reduce the 

possible bias due to confounding 

on these predictions. 

Carter, D., R. 

Srinivasan, F. 

Gross, and F. 

Council (2012), 

Recommended 

Protocols for 

Developing Crash 

Modification 

Factors, Prepared 

as part of NCHRP 

Project 20-07 (Task 

314), Washington, 

D.C. Available 

at http://www.

cmfclearinghouse.

org/resources_

develop.cfm. 

Accessed July 2016.

50
4-36 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

This example is an illustration of an EB 

before-after evaluation that was conducted 

as part of NCHRP Project 17-35.51 The 

countermeasure was a change from 

permissive to protected-permissive left turn 

phasing at signalized intersections in North 

Carolina. Data from twelve locations were 

used in this evaluation. A reference group 

of 49 signalized intersections was identified 

for the development of SPFs. The analysis 

looked at total intersection crashes, injury 

and fatal crashes, rear end crashes, and left 

turn opposing through (LTOT) crashes. In 

this example, only the data for LTOT crashes 

will be used.

The SPF for LTOT crashes based on the data 

from the reference group was:

LTOT crashes/intersection/year = 

e-0.3696 (MajAADT/10000)

0.5564

e0.6585×(MinAADT / 10000)

Where, MajAADT is the major road AADT 

and the MinAADT is the minor road AADT. 

The overdispersion parameter (k) for this 

SPF was 0.5641.

In the first site of this study, there were 10 

observed crashes in the before period (Xb

), 

and the predicted number of crashes from 

the SPF in the before period was 5.535 (Pb

). 

The formula for obtaining the EB estimate 

of the expected crashes in the before period 

(EBb

) is as follows:

EBb = w × Pb + ( 1 - w ) × Xb

Where, Xb is the observed crashes in the 

before period, and w is the EB weight that is 

calculated as follows:

Where, k is the overdispersion parameter 

for the estimated SPF.

In this example: 

The EB estimate of the crashes in the before 

period (EBb

) = 5.535*0.243 + 10*(1-0.243) = 

8.917 crashes.

The predicted number of crashes from the 

SPF in the after period was 11.391 (Pa

). 

The formula for the EB expected number 

of crashes that would have occurred 

in the after period had there been no 

countermeasure is given by: 

π = EBb × ( Pa / Pb )

In this example, the EB expected number 

of crashes in the after period had the 

countermeasure not been implemented 

(π) is equal to:

8.917 × ( 11.391 / 5.535 )

= 18.350 crashes

The variance of this expected number of 

crashes is also estimated in this step:

Var( π ) = π × ( Pa / Pb ) × ( 1 - w )

Where, Pa is the SPF predictions in the after 

period. In this example, the variance of ππ 

is estimated as follows:

Var( π ) = 18.350 × ( 11.391 / 5.535 ) 

× ( 1 - 0.243 ) = 28.603

This process was repeated for all 12 sites. 

Based on the data for all the 12 sites that 

were used in the evaluation, the actual 

crashes in the after period were 115, the EB 

expected crashes had the countermeasure 

not been implemented was 131.933 with a 

variance of 140.080. 

(continued on next page)

Srinivasan, R. et 

al., Evaluation of 

Safety Strategies 

at Signalized 

Intersections, 

NCHRP Report 705, 

Washington, DC.

51

Using an EB before-after evaluation to develop a CMF for signal phasing changes

1

( 1 + k × Pb

 )

w =

1

( 1 + 0.5641 × 5.535 )

w = = 0.243
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-37

The formula for the CMF and its standard 

deviation (StDev) are as follows:

Where, λsum is the total number of crashes 

that occurred in the after period, for all 

the treated sites in the sample, πsum is the 

total number of expected crashes in the 

after period had the countermeasure not 

been implemented, and Var represents the 

variance. Since crashes are assumed to 

be Poisson distributed, Var(λsum) is usually 

assumed to be equal to λsum. So, (Var(λsum))/

(λsum

2

 ) will be equal to 1/λsum.

In this example, the overall CMF was 

calculated as:

This CMF of 0.865 indicates that the 

countermeasure (changing from permissive 

to protected-permissive left turn phasing) 

would decrease crashes, since the CMF 

is less than 1.0. It would be expected 

to decrease crashes by 13.5% 

(1.0 – 0.865 x 100 = 13.5).

Again, it is important to recognize that the 

CMF is the midpoint of a range of possible 

values (i.e., the confidence interval). The 

standard deviation of the CMF can be 

estimated as follows:

Based on this standard deviation 

of the CMF, the approximate 95% 

confidence interval is (0.865-1.96×0.111, 

0.865+1.96×0.111), which translates 

to a range of 0.647 to 1.083. Since this 

confidence interval includes values greater 

than 1.0, the CMF is not statistically 

different from 1.0 at the 95% confidence 

level. This indicates that there is less 

confidence that this countermeasure 

will reduce crashes compared to a 

countermeasure whose CMF is significantly 

different from 1.0.

λsum

πsum

 Var ( πsum ) 2

π2

sum

CMF =

1 +

 115

131.933

 140.080

131.9332

CMF = = 0.865

= 0.111

1 +

StDev( CMF ) =

StDev( CMF ) =

 Var ( λsum )

λ2

sum

 Var ( πsum )

π2

sum

 Var ( πsum )

π2

sum

CMF2 +

( 1 + ) 2

 1

115

 140.080

131.9332

 140.080

131.9332

0.8652 +

( 1 +

( 

) 2

)
4-38 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

3. In estimating SPFs, CALIBRATE

annual SPF multipliers to 

account for the temporal effects 

(e.g., variation in weather, 

demography, and crash 

reporting) on safety. The annual 

SPF multiplier is the ratio of the 

observed crashes to the predicted 

crashes from the SPF. In using 

the annual SPF multipliers from 

the SPFs to account for temporal 

effects, it is assumed that the 

trends in the crash counts are 

similar in the treatment and 

reference groups.

4. USE the SPFs, annual SPF 

multipliers, and data on traffic 

volumes for each year in the 

before period for each treatment 

site to estimate the number of 

crashes that would be predicted 

for the before period in each site.

5. CALCULATE the EB estimate 

of the expected crashes in the 

before period at each treatment 

site as the weighted sum of the 

actual crashes in the before 

period and predicted crashes 

from Step 4. 

6. For each treatment site, 

ESTIMATE the product of the EB 

estimate of the expected crashes 

in the before period and the SPF 

predictions for the after period 

divided by these predictions for 

the before period. This is the EB 

expected number of crashes that 

would have occurred had there 

been no treatment. The variance 

of this expected number of 

crashes is also estimated in 

this step.

The expected number of crashes 

without the treatment along with 

the variance of this parameter and 

the number of reported crashes after 

the treatment is used to calculate 

the CMF and the standard deviation 

of the CMF. This procedure is 

repeated for each treated site. Once 

CMFs have been calculated for each 

individual site in a group of treated 

sites, the CMFs can be combined to 

calculate the overall effectiveness 

of the countermeasure. More details 

on this procedure are provided in 

the previously mentioned guidance 

documents.52,53

In some cases, treatments may 

be installed system-wide for a 

particular type of facility. For 

example, a jurisdiction may decide 

to increase the retroreflectivity 

of all their stop signs. Since sites 

are not specifically selected based 

on their crash history, the bias 

due to RTM is minimal. However, 

it is still necessary to account for 

changes in traffic volume and other 

trends. To evaluate the safety of 

such installations, an EB method 

could still be used, and while a 

reference group is not necessary, 

a comparison group is necessary 

in order to account for trends. 

SPFs can be estimated using the 

before-data from the treatment 

sites and these SPFs can be used 

to account for changes in traffic 

volumes. In addition, SPFs could be 

estimated for a group of comparison 

sites and the annual factors from 

these SPFs can be used to account 

for trends. Further details about 

such evaluations can be found 

elsewhere.54

Carter, D., R. 

Srinivasan, F. 

Gross, and F. 

Council (2012), 

Recommended 

Protocols for 

Developing Crash 

Modification 

Factors, Prepared 

as part of NCHRP 

Project 20-07 (Task 

314), Washington, 

D.C. Available 

at http://www.

cmfclearinghouse.

org/resources_

develop.cfm. 

Accessed July 2016.

B. Persaud and 

C. Lyon (2007), 

Empirical Bayes 

Before After 

Studies: Lessons 

Learned from 

Two Decades of 

Experience and 

Future Directions, 

Accident Analysis 

and Prevention, 

39(3):546-55.

Gross, F., B. 

Persaud, and 

C. Lyon (2010), 

A Guide for 

Developing Quality 

Crash Modification 

Factors, Report 

FHWA-SA-10-032, 

Federal Highway 

Administration, 

Washington, 

D.C. Available 

at http://www.

cmfclearinghouse.

org/resources_

develop.cfm. 

Accessed July 2016.

53

54

52
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-39

System-level safety management 

involves addressing road safety 

issues that affect the broad 

transportation system, as opposed 

to treating specific high priority 

sites. The size and scope of the 

transportation system depends 

on the agency or jurisdiction. For 

a State DOT, the transportation 

system would consist of all Stateowned roads, signals, bridges, and 

other features across the entire 

State, whereas the transportation 

system for a town would consist of 

a much smaller area and roadway 

network. Road safety at a systemlevel often has to do with policies, 

whether design policies for the 

construction and operation of roads 

and intersections, driver policies for 

licensing, or vehicle policies that 

require certain safety technologies. 

Other system-level efforts would 

include broad media or enforcement 

campaigns.

Recall that Chapter 10 presented 

road safety management in terms of 

three general components: 

J Identifying safety problems

J Developing potential 

safety strategies

J Selecting and 

implementing strategies

This chapter will discuss how each of 

these components can be addressed 

at a system-level.

Identifying safety problems

To identify safety problems on a 

system-level, safety professionals 

analyze safety data that apply 

to the entire jurisdiction. They 

examine crash data and link crashes 

to other safety data to determine 

the nature and locations of safety 

problems. Problem identification on 

a system-level involves identifying 

crash trends and using risk-based 

methods to prioritize safety efforts.

Identifying crash type trends

Safety professionals can examine 

crash types and contributing factors 

to determine the nature of crashes 

within their agency’s jurisdiction. 

This type of examination may reveal 

crash trends, such as those related 

to alcohol involvement, seat belt 

use, driver age, or vulnerable road 

users. For example, crash data 

might show that crashes involving 

unbelted occupants have been 

increasing over the past several 

System-Level Safety Management

CHAPTER 12 UNIT 4: SOLVING SAFETY PROBLEMS

System-wide vs. systemic?

System-wide is a general term that refers 

to treating safety issues across an entire 

transportation system using policies or 

campaigns. Systemic is a more specific 

term that refers to identifying a subset 

of a transportation system based on 

risk factors and implementing safety 

efforts that address the particular 

characteristics of that subset. 

See page 4-41 for more discussion 

on the systemic approach.
4-40 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

years, or it might show that the 

number of crashes involving 

unbelted occupants is significantly 

higher than other nearby agencies, 

such as adjacent counties or 

States. This would lead an agency 

to consider how to increase seat 

belt use, perhaps through media 

campaigns, increased enforcement, 

or educational campaigns in schools. 

This type of agency-wide analysis 

of crash data can demonstrate 

broad scale trends that need to 

be addressed through broad scale 

efforts. 

It is important that safety 

professionals are specific when 

identifying safety problems in 

crash trends. For example, “crashes 

involving teen drivers” is not 

defined well enough, because the 

causes of crashes for 16 year-olds 

is markedly different from those 

of older, more experienced teens. 

Crashes in which teens are victims 

of other drivers’ errors require 

different solutions from those where 

the teen was at fault. Similarly, the 

cause of crashes depends greatly on 

the specific time, place and driving 

environment. A better target crash 

type would be “crashes occurring 

between 7-9 a.m. involving 16-year 

old drivers.” 

Example of safety 

problem identification in 

State Highway Safety Plans

A good example of identifying 

safety problems from crash type 

trends can be seen in how States 

develop strategic highway safety plans 

(SHSPs). The development of a SHSP 

involves the identification of safety 

problems on the State and local 

roads. A State analyzes safety data 

to determine the priorities, referred 

to as emphasis areas. The analysis 

can involve an examination of crash 

proportions between categories of 

crashes, crash trends, crash severity 

(e.g., fatal and serious injury), or 

more advanced crash modeling 

techniques. As presented in the 

call-out boxes, Ohio and Florida 

conducted analyses of their crash 

data and identified areas of concern.

Strategic 

highway 

safety plan

A statewidecoordinated 

safety plan 

that provides a 

comprehensive 

framework 

for reducing 

highway 

fatalities and 

serious injuries 

on all public 

roads.

Florida’s emphasis on 

motorcyclist safety

The State of Florida examined its crash 

data to identify emphasis areas in the 

development of their SHSP in 2012. One 

area that continued to be a focus was 

motorcyclist safety. The data indicated 

that crashes involving motorcycles 

had decreased somewhat during the 

time period analyzed (2006 to 1010) 

but remained a significant portion of 

the crashes on Florida roads. Florida’s 

safety professionals recognized that 

since Florida hosts numerous national 

motorcycle events, the state’s SHSP 

should have motorcycle safety as an 

emphasis area.

2006 2007 2008 2009 2010

2,324

2,548

Serious Injuries

Fatalities

2,813 2,758

2,850

383 402

550 550 532

FIGURE 4-8. Florida motorcycle crash 

trend 2006-2010
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-41

Risk based prioritization – 

the systemic approach

Chapter 11 presented various 

methods of selecting high priority 

sites through a process of network 

screening based on crash data. Many 

safety professionals recognize that 

this process of identifying specific 

locations using past crash data does 

not adequately address the fact that 

there may be locations that pose 

a safety threat but have not yet 

experienced many (or any) crashes. 

This recognition led to an increased 

use of risk-based prioritization, also 

called the systemic approach.55

In this approach, a transportation 

agency identifies priority locations 

based on the presence of risk factors 

rather than crashes. In the medical 

field, doctors pay attention to factors 

that may elevate a person’s risk for 

disease. A history of smoking, poor 

eating habits, and a lack of exercise 

may indicate a higher-than-average 

risk for heart disease, even if the 

person has not yet experienced heart 

problems. Similarly, a section of 

road with certain characteristics, 

such as sharp curvature, old 

pavement, or lack of visibility, may 

be at risk for run-off-road crashes, 

even if none have occurred yet. 

Agencies can be proactive in their 

approach to safety management 

by identifying and treating these 

sites before crashes occur. These 

treatments are often low cost, such 

as signs and markings, so many 

systemic-identified locations can be 

treated within an agency’s limited 

budget. 

An agency using the systemic 

approach selects the focus crash 

type(s) and identifies risk factors 

associated with the focus crashes. 

Risk factors are site characteristics 

(e.g., design and operational 

features) that are common across 

Systemic

The process of 

identifying road 

or intersection 

characteristics 

that increase 

the risk of 

crashes and 

selecting 

locations 

for safety 

treatment 

based on the 

presence of 

these risk 

factors.

Ohio’s emphasis on 

older driver safety

Ohio developed a SHSP in 2014 in which 

they identified fifteen emphasis areas. One 

of the emphasis areas was the safety of 

older drivers (65 and older). The crash data 

showed that older driver-related crashes 

accounted for 18% of highway deaths and 

16% of serious injuries. They recognized 

that these numbers would likely increase 

with an aging population. The crash trends 

over the time period examined (2003 to 

2013) showed a slight upward trend to 

older driver serious injuries and a slight 

downward trend to older driver fatalities. 

This contrasted to other types of crashes 

that experienced significant declines. 

These reasons motivated Ohio to make 

older driver safety an emphasis area in their 

2014 SHSP. 

http://safety.fhwa.

dot.gov/systemic/

55
4-42 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

locations with the focus crash 

type(s). The agency can identify risk 

factors by analyzing crash data from 

their jurisdiction or by reviewing 

previous research studies. Using 

the list of risk factors as a guide, the 

agency identifies a list of sites with 

those specific characteristics, and 

then develops targeted treatments 

to address or mitigate the specific 

risk factors. The agency can apply 

crash history and other thresholds 

to reduce the list of sites based on 

available resources and program 

objectives. 

The systemic approach has two 

attractive features. First, an agency 

can employ the systemic approach 

even for roads or intersections where 

crash data are not fully available 

(e.g., where location accuracy is 

questionable or underreporting is 

a problem). For instance, locating 

crashes accurately and precisely in 

rural areas or on non-State owned 

urban roads can be difficult. Second, 

the systemic approach is useful 

for treating safety issues where 

crashes are highly dispersed, such 

as on rural or low volume roads. 

Specifically, agencies can use the 

systemic approach to address 

existing and potential safety issues 

across a large portion of the network 

(e.g., shoulder rumble strips on all 

rural, two-lane roads with a certain 

shoulder width and traffic volume 

level). 

Developing potential 

safety strategies

After safety professionals analyze 

data and identify safety problems, 

they must develop potential 

strategies to address the problems. 

It is important to engage safety 

stakeholders and other partners 

when selecting potential strategies 

as they may provide unique 

perspectives. Safety professionals 

should seek to involve local officials, 

citizens, and safety partners to 

produce effective multidisciplinary 

strategies. For example, addressing 

a particular safety problem with 

law enforcement and education 

can be far more economical than 

implementing a multimilliondollar engineering fix. On the other 

hand, law enforcement tends to 

be effective only during the time 

in which it is active, so a more 

permanent engineering measure 

may be needed in some cases. It is 

often the case that a combination 

of strategies is necessary to 

effectively address the multitude of 

contributing factors.

On a system level, agencies must 

think broadly across the many 

disciplines represented by those 

who have a stake in road safety. 

Potential strategies might address 

infrastructure policies and practices 

(e.g., design standards, speed limits, 

etc.) or they may be directed at 

specific population focused efforts 

(e.g., seat belt laws, helmet laws, 

young driver restrictions, etc.).

Just as the identification of problems 

was based on safety data, so too 

must the development and selection 

of strategies be driven by the data. 

If an agency identified concerning 

trends in certain types of crashes, 

then they should further examine 

the crash data to determine how 

best to address the safety problem. 

For example, Figure 4-9 shows an 

example of alcohol-related crashes 

where an agency identified a spike in 

frequency (or high pole) of crashes 

occurring near 2:00 AM. Further 

examination revealed that bars in 
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-43

The Thurston County Public Works 

Department in Washington conducted 

a systemic safety analysis for their road 

network. Based on a review of severe 

crashes, Thurston County decided to focus 

on roadway departure crashes in horizontal 

curves on arterial and collector roadways 

when it found that:

1. Most of the severe crashes occurred 

due to roadway departures, and that

2. 81% of the severe curve /roadway 

departure crashes occurred on arterial 

and collector roads. Because this 

effort coincided with ongoing efforts 

to identify and upgrade warning 

signs for horizontal curves on their 

County road system, Thurston County 

chose to focus on currently signed 

horizontal curves.

Thurston County accessed an inventory 

of their roads and intersections through 

a database maintained by the Statewide 

County Road Advisory Board. In addition, 

Thurston County assembled crash data 

for the 2006-to-2010 timeframe from the 

Washington State DOT crash database. 

They linked the road, intersection, and 

curve data with crash data and used these 

data to identify risk factors. Thurston 

County assembled a list of 19 potential risk 

factors and then performed a descriptive 

statistics analysis to identify 9 risk factors 

for use in screening and prioritizing 

candidate locations. The identified risk 

factors were:

J Roadway class of major rural collector

J Presence of an intersection

J Traffic volume of 3,000 to 7,500 

annual average daily traffic

J Edge clearance rating of 3

J Paved shoulders equal to or 

greater than 4 feet in width

J Presence of a vertical curve

J Consecutive horizontal curves 

(windy roads)

J Speed differential between posted 

approach speed and curve advisory 

speed of 0, 5, and 10 miles per hour

J Presence of a visual trap (a minor road 

on the tangent extended)

Thurston County decided that a risk factor 

could be worth one point or a one-half 

point. Those factors present in at least 30% 

of the severe (fatal and injury) crashes and 

overrepresented by at least 10% (when 

comparing the proportion of all locations 

with the proportion of severe crash 

locations) were used as a guideline to have 

a high confidence and assigned one point 

in the risk assessment process. The risk 

factors that had a lower confidence in their 

relative data were assigned one-half point. 

Thurston County then tallied the number 

of risk factors present for each of the 

curves. The risk factor totals for the ten 

curves with the highest scores ranged 

from 4.5 to 6.0. All 270 signed curves 

were prioritized for potential low cost 

safety investments. They identified the 

following low-cost, low-maintenance 

countermeasures with documented 

crash reductions to implement at the 

selected locations: 

J Traffic signs – enhanced curve 

delineation with the addition of 

chevrons and larger advance 

warning signs

J Pavement markings – dotted extension 

lines at intersections and recessed 

raised pavement markers

J Shoulder rumble strips

J Roadside improvements – object 

removal, guardrail, and slope flattening

Systemic analysis provided Thurston 

County a proactive, data-driven, and 

defensible approach to identifying curves 

for improvement prior to a severe crash 

occurring, rather than reacting after an 

incident has occurred.56

“Thurston County, 

Washington, Public 

Works Department 

Applies Systemic 

Safety Project 

Selection Tool” 

FHWA-SA-13-026, 

June 2013. http://

safety.fhwa.dot.gov/

systemic/

56

Case Study: Systemic Analysis in Thurston County, Washington
4-44 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

that jurisdiction closed at 2:00 AM. 

This could lead to potential strategies, 

such as increased enforcement of 

impaired driving at that time of night 

and in the vicinity of bars. 

It is also important to use the data 

to determine the necessary scope of 

the intervention. If the data show 

that the problem exists year-round, 

then the solution needs to match 

that. For example, a “safe ride 

program” for drinkers to get home 

on New Year’s Eve is not going to 

significantly impact the problem of 

impaired driving overall.

Critical thinking is needed to 

develop effective solutions to the 

safety problems at hand. Analysts 

should look for characteristics of 

crash trends that could be addressed 

by practical strategies. An NCHRP 

report on an integrated safety 

management process states that 

safety professionals should use 

safety data to perform “further 

analyses of those characteristics 

that are found to be significantly

or practically over-represented on 

a percentage or rate basis.”57 The 

report gives a set of guidelines to be 

considered in analyzing crash data to 

identify trends and develop potential 

safety strategies:

1. ASK the questions, “Is this 

information sufficient for action 

item development? If not, what 

further information is needed to 

act on this finding?”

2. CONSIDER cross tabulations of 

two variables within the subset 

of data that pertains to the 

activities under consideration 

if one or more of the following 

types of conditions hold: 

J If the activities are time 

critical (e.g., all selective 

0

5

10

15

20

0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Example of a high pole in a jurisdiction 

where bars close at 2:00 a.m.

NUMBER OF COLLISIONS

TIME OF DAY

FIGURE 4-9. Example of “high pole” in crash data (Source: NCHRP Report 501)

Consider How Specific Behaviors 

Influence the Safety Problem

Safety professionals must identify 

and target road user behaviors that 

contribute to the identified safety 

problem. The target behavior should 

be specific. For example, “safe driving” 

is not a specific behavior that can be 

changed because it involves a number of 

different behaviors. However, “speeding 

on Main Street” is a specific behavior 

that can be targeted. It is also important 

to consider the factors influencing this 

behavior. Why are people speeding on 

Main Street? Which social, cultural, or 

environmental factors are influencing 

this behavior? Does it vary by time of day 

or week, perhaps reflecting the kind of 

drivers who are speeding?

Bahar, G., M. 

Masliah, C. Mollett, 

and B. Persaud, 

Integrated Safety 

Management 

Process, National 

Cooperative 

Highway Research 

Program, Report 

501, Transportation 

Research Board 

of the National 

Academies, 

Washington, D.C., 

2003

57
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-45

enforcement strategies), 

perform a time-of-day by 

day-of-the-week analysis. 

As an example, alcoholrelated crashes will likely be 

over-represented in the early 

morning and on weekend 

days. A logical approach is to 

perform a cross tabulation of 

time-of day by day-of-theweek to determine the best 

times and days for driving 

under the influence (DUI)

selective enforcement. The 

goal of the procedure at 

this point is to determine 

additional details (who, what, 

where, when, and how) for 

those crash types identified

by the analyses performed to 

this point. 

J If the over-represented 

variable is not constant 

over all crash severities, 

cross tabulate the variable 

by severity (e.g., nighttime, 

rural, and older-driver 

crashes tend to be more 

severe). 

J If the activities can be 

targeted to geographic 

location, age group, 

gender, race, or any other 

demographic factor within 

the crash records, consider 

these variables for cross 

tabulation with other overrepresented variables.

3. CONSIDER creating subsets 

of the data for additional 

comparisons where activities 

are to be targeted to a particular 

subgroup of the population. For 

example, insight into a graduated 

driver’s license strategy can be 

obtained by comparing 16-yearold causal driver crashes against 

17- to 20-year-old causal 

driver crashes. As another 

example, insight into youth 

alcohol enforcement activities 

can be attained by comparing 

alcohol-related crashes of 16- 

to 20-year-old causal drivers 

against alcohol-related crashes 

of their 21-year-old and older 

counterparts. Each of these 

types of comparisons can 

show differences between the 

respective subpopulations. 

4. USE the results of each analysis 

to determine what further 

information is needed before 

the best decision can be made, 

and repeat the analysis with the 

additional information. 

5. PERSIST and maintain a thread 

of evidence until the information 

available has been exhausted. 

If the information generated 

indicates a significant factor,

create further subsets of the data 

(e.g., youth-pedestrian crashes), 

and repeat the entire analysis. 

6. REJECT any strategies and 

activities at this point that 

the data clearly show to be 

counterproductive (i.e., activities 

that will consume resources 

that could be better applied 

elsewhere). Maintain a list of 

all potential strategies and 

corresponding activities that will 

be subjected to further analysis 

in the optimization procedure.58

Many system-level safety strategies 

focus on behaviors of drivers 

and other road users. Resources 

like Countermeasures That Work 

provide a useful listing of potential 

safety strategies for system-level 

safety management.59 The excerpt 

from Countermeasures That Work in 

Goodwin, A., 

Thomas, L., 

Kirley, B., Hall, W., 

O’Brien, N., & Hill, 

K. Countermeasures 

That Work: A 

Highway Safety 

Countermeasure 

Guide for State 

Highway Safety 

Offices, Eighth 

edition, National 

Highway 

Traffic Safety 

Administration, 

Report No. DOT 

HS 812 202, 

Washington, DC, 

2015.

Bahar, G., M. 

Masliah, C. Mollett, 

and B. Persaud, 

Integrated Safety 

Management 

Process, National 

Cooperative 

Highway Research 

Program, Report 

501, Transportation 

Research Board 

of the National 

Academies, 

Washington, D.C., 

2003

59

58
4-46 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

1. Laws

† When enforced and obeyed

2. Enforcement

† Can be covered by income from citations

†† For aggressive driving, but use of short-term, high-visibility enforcement campaigns for 

speeding is more widespread

3. Penalties and Adjudication

4. Communications and Outreach

Effectiveness:

 Demonstrated to be effective by several 

high-quality evaluations with consistent results

 Demonstrated to be effective in certain situations

 Likely to be effective based on balance of evidence 

from high-quality evaluations or other sources

 Effectiveness still undetermined; different methods of 

implementing this countermeasure produce different results

 Limited or no high-quality evaluation evidence

COUNTERMEASURE EFFECTIVENESS COST USE TIM

1.1 Speed limits  $ High Short

1.2 Aggressive driving laws  $ Low Shor

COUNTERMEASURE EFFECTIVENESS COST USE TIM

3.1 Penalty types and levels  Varies High Low

3.2 Diversion and plea agreements  Varies Unknown Varies

COUNTERMEASURE EFFECTIVENESS COST USE TIM

4.1 Public Information 

 supporting enforcement  Varies Medium Medium

COUNTERMEASURE EFFECTIVENESS COST USE TIM

2.1 Automated enforcement  $$$ Medium Mediu

2.2 High-visibility enforcement  $$$ Low Medium

2.3 Other enforcement methods  Varies Unknown Varies

†

†

††

FIGURE 4-10. Potential Safety Strategies to Address Speeding and Aggressive Driving
ROAD SAFETY FUNDAMENTALS UNIT 4: SOLVING SAFETY PROBLEMS 4-47

Figure 4-10 gives a list of potential 

strategies for addressing speedingrelated crashes, from either 

laws, enforcement, penalties and 

adjudication, or communications 

and outreach. The list also includes 

an indication of the effectiveness, 

cost, current usage, and time of each 

strategy, which are all important 

considerations when selecting safety 

strategies to implement.

If the agency identifies safety 

problems from a systemic analysis, 

the potential safety strategies 

should address the types of crashes 

that were related to the roadway 

characteristic risk factors. These 

strategies may often be engineering 

improvements related to the 

risk factors. For example, if an 

examination of crash trends may 

highlight run-off-road crashes, 

and a systemic analysis would 

identify the type(s) of road on which 

run-off-road crashes are likely to 

occur. Table 4-12 shows a list of 

potential safety strategies that could 

be implemented for engineering 

treatments for a run-off-road crash 

problem. In a systemic approach, 

these engineering treatments would 

be implemented across some or all 

roads meeting the risk factors that 

increase the likelihood of run-offroad crashes.

Example of system-level 

safety strategies in 

state highway safety plans

SHSPs provide many good examples 

of system-level strategies that 

address safety problems identified 

through analysis of crash and other 

safety data. The previous section 

showed how Ohio and Florida 

had identified safety priorities on 

older drivers and motorcyclists, 

respectively. The SHSPs from these 

States also demonstrated the types 

of safety strategies each State 

intended to pursue to combat the 

safety problems in these areas.

Selecting and 

implementing strategies

A transportation agency must 

determine which of the potential 

strategies they will implement 

to address the identified safety 

problems. Since system-level safety 

solutions can involve broad changes 

to policies, design practices, or 

jurisdiction-wide road user behavior, 

there are different issues to consider 

compared to implementing a 

safety countermeasure at a specific 

Florida’s strategies for 

motorcyclist safety

After identifying motorcyclist safety as 

an emphasis area in their 2012 SHSP, 

Florida identified a list of strategies to 

address motorcyclist safety. Example 

strategies include:

J Promote personal protective 

gear and its value in reducing 

motorcyclist injury levels and 

increasing rider conspicuity

J Promote adequate rider training 

and preparation to new and 

experienced motorcycle riders 

by qualified instructors at Stateapproved training centers

J Incorporate motorcycle-friendly 

policies and practices into roadway 

design, traffic control, construction, 

operation, and maintenance

J Develop and implement 

communications strategies that 

target high-risk populations and 

improve public awareness of 

motorcycle crash problems and 

programs.
4-48 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

OBJECTIVES COUNTERMEASUR

RELATIVE

COST TO 

IMPLEMENT 

AND OPERA

EFFECTIVE- NESS

15.1 A: KEEP

VEHICLES 

FROM 

ENCROACHING

ON THE 

ROADSID

15.1A1: Install shoulder 

rumble strips Low Tried

15.1 A2: Install edgelines 

“profile marking”, edgeline 

rumble strips or modified 

shoulder rumble strips on 

section with narrow or no 

paved shoulders

Low Experimental

15.1 A5: Provide improved 

highway geometry for 

horizontal curves

High Proven

15.1 A6: Provide enhanced 

pavement markings Low Tried

15.1 A7: Provide skidresistance pavement surfaces Moderate Proven

15.1 B:

MINIMIZE THE 

LIKELIHOOD 

OF CRASHIN

INTO AN

OBJECT OR

OVERTURNING 

IF THE VEHICLE 

TRAVELS OFF

THE SHOULDER

15.1 B1: Design safer slopes 

and ditches to prevent 

rollovers

Moderate Proven

15.1 B2: Remove/relocate 

objects in hazardous locations

Moderate 

to High Proven

15.1 C: REDUCE 

THE SEVERITY 

OF THE CRAS

15.1 C1: Improve design of 

roadside hardware

Moderate 

to High Tried

15.1 C2: Improve design and 

application of barrier and 

attenuation systems

Moderate 

to High Tried

TABLE 4-12. Potential Safety Strategies for Run-Off-Road Crashes (Source: NCHRP 500, Volume 6)
ROAD SAFETY FUNDAMENTALS UNIT 

4: SOLVING SAFETY PROBLEMS

4-49

location. Many more people will 

be affected by the system-level 

changes. This carries great promise 

in that safety might be improved 

across an entire system, but it also 

carries unique challenges.

Agencies will need to consider the 

following questions when selecting 

strategies to implement: J Safety effectiveness – How 

likely will it address the safety 

problem?

J Public acceptance – How will 

the strategy be accepted by the 

public? What kind of marketing 

will be needed to communicate 

the intent and benefit of the 

strategy?

J Stakeholders and partners – 

Which parties will need to be 

involved in implementing the 

strategy?

J Cost efficiency – What kind of 

return on the dollar would be 

expected?

J Time – How long will it take to 

implement the strategy?

Communication is critically 

important for system-level safety 

strategies. Both the general public 

and road users affected by the 

strategy must understand the 

benefits. Other public agencies 

may need to integrate their efforts 

with the proposed safety strategy. 

Administrators, lawmakers, 

and other key decision-making 

personnel must understand how 

the strategy will improve road 

safety for their constituency and 

bring an overall financial benefit. 

Unit 5 provides more discussion on 

communication, marketing, and 

outreach for agencies who seek to 

implement system-level safety 

strategies.

Evaluating a system-level strategy 

(e.g., program or intervention) to 

determine its effectiveness is a 

critical but often overlooked step. 

The transportation agency in charge 

should evaluate the effect of the 

safety strategy using good quality 

data; ideally the same type of data 

that was used to identify the safety 

problem initially. If a program or 

intervention is not effective, the 

overseeing agency should consider 

why this might be the case. Can the 

program be improved, or should 

other approaches be considered 

instead? If successful, how can the 

intervention be institutionalized 

to ensure long term support (and 

therefore lasting change)? Finally, 

it is important to remember that 

success or failure in one location 

does not guarantee the same results 

at a different location.

Ohio’s strategies for 

older driver safety

Ohio identified three strategies to 

address the older driver emphasis area 

in their 2014 SHSP: J Coordinate older driver messages 

developed by multi-agency 

communication committee.

J Create a comprehensive and 

coordinated outreach effort that 

educates older drivers and their 

caregivers on driving risks and 

remedies.

J Encourage roadway design and 

engineering measures that reduce 

the risks of traffic crashes for older 

drivers.
4-50 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

Example of System-Level 

Safety Management

The following provides an 

example of using system-level 

safety management to address a 

specific problem. This example 

demonstrates the three general 

components of safety management 

presented in this unit.

1. Identify the safety problem.

County A noticed a large number 

of crashes involving 16-17 year old 

drivers occurring weekdays between 

11:00am and 1:00pm. Neighboring 

counties have not experienced this 

problem. County officials coordinate 

with school district staff to tackle 

this issue.

In exploring the problem, the 

officials discover that County A is 

the only jurisdiction that has an 

open campus lunch policy allowing 

students to leave school during 

their lunch period. Allowing teens 

to leave campus during lunch 

means there are many young, 

inexperienced drivers on the roads at 

the same time. They may be carrying 

additional passengers which 

research has established leads to 

an increased risk of a fatal crash.60,61 

The brief lunch period also results in 

pressure to get back in time for the 

next class. Combined, these factors 

lead to a risky driving situation and 

an increased risk of crashing.

2. Develop potential safety strategies.

In this situation, an informational 

approach that simply tells teenagers 

about the problem would likely 

not make a difference. Teens are 

not crashing because they lack 

information about the importance of 

safe driving or the consequences of 

unsafe driving. Teens are crashing 

largely because they lack the driving 

experience that equips most drivers 

to intuitively/near instantaneously 

do the things necessary to avoid 

crashing. Because of this, changing 

the environment is more likely 

to be effective. 

The officials recognize that 

eliminating the policy that allows 

students to leave campus during 

lunch would lead to a reduction in 

crashes during this time. This policy 

would eliminate exposure to the 

risky driving situation and reduce 

the potential for crashes.

3. Select and implement strategies.

The school districts accordingly 

eliminate the policy allowing 

students to leave campus during 

lunch. They recognize that this 

policy change should be evaluated 

to determine its safety effect. Crash 

data would be needed to examine 

whether the closed school lunch 

policy has an effect on weekday 

crashes between 11:00am and 

1:00pm. However, it will take many 

years to accumulate enough data 

for this evaluation. In this example, 

there is a proxy measure that can 

be used in the interim. A before and 

after observational survey with an 

appropriate control could quantify 

the number of students leaving 

campus during lunch before and 

after the change. In this case the 

officials know that the proxy measure 

(reduced driving from 11:00am to 1:00 

pm) is a guaranteed indicator of crash 

reduction for this specific problem. 

However, it is not often the case that 

proxy measures are so closely aligned 

to the outcome of interest.

Chen, L., Baker, S.P., 

Braver, E.R., & Li, 

G. (2000). Carrying 

Passengers as a Risk 

Factor for Crashes 

Fatal to 16- and 

17-Year-Old Drivers. 

Journal of the 

American Medical 

Association, 283, 

1578-1582.

61

Tefft B.C., Williams 

A.F., & Grabowski 

J.G. (2013). Teen 

driver risk in relation 

to age and number 

of passengers, 

United States, 

2007-2010. Traffic 

Injury Prevention, 

14, 283-292.

60
ROAD SAFETY FUNDAMENTALS UNIT 

4: SOLVING SAFETY PROBLEMS

4-51

Unit Summary

Solving road safety problems 

requires a comprehensive process 

to identify safety problems, 

develop potential safety strategies, 

and select and implement those 

strategies. To get the most effective 

results, this process must be based 

on solid safety data, particularly 

good quality crash data. The 

methods of undertaking the safety 

management process will depend on 

the scope of the effort. 

Safety management of individual 

sites involves a six-step process of 

screening the network for highpriority sites, diagnosing the safety 

issues at those sites, selecting 

appropriate countermeasures, 

conducting an economic appraisal 

for all options, prioritizing the 

countermeasure projects based on 

estimated costs and benefits, and 

evaluating the countermeasure 

performance afterwards. Safety 

management at a system-level 

involves identifying safety problems 

by examining crash trends or using 

a systemic approach to identifying 

high-risk road characteristics. 

State agencies who are developing 

system-wide safety strategies 

must examine the data trends 

and the road users involved. They 

must consider factors, such as how 

system-wide policies and programs 

will be accepted by the public and 

who will be the partners to involve 

in implementing the safety strategy.
4-52 UNIT 4: SOLVING SAFETY PROBLEMS ROAD SAFETY FUNDAMENTALS

J PRESENT an example road safety 

problem and compare and contrast the 

ways in which the problem could be 

addressed at a system-level vs. site-level.

J Your state has a small, rural, 

mountainous county where a large 

number of motorcycle crashes are 

happening. The crash rate per registered 

motorcycle in this county is nearly 10 

times the state average. Upon further 

investigation you learn that this county 

is a popular motorcycling tourist 

destination. People come from all over 

the country to ride the curvy mountain 

roads. In fact, the majority of people 

involved in crashes are not from that 

area at all. Clusters of crashes occur 

on certain curves. What are some 

approaches that could be used to reduce 

crashes in this county? How could these 

approaches be evaluated? In particular, 

DETAIL how you would apply the three 

major components described in this 

unit:

J Identify the safety problem

J Develop potential safety strategies

J Selecting and implement strategies

When you work through this process, 

recall the discussion of human behavior 

from Unit 2. What are possible behaviors 

leading to the safety problem? What 

other factors could be influencing this 

behavior? How does this affect your 

identification and selection of potential 

safety strategies?

J If possible, OBTAIN three to five years of 

crash data for an intersection or section 

of road in your area. You will likely need 

to contact the controlling agency – the 

State DOT, county, or city. Describe how 

you would apply the steps in Chapter 

11 on site-level safety management to 

this location (the network screening 

step would not apply since this location 

is already identified). Consider safety 

strategies across a range of disciplines 

(e.g., engineering, law enforcement, 

public communication and education, 

etc.).

J This exercise should be conducting 

using the Excel spreadsheet that 

accompanies this book. The goal of this 

exercise is to USE selected performance 

metrics to create a ranked list of sites 

for further investigation as part of a 

network screening effort. The Excel 

spreadsheet includes nearly 1,400 

intersections, or sites. Each site has a 

unique ID number, traffic volume data, 

and other information about its location 

and characteristics. Three performance 

metrics have been calculated for each 

site. These have been calculated using 

five years of data (2010-2014) and one 

year of data (2014), resulting in a total of 

six performance metrics per site. Your 

assignment is to rank the sites using 

these various performance metrics and 

document the results. Document the 

twenty highest priority sites based on 

each method. Use the results to answer 

the following questions:

J What were some of the sites 

that routinely ranked in the top 

twenty? What were some of their 

characteristics (volumes, number of 

lanes, stop/signal control)?

J Were there any sites that were 

only occasionally present in the 

top twenty? What were some 

characteristics of these sites?

EXERCISES

FHWA, Office of Safety

Felix H. Delgado, P.E.

Felix.Delgado@dot.gov

FHWA-SA-18-003

Road Safety Fundamentals is available for free at: 

https://rspcb.safety.fhwa.dot.gov/rsf/