Reliability of Safety 

Management Methods 

Diagnosis

FHWA-SA-16-038 September 2016

http://safety.fhwa.dot.gov 
 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

NOTICE 

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

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

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

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

objective of the document. 

QUALITY ASSURANCE STATEMENT 

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

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

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

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

and processes to ensure continuous quality improvement. 

ii 
1. Report No.

FHWA-SA-16-038

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

 

4. Title and Subtitle

Reliability of Safety Management Methods: Diagnosis

5. Report Date

September 2016 

6. Performing Organization Code

7. Author(s)

Raghavan Srinivasan, Geni Bahar, and Frank Gross

8. Performing Organization Report No.

9. Performing Organization Name and Address

VHB

8300 Boone Boulevard

Suite 700

Vienna, VA 22182

10. Work Unit No.

11. Contract or Grant No.

DTFH61-10-D-00022-T-13005

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Final Report, March 2015 – September 2016

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

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

16. Abstract

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

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

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

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

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

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

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

The Reliability of Safety Management Methods: Diagnosis information guide describes various methods and the latest tools to support 

diagnosis. The target audience includes data analysts, project managers, and program managers involved in projects that impact highway 

safety. The objectives of this information guide are to 1) raise awareness of more reliable methods, and 2) demonstrate through 

examples the value of more reliable methods in diagnosis. This information guide compares more reliable diagnosis methods to 

traditional methods which are more susceptible to bias and may result in less effective decisions. Readers will understand the value of 

and be prepared to select more reliable methods in diagnosis. 

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

diagnosis. The second section provides an overview of the diagnosis process and various methods to enhance the process. The third 

section demonstrates the value of more reliable methods for diagnosing safety issues. Examples highlight the benefits of more reliable 

methods, which will likely lead to better-informed decisions, more optimal use of funds, and the implementation of more effective 

treatments. The fourth section summarizes the data requirements for diagnosis . The final section describes available tools and resources 

to support diagnosis. 

17. Key Words:

Roadway, Safety, Data, 

Contributing, Predisposing, Factors

Analysis, Diagnosis, 

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

22

22. Price

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

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

TECHNICAL DOCUMENTATION PAGE



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RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

ACKNOWLEDGMENTS

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

Technical Working Group for this effort. 

Technical Working Group

Stuart Thompson, FHWA Office of Safety 

Ray Krammes, FHWA Office of Safety 

Yanira Rivera, FHWA Office of Safety 

Karen Scurry, FHWA Office of Safety 

Craig Thor, FHWA Office of Safety Research and Development 

iv
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

TABLE OF CONTENTS



1. Introduction to Diagnosis......................................................................................................................1
 

2. Overview of Diagnosis ...........................................................................................................................3
 

Step 1: Safety Data Review ........................................................................................................................4
 

Step 2: Assess Supporting Documentation ............................................................................................4
 

Step 3: Assess Field Conditions ................................................................................................................4
 

Methods to Enhance Diagnosis.................................................................................................................4
 

Time Trends .............................................................................................................................................5
 

Test of Proportions.................................................................................................................................5
 

The Haddon Matrix.................................................................................................................................5
 

3. Demonstrating The Value of More Reliable Methods.....................................................................6
 

Time Trends in Diagnosis...........................................................................................................................6
 

Test of Proportions in Diagnosis..............................................................................................................9
 

Example 1: Left-Turn Crashes at Urban Signalized Intersection ............................................... 10
 

Example 2: Right Angle Crashes at Urban Signalized Intersection............................................ 10
 

Example 3: Rear-end Crashes at Rural Unsignalized Intersection............................................. 10
 

Example 4: Rear-End Crashes at Rural Signalized Intersection.................................................. 11
 

Example 5: Multiple Crash Types at Urban Signalized Intersection.......................................... 12
 

The Haddon Matrix .................................................................................................................................. 14
 

Example 1: General Application of the Haddon Matrix............................................................... 15
 

Example 2: Site-Specific Application of the Haddon Matrix........................................................ 16
 

4. Data Requirements for Diagnosis..................................................................................................... 19
 

5. Tools and Resources for Diagnosis ................................................................................................... 20
 

Using the Roadway Safety Data and Analysis Toolbox .................................................................... 20
 

6. References.............................................................................................................................................. 22
 

v 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

LIST OF TABLES



Table 1. Sample data for time trend methodology. ..................................................................................7
 

Table 2. Results based on sample data for time trend methodology. ..................................................7
 

Table 3. Comparison of statewide and site-specific proportions for example 4. ........................... 12
 

Table 4. Comparison of statewide and site-specific proportions for example 5. ........................... 13
 

Table 5. Haddon Matrix. .............................................................................................................................. 14
 

Table 6. Example 1 Haddon Matrix for urban crashes.......................................................................... 15
 

Table 7. Example 2 Haddon Matrix for right-angle and left-turn crashes......................................... 16
 

LIST OF FIGURES



Figure 1. Schematic. Roadway safety management process....................................................................2
 

Figure 2. Equation. Computing change in crashes over time..................................................................7
 

Figure 3. Equation. Computing change in crashes over time..................................................................7
 

Figure 4. Chart. Sample plot of change in crashes over time against period. .....................................8
 

Figure 5. Equation. Computing crash probability. .....................................................................................9
 

Figure 6. Equation. Computing crash probability for example. ..............................................................9
 

Figure 7. Equation. Computing crash probability for example 4......................................................... 11
 

Figure 8. Equation. Computing other multiple vehicle crash probability for example 5. .............. 12
 

Figure 9. Equation. Computing rear-end crash probability for example 5. ...................................... 12
 

Figure 10. Equation. Computing angle crash probability for example 5............................................ 13
 

Figure 11. Diagram. Study intersection for example 2.......................................................................... 17
 

Figure 12. Screenshot. Roadway Safety Data and Analysis Toolbox. ................................................ 20
 

Figure 13. Screenshot. Advanced search feature.................................................................................... 21
 

Figure 14. Screenshot. Filter options from advanced search page. .................................................... 21
 

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RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

AASHTO American Association of State Highway and Transportation Officials 

EMS Emergency medical services 

FHWA Federal Highway Administration 

HSIP Highway Safety Improvement Program 

NCHRP National Cooperative Highway Research Program 

RSA Road safety audit 

LIST OF ACRONYMS



vii 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

PREFACE 

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

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

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

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

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

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

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

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

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

reliable) methods.

The Reliability of Safety Management Methods: Diagnosis information guide describes various 

methods and the latest tools to support diagnosis. The target audience includes data analysts, 

project managers, and program managers involved in projects that impact highway safety. The 

objectives of this information guide are to 1) raise awareness of more reliable methods, and 2) 

demonstrate through examples the value of more reliable methods in diagnosis. This 

information guide compares more reliable diagnosis methods to traditional methods which are 

more susceptible to bias and may result in less effective decisions. Readers will understand the 

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

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

roadway safety management process and diagnosis. The second section provides an overview of 

the diagnosis process and various methods to enhance the process. The third section 

demonstrates the value of more reliable methods for diagnosing safety issues. Examples 

highlight the benefits of more reliable methods, which will likely lead to better-informed 

decisions, more optimal use of funds, and the implementation of more effective treatments. The 

fourth section summarizes the data requirements for diagnosis. The final section describes 

available tools and resources to support diagnosis. 

viii 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

1. INTRODUCTION TO DIAGNOSIS

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

in the Highway Safety Manual (HSM).

(1) The objectives of this process are as follows. 

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

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

Safety Management Methods: Network Screening for further discussion of network

screening and related methods.(2) 

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

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

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

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

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

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

future crashes. Refer to the next sections of this information guide for further

information and considerations related to diagnosis.

3. Countermeasure Selection: Develop appropriate countermeasures to target crash

contributing factors and reduce crash frequency and severity at identified locations. The

countermeasures should directly target the crash contributing factors, and may include

engineering, education, enforcement, and EMS-related measures (i.e., the 4E approach).

Refer to Reliability of Safety Management Methods: Countermeasure Selection for

further information and considerations related to countermeasure selection.(3) 

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

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

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

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

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

Highway Safety Manual for further information and considerations related to economic

appraisal.(1) 

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

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

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

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

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

for further information and considerations related to project prioritization.(1) 

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

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

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

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

methods.(4) 

1 
 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

2 

Figure 1. Schematic. Roadway safety management process. 

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

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

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

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

Diagnosis is the second step in the roadway safety management process. Based on the results 

of step 1 (network screening), analysts perform more detailed investigations of specific sites. 

The objective of diagnosis is to understand the crash patterns and identify the underlying 

factors contributing to crashes at sites identified from network screening. The diagnosis results 

feed step 3 (countermeasure selection). Given a list of underlying contributing factors, analysts 

can develop more effective countermeasures to directly target the site-specific issues. Note it is 

critical to diagnose the underlying safety issues before selecting countermeasures. Otherwise, 

agencies may misallocate resources if the selected countermeasure does not properly or 

effectively target the underlying issues. 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

2. OVERVIEW OF DIAGNOSIS

Diagnosis is the second step in the roadway safety management process, following network 

screening. Diagnosis is the process of further investigating the sites or high-level safety issues 

identified from network screening. The intent of diagnosis is to understand the crash patterns 

and identify the underlying factors contributing to crashes at specific sites. Diagnosis can also 

help to identify potential safety issues that have not yet manifested in crashes. 

Diagnosis involves a review of site-specific crash history, traffic operations, and general site 

conditions. This includes a desktop data analysis as well as a field visit to observe road user 

behaviors and site conditions not observable from the office. It is useful to conduct a field 

review under multiple conditions (e.g., day and night, peak and off-peak travel times, dry and 

wet) to investigate potential crash contributing factors identified from the crash history. It is 

also useful to engage a multidisciplinary team to perform the diagnosis and subsequent 

countermeasure selection. Note the following benefits of using a multidisciplinary team to 

diagnose issues and select countermeasures: 

• Combined team experience: As opposed to a single traffic engineer or highway

designer diagnosing a given site, a multidisciplinary team provides their combined

experience. Depending on the focus of the investigation, it may be useful to include

team members with experience in highway design, traffic operations, transportation

planning, human factors, pedestrian and bicycle safety, law enforcement, and other

specialized areas as needed (e.g., commercial motor vehicles).

• Limited influence of personal bias: Personal professional experience and judgment

are critical for all aspects of safety management, including the diagnosis of safety issues.

Based on professional experience, a person can identify potential underlying safety issues

and select appropriate countermeasures to address or mitigate each issue. While

experience is important, an individual is limited to their personal professional

experience, and the results of a single-person diagnosis are susceptible to personal bias

and experience that may be relatively limited. A multidisciplinary team provides the

professional experience of multiple people with different backgrounds and perspectives.

• Multidisciplinary issues and countermeasures: Crash contributing factors include

issues related to the person, the roadway, and the vehicle. Further, there are often

multiple contributing factors related to a given crash. As such, it is critical to identify the

various contributing factors and develop targeted countermeasures which may include

engineering, education, enforcement, and emergency medical services (EMS) efforts.

The HSM suggests the following three-step process for conducting site diagnosis.(1) For further 

discussion of the diagnosis process, including detailed prompts, refer to the HSM and FHWA’s 

Road Safety Audit Guidelines.

(5) 

1. Safety data review.

2. Assess supporting documentation.

3. Assess field conditions.

3 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

STEP 1: SAFETY DATA REVIEW 

The first step involves a detailed review of data from 

police crash reports to identify crash patterns and 

contributing factors. This typically involves reviewing a 

summary of crash types, crash severity, sequence of 

events, and contributing circumstances. Different 

tools such collision diagrams, tabular summaries, bar 

charts, and pie charts are useful for displaying descriptive statistics. In addition to descriptive 

statistics, analysts can use statistical methods to identify underlying safety issues based on time 

trends and crash proportions. These methods are detailed in the next section. 

A collision diagram presents 

crashes as symbols to represent 

the crash type and severity at the 

approximate location within the 

site of interest. Collision diagrams 

help to identify crash patterns. 

STEP 2: ASSESS SUPPORTING 

DOCUMENTATION 

This second step involves a review of documented 

information about the site along with interviews of 

local stakeholders (e.g., transportation professionals, 

community groups, local board members) to obtain 

additional perspectives on the safety data review from 

step 1. Supporting documentation may include traffic 

volumes, condition diagrams, construction plans and 

design criteria, photos and maintenance logs, weather 

patterns, and recent traffic studies. 

Condition diagrams present an 

aerial view of site conditions such 

as roadway characteristics (e.g., 

number of lanes, presence of 

medians, traffic control devices, 

pedestrian and bicycle facilities), 

surrounding land uses, and 

pavement conditions. Analysts can 

overlay collision diagrams on 

condition diagrams to diagnose 

the crash patterns further. 

STEP 3: ASSESS FIELD CONDITIONS 

Field observations are useful and necessary for supplementing the information gathered in steps 

1 and 2, and can help understand the behavior of drivers, pedestrians, and bicyclists. The first 

stage of the field investigation should be an on-site examination of the road user’s experience. 

An effective field assessment involves traveling the site at different times of the day using 

different modes of transportation. The field review should include observations of traffic 

operations such as turning movements, conflicts, and operating speeds, as well as 

accommodation for pedestrians, cyclists, and special road users such as senior pedestrians. In 

addition, the field review should include observations of highway and roadside design to 

determine whether the design and location of roadway and roadside features are consistent 

with road user expectations. The roadside review can also help to determine if roadside 

recovery zones are clear and traversable. 

METHODS TO ENHANCE DIAGNOSIS 

The remainder of this document focuses on more reliable diagnosis methods compared to a 

traditional engineering review based on limited analysis of police crash reports and collision 

diagrams. Specifically, analysts can assess crash trends over time and use tests of proportions 

and the Haddon Matrix to identify contributing factors and develop targeted countermeasures. 

The following is a brief description of each method, and the following section, Demonstrating the 

Value of More Reliable Methods, provides further discussion and examples of each. 

4 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

Time Trends 

Analysts can use time trends to identify sites that experience a gradual or sudden increase in 

mean collision frequency. There are statistical tests to help determine if the changes are 

statistically significant.(6,7) 

Test of Proportions 

Analysts can use a test of proportions to identify locations that have a high proportion of a 

specific collision type relative to the average or some threshold proportion for similar facility 

types.(8) Kononov noted 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.(9) While 

the method is identical for different facility types, analysts should only include similar sites in a 

single analysis because collision patterns will naturally differ among facility types. For example, 

the collision patterns are different for stop-controlled intersections, signalized intersections, 

and two-lane roadway segments. As such, analysts should apply the method to the three 

individual facility types, and separately for urban and rural areas. 

The Haddon Matrix 

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

to highway safety in both diagnosis and countermeasure selection.(10) For diagnosis, the Haddon 

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

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

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

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

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

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

and targeted countermeasures. Chapter 6 (page 6.2) of the HSM provides further discussion of 

the Haddon Matrix.(1) Refer to the Reliability of Safety Management Methods: Countermeasure 

Selection for further details related to the use of the Haddon Matrix in identifying targeted 

countermeasures. 

The following section describes the use of time trends, test of proportions, and the Haddon 

Matrix in diagnosis. Examples illustrate the benefit of using these methods to enhance diagnosis. 

Conducting analytical tests will help to focus on crash types and patterns of concern. Employing 

a comprehensive, multidisciplinary diagnosis approach will help to understand the underlying 

crash contributing factors. This focused approach will generally lead to the selection of 

targeted, effective, and defensible countermeasures to mitigate specific safety issues. 

5 
 

 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

3. DEMONSTRATING THE VALUE OF MORE RELIABLE METHODS

This section demonstrates the value of applying more reliable diagnosis methods to focus the 

detailed investigation on trends and crash types that warrant further investigation and to 

identify underlying contributing factors for the focus crash types. Empirical examples highlight 

the benefits of more reliable methods, which will generally lead to more reliable results and 

conclusions. Note the examples illustrate the general comparative results of the methods. 

Different scenarios and different data will produce different results. 

TIME TRENDS IN DIAGNOSIS 

It is useful to examine crash-related time trends as part of diagnosis. If there is a consistent 

increase in crashes over time, this can indicate an emerging or intensifying issue, which may 

provide further justification to prioritize the location for improvement. If there is a sudden 

increase in crashes, this can indicate the onset of a particular issue such as increased turning 

volumes due to a new development. This can also indicate an anomalous event such as a rare 

weather event during which several crashes occurred. In the case of a rare event, it may be 

appropriate to forego the diagnosis and implementation of safety countermeasures as other 

locations may provide better opportunities to improve safety. 

In some cases, there is a long lag (potentially several years) between project programming and 

project implementation. During the period between programming and implementation, it is 

useful to revisit the crash history of the study location to evaluate time trends. Evaluating time 

trends can help to determine if the safety performance of a site is further deteriorating over 

time or remaining relatively constant. In some cases, prior safety issues may self-correct over 

time or other projects may mitigate the issues. In these cases, it may be appropriate to forego 

or postpone further improvements as other locations may provide better opportunities to 

improve safety. 

Following is a brief overview of the method adapted from a network screening whitepaper to 

examine time trends in diagnosis.

(11) Hauer provides statistical tests to determine the type and 

rate of change.

(6) These tests are beyond the scope of this guide because they require software. 

Interested readers should refer to the referenced work for details. These tests are also 

available in AASHTOWare Safety AnalystTM. 

The sample data in Table 1 and subsequent equation in Figure 2 illustrate the first component 

of the methodology, prior to the use of software to perform statistical tests. Table 1 presents 

five years of crash data on a section of highway. 

6 
 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

Table 1. Sample data for time trend methodology. 

Time Period (Ti) Recorded crashes (xi) 

1 7 

2 5 

3 10 

4 15 

5 13 

The table presents time periods (Ti

) sequentially from 1 to L. The number of reported crashes 

in each time period is denoted by x1, x2 ... xL. For the end of any time period (T), where T is 

less than L (1 ≤ T < L), the equation in Figure 2 estimates the difference in average crashes 

between the after period and before period (Δ(T)). 

Figure 2. Equation. Computing change in crashes over time. 

Figure 3 shows a sample calculation using the equation in Figure 2 for the second time period 

(T = 2) from Table 1. 

Figure 3. Equation. Computing change in crashes over time. 

Table 2 presents the results for the sample road section, applying the equation in Figure 2 to 

the sample data in Table 1. 

Table 2. Results based on sample data for time trend methodology. 

Period 

(Ti) 

Recorded 

crashes (xi) 

Average 

crashes before 

Average 

crashes after 

Change in average 

crashes Δ(T) 

1 7 7.0 10.8 3.8 

2 5 6.0 12.7 6.7 

3 10 7.3 14.0 6.7 

4 15 9.3 13.0 3.8 

Hauer suggests the following three step process to determine whether there is a change in 

safety performance over time at a particular site.(6) 

7 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

1. Screening: The first step is screening to determine whether most changes (Δ(T)) are

positive or negative for a time series of crash counts. If the changes are consistently

positive or negative, then there is likely a steady time trend. In Table 2, the changes are

consistently positive, indicating an increasing and likely steady time trend. If there is a

large change in a given period, then there may be a sudden change.

2. Test for steady change: The next step is to plot the changes (Δ(T)) from the last

column of Table 2 against the time periods (Ti) from the first column in Table 2. Fitting a

linear trend line through the data will help to determine the intercept (a) and slope (b).

The sign of the slope (b) indicates the direction of change; a positive slope indicates an

increasing trend and a negative slope indicates a decreasing trend. The magnitude of the

slope indicates the rate of change over time; a greater slope indicates a greater rate of

change. Figure 4 presents a sample plot based on the sample results in Table 2 along

with a fitted linear regression line to estimate the intercept (a) and slope (b). The

statistical test of significance uses the intercept and slope. Refer to Hauer’s paper for

further details on the statistical test and related software program.(6) 

3. Test for sudden change: To test for a sudden change in crashes over time, there is a

need to identify the period with the largest change, and calculate a test statistic for

assessing the statistical significance. Refer to Hauer’s paper for further details on the

statistical test and related software program.(6) 

Figure 4. Chart. Sample plot of change in crashes over time against period. 

8 
 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

TEST OF PROPORTIONS IN DIAGNOSIS 

While many network screening measures identify locations with potential for safety 

improvement, they do not provide any insight into the nature of the crash occurrence. 

Heydecker and Wu initially proposed a test of proportions to identify locations that have a high 

proportion of a specific collision type relative to the average or some threshold proportion for 

similar facility types.(8) Analysts can use the test of proportions in diagnosis to identify crash 

types warranting further investigation based on the recognition of crash patterns. 

Kononov and Janson subsequently postulated that crashes are a sequence of Bernoulli trials, 

and by calculating the cumulative probability of occurrence for a particular crash type, it is 

possible to statistically test if the crash pattern is different from the norm.(12) For example, if a 

particular rural, four-legged, stop-controlled intersection has experienced ‘x’ target crashes out 

of ‘n’ total crashes, and the average proportion of target crashes for rural, four-legged, stopcontrolled intersections in the jurisdiction is ‘p’, then the probability of experiencing less than 

or equal to ‘x-1’ target crashes is computed by the equation in Figure 5. 

9 

Figure 5. Equation. Computing crash probability. 

The probability of experiencing ‘x’ or more target crashes is one minus the probability 

calculated from Figure 5. If the probability from Figure 5 is close to one, then the probability of 

experiencing ‘x’ or more target crashes is very small, indicating the crash pattern at the location 

of interest is likely very different from the norm. Hence, the target crash type warrants further 

investigation. If the probability from Figure 5 is close to zero, then the probability of 

experiencing ‘x’ or more target crashes is very small, indicating the target crash type at the 

location of interest is similar to the norm, and other crash types may warrant more attention. 

Following is a numerical example to illustrate this method. Suppose a rural, four-legged, stopcontrolled intersection has experienced six right angle crashes out of 10 total crashes, and the 

average proportion of right angle crashes for intersections of this type in the jurisdiction is 0.3. 

Figure 6 shows the computation of the probability of experiencing five or fewer right angle 

crashes. 

Figure 6. Equation. Computing crash probability for example. 

Hence, the probability of experiencing six or more right angle crashes is 1 – 0.953 = 0.047. In 

this case, there is less than a five percent chance that an intersection of this type would 

experience this proportion of right angle crashes. As such, the right angle crashes are likely 

over-represented and warrant further investigation. 
The following three examples are adapted from Kononov and Janson.(12) 

Example 1: Left-Turn Crashes at Urban Signalized Intersection 

 

An urban signalized intersection experienced 246 crashes in a five-year period, among which 97 

were left-turn crashes. Overall, left-turn crashes represent 19 percent of total crashes at urban 

signalized intersections. Applying the equation from Figure 5, the probability of observing 96 or 

fewer left-turn crashes is approximately 1.0. Therefore, the probability of observing 97 or more 

left-turn crashes is extremely small (approaching zero). Based on the test of proportions, the 

Team determined that left-turn crashes are over-represented at this site. Further investigation 

revealed the presence of a dual left-turn lane with a permitted left-turn phase. The Team also 

noted limited sight distance from the intersection approach. These characteristics contributed 

to an unusually high number of left-turn crashes, and the Team was able to develop targeted 

countermeasures to address the underlying issues. Refer to Reliability of Safety Management 

Methods: Countermeasure Selection for further information and considerations related to 

countermeasure selection.(3) 

Example 2: Right Angle Crashes at Urban Signalized Intersection 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

An urban signalized intersection experienced 112 crashes over a three-year period, among 

which 36 were right angle. Overall, right angle crashes represent 16 percent of crashes at urban 

signalized intersections in this jurisdiction. Applying the equation from Figure 5, the probability 

of observing 35 or fewer right angle crashes is approximately 1.0. Therefore, the probability of 

observing 36 or more right angle crashes is extremely small (approaching zero). Based on the 

test of proportions, the Team determined that right angle crashes are over-represented at this 

site. A field investigation revealed the obstruction of signal head visibility by foliage on one of 

the approaches. This contributed to the high number of right angle crashes, and the Team was 

able to develop targeted countermeasures to address the underlying issue. Refer to Reliability of 

Safety Management Methods: Countermeasure Selection for further information and considerations 

related to countermeasure selection.(3) 

Example 3: Rear-end Crashes at Rural Unsignalized Intersection 

A rural, stop-controlled intersection experienced 12 total crashes in the study period, among 

which nine were rear-end crashes. For this jurisdiction, rear-end crashes represent 22 percent 

of crashes at rural, stop-controlled intersections. Applying the equation from Figure 5, the 

probability of observing eight or fewer rear-end crashes is approximately 1.0. Therefore, the 

probability of observing nine or more rear-end crashes is extremely small (approaching zero). 

Based on the test of proportions, the Team determined that rear-end crashes are overrepresented at this site. A field investigation revealed a heavy left-turn movement and no left 

turn lanes on the approach where all of the rear-end crashes were reported. Given the focused 

diagnosis and identification of underlying crash contributing factors, the Team was able to 

develop targeted countermeasures to address the underlying issue. Refer to Reliability of Safety 

Management Methods: Countermeasure Selection for further information and considerations 

related to countermeasure selection.(3) 

10 
 

 

 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

These examples illustrate the application of the test of proportions to determine whether a 

given crash pattern at a particular location is different from the norm. Specifically, this method 

provides a statistically valid approach to determine which crash types and locations should be 

the focus of further investigation. This reduces the potential to misallocate resources to 

investigate and mitigate apparent safety issues that may not warrant treatment. Note the test of 

proportions is available in the network screening module in AASHTOWare Safety AnalystTM. 

The following two examples are from a comprehensive analysis of intersections in New 

Hampshire. Gross et al. performed a network screening to identify intersections with potential 

for improvement.(13) Subsequently, the study team diagnosed potential safety issues using 

collision diagrams, tests of proportions, general crash summaries, and aerial images. The two 

examples focus on the use of the test of proportions to focus the diagnosis. 

Example 4: Rear-End Crashes at Rural Signalized Intersection 

Table 3 presents crash counts and proportions by crash type for a rural signalized intersection 

as well as the statewide average crash type proportions for similar rural signalized intersections. 

At first glance, rear-end crashes represent the most prevalent crash type (25 of 32 crashes) and 

appear to be an issue based on crash count. Comparing the site-specific proportions to the 

statewide average, rear-end crashes are over-represented (78 percent of the crashes at this 

intersection compared to 54 percent statewide). Applying the equation from Figure 5, the 

probability of observing 24 or fewer rear-end crashes is approximately 1.0 as shown in Figure 7. 

Therefore, the probability of observing 25 or more rear-end crashes is extremely small 

(approaching zero). 

11 

Figure 7. Equation. Computing crash probability for example 4. 

Hence, the probability of experiencing 25 or more rear-end crashes is 1 – 0.996 = 0.004, which 

is less than 0.5 percent. Applying the equation from Figure 5, the last column of Table 3 

presents the probability for each crash type. In this case, the crash type with the largest 

proportion (rear-end crashes) is also significantly higher than the statewide average and likely 

correctable. There is a high chance of observing the actual proportion of other crash types: 91 

percent for right angle, 80 percent for left-turn, and 93 percent for other multiple-vehicle 

crashes. As such, there may be less opportunity to address right angle, left-turn, and other 

multiple-vehicle crashes. 

Further investigation provided insights into specific problems leading to rear-end crashes at this 

location. During diagnosis, the team focused on potential contributing factors to rear-end 

crashes. They observed short turn lanes and heavy traffic volumes on two approaches, which 

contribute to spillover and the potential for rear-end crashes. To target the underlying issue, 

the team suggested extending the existing turn lanes to reduce spillover. Refer to Reliability of 

Safety Management Methods: Countermeasure Selection for further information and considerations 

related to countermeasure selection.(3) 
- - 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

Table 3. Comparison of statewide and site-specific proportions for example 4. 

Crash Type Site Specific 

Crash Count 

Site Specific 

Proportion 

Statewide 

Proportion 

Chance of 

Observing 

Right angle 4 13% 20% 91% 

Fixed object 0 0% 4% --

Left-turn 2 6% 9% 80% 

Other multiple-vehicle 1 3% 8% 93% 

Pedestrian 0 0% 1% --

Rear-end 25 78% 54% 0.4% 

Sideswipe, opposite 

direction 0 0% 1% --

Sideswipe, same direction 0 0% 4% --

Total 32 100% 100% --

Example 5: Multiple Crash Types at Urban Signalized Intersection 

Table 4 presents crash counts and proportions by type for an urban signalized intersection as 

well as the statewide average crash type proportions for similar urban signalized intersections. 

At first glance, other multiple vehicle, rear-end, and angle crashes represent the most prevalent 

crash types (10, 9, and 5 of 29 total crashes) and appear to be an issue based on crash count. 

Comparing the site-specific proportions to the statewide average, other multiple vehicle 

collisions are over-represented (33 percent of the crashes at this intersection compared to 8 

percent statewide); however, rear-end and angle crashes are under-represented. Applying the 

equation from Figure 5, the probability of observing nine or fewer other multiple vehicle 

collisions is approximately 1.0 as shown in Figure 8. Hence, the probability of experiencing 10 

or more other multiple vehicle crashes is 1 – 0.9999 = 0.0001, which is 0.01 percent. 

Figure 8. Equation. Computing other multiple vehicle crash probability for example 

5. 

Applying the equation from Figure 5, the probability of observing eight or fewer rear-end 

crashes is approximately 0.05 as shown in Figure 9. Hence, the probability of experiencing nine 

or more rear-end crashes is 1 – 0.05 = 0.95, which is 95 percent. Therefore, the probability of 

observing nine or more rear-end crashes is relatively high. 

12 

Figure 9. Equation. Computing rear-end crash probability for example 5. 
 

 

 

- -

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

Applying the equation from Figure 5, the probability of observing four or fewer angle crashes is 

approximately 0.08 as shown in Figure 10. Hence, the probability of experiencing five or more 

angle crashes is 1 – 0.08 = 0.92, which is 92 percent. Therefore, the probability of observing 

five or more angle crashes is relatively high. 

Figure 10. Equation. Computing angle crash probability for example 5. 

Applying the equation from Figure 5, the last column of Table 4 presents the probability for 

each crash type. In this case, the crash type with the largest proportion (other multiple vehicle 

collisions) is also significantly higher than the statewide average and likely correctable. The 

crash types with the second and third highest proportions (rear-end and angle crashes) 

represent less potential for improvement. Specifically, the proportions of rear-end and angle 

crashes are substantially lower than the statewide average. As such, it may be difficult for an 

agency to address or mitigate rear-end and angle crashes with measures common to other 

urban signalized intersections in the State. 

Further investigation provided insights into specific problems leading to other multiple vehicle 

collisions at this location. During diagnosis, the team focused on potential contributing factors 

to other multiple vehicle crashes. They observed multiple driveways in close proximity to the 

intersection, which may contribute to other multiple vehicle crashes. To target the underlying 

issue, the team suggested access management strategies to eliminate and consolidate access 

points near the intersection. Refer to Reliability of Safety Management Methods: Countermeasure 

Selection for further information and considerations related to countermeasure selection.(3) 

Table 4. Comparison of statewide and site-specific proportions for example 5. 

Crash Type Site Specific 

Crash Count 

Site Specific 

Proportion 

Statewide 

Proportion 

Chance of 

Observing 

Right angle 5 17% 27% 92% 

Bicyclist 0 0% 1% --

Fixed object 3 10% 3% 6% 

Left-turn 0 0% 9% --

Other multiple-vehicle 10 33% 8% 0.01% 

Pedestrian 0 0% 1% --

Rear-end 9 30% 44% 95% 

Sideswipe, opposite 

direction 0 0% 2% --

Sideswipe, same direction 2 7% 5% 43% 

Total 29 100% 100% --

13 
 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

THE HADDON MATRIX 

The Haddon Matrix is a comprehensive tool to identify 

suitable and effective countermeasures to mitigate a 

substantiated safety issue. During step 2 of the safety 

management process (diagnosis), the multidisciplinary 

analysis team (namely, the Team) reviews past crash 

data in conjunction with attributes of the road users, 

adjacent land use, geometry, and traffic operations of 

the study location. This includes the identification of 

target crash types and crash contributing factors, which 

provides the foundation for the identification and 

selection of appropriate countermeasures. 

The Haddon Matrix supports a comprehensive approach to understanding crash contributing 

factors as shown in Table 5. The Team would complete the Haddon Matrix for each target 

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

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

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

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

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

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

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

Using the Haddon Matrix will 

help you identify underlying crash 

contributing factors and select 

more targeted countermeasures. 

You may opt for less effective 

countermeasures if you do not 

consider the site-specific and 

multiple factors that contributed 

to past crashes or may 

contribute to future crashes. 

Table 5. Haddon Matrix. 

Period Human 

Factors 

Vehicle 

Factors 

Roadway 

Factors 

Before (causes of hazardous situation) 

During (causes of crash severity) 

After (factors of crash outcome) 

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

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

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

14
 
Example 1: General Application of the Haddon Matrix 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

The following is an example application of the Haddon Matrix adapted from the Highway Safety 

Improvement Program (HSIP) Manual.

(14) 

The Haddon Matrix in Table 6 represents a set of crashes in an urban area. The upper-left cell 

identifies potential human contributing factors prior to the crash. As shown in the example 

table, poor vision, slow reaction time, alcohol consumption, speeding, and risk taking are all 

potential human contributing factors that exist prior to the crash. Given these factors, the 

Team can develop targeted mitigation measures to address human factors and behavior. 

Similarly, the upper-right cell identifies potential roadway contributing factors prior to the 

crash. Examples include narrow shoulders, limited pavement markings, and poorly-timed signals. 

Given these factors, the Team can develop targeted engineering measures to reduce the 

likelihood of a crash. In the periods during and after the crash, there are opportunities to 

reduce the crash severity. The matrix in its entirety presents a range of potential issues that 

agencies can address through a variety of countermeasures, including education, enforcement, 

engineering, and emergency response solutions (the 4Es of safety). 

Table 6. Example 1 Haddon Matrix for urban crashes. 

Period Human 

Factors 

Vehicle 

Factors 

Roadway 

Factors 

Before (causes of 

hazardous situation) 

Poor vision, slow 

reaction time, 

alcohol, speeding, 

risk taking 

Failed brakes, 

missing lights, lack of 

warning systems 

Narrow shoulders, 

limited pavement 

markings, poorlytimed signals 

During (causes of 

crash severity) 

Failure to use 

occupant restraints 

Malfunctioning safety 

belts, poorly 

engineered air bags 

Non-breakaway sign 

supports 

After (factors of 

crash outcome) 

Age (high 

susceptibility), 

alcohol 

Poorly designed fuel 

tanks 

Poor emergency 

communication 

systems 

15
 
Example 2: Site-Specific Application of the Haddon Matrix 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

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

based on their potential for safety improvement. One site is an urban, four-legged, signalized 

intersection. A multidisciplinary team identified right-angle and left-turn crashes as target crash 

types for further consideration. Note one team member suggested a red-light-running camera 

to mitigate right-angle crashes prior to a comprehensive investigation. 

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

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

crashes. Table 7 presents their results. 

Table 7. Example 2 Haddon Matrix for right-angle and left-turn crashes. 

Period Human Factors Vehicle Factors Roadway Factors 

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

of hazardous - inadequate sight horizontal and

situation) distance and issues

processing information

(gap judgment)

• Drivers turning left

(northbound-west) -

inadequate sight distance

and issues processing

information (gap

judgment)

• High speed corridor

leading to high

intersection approach

speeds (downgrade on

southbound approach)

• High school and

community center near

intersection - novice

drivers (daytime hours)

and senior drivers

(evening hours)

vertical alignment

(southbound

approach)

• Limited sight

distance for left-turn

drivers

• Limited sight

distance to

intersection

(southbound

approach)

• Permissive left-turn

phase only

During (causes 

of crash 

severity) 

• Vulnerability to injury • Side impact

• No airbags

• Utility pole on the

sidewalk

After (factors 

of crash 

outcome) 

• Age • No factors

identified

• Emergency response

time

16
 
 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

After careful study of the Haddon Matrix, the Team summarized their conclusions. Figure 11 

presents a diagram of the study intersection. First, novice drivers traveling northbound to 

access the high school located in the southwest quadrant of the intersection have inadequate 

sight distance to southbound vehicles due to the vertical and horizontal alignments. Further, left 

turn movements require experience in checking for pedestrians as well as judging gaps (i.e., 

assessing the approach distance and speed of oncoming vehicles). The combination of limited 

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

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

issue occurs when senior drivers attend community events at the school in the evening. 

Figure 11. Diagram. Study intersection for example 2. 

17
 
 

 

 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

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

the following infrastructure-related countermeasures for further consideration. Note a 

comprehensive diagnosis leads to the identification of potential countermeasures to target the 

underlying crash contributing factors. There is still a need to employ countermeasure selection 

methods to determine the most appropriate countermeasure(s). Refer to Reliability of Safety 

Management Methods: Countermeasure Selection for further information and considerations 

related to countermeasure selection.(3) 

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

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

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

lane on all approaches.

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

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

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

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

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

intersection. Without a comprehensive diagnosis, the agency would have spent approximately 

$75,000 to install a red-light camera system, which is not likely to mitigate the target crashes in 

this particular scenario. From the comprehensive review, the Team identified limited sight 

distance and high approach speeds as factors contributing to right-angle crashes, not driver 

disobedience. Therefore, red-light cameras would not have treated the target crash type as 

demonstrated by the comprehensive and holistic method using the Haddon Matrix. In 

conclusion, preventing the installation of red-light cameras at this location demonstrates the 

value of using the Haddon Matrix to conduct a more comprehensive and reliable site diagnosis. 

18
 
 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

4. DATA REQUIREMENTS FOR DIAGNOSIS

The following is a brief overview of the data requirements for diagnosis using the more reliable 

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

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

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

Tools such as collision diagrams, summary tables, and charts are useful to review the

crash data. This information supports the completion of the Haddon Matrix for each

target crash type.

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

information supports the analysis of changes in traffic volume over time. Turning

movement counts, peak hour counts, and directional splits help to assess potential

safety issues related to traffic operations. Reviewing past traffic analysis reports, land

use, and information on future developments can help to understand future trends in

traffic operations and help to identify potential future safety issues related to operations.

• Roadway Data (Site Conditions): identify the site characteristics, including traffic

control and design elements. A preliminary desktop review of the site using aerial

images and video or photo logs can support a more efficient field investigation. Tools

such as condition diagrams are useful to summarize the roadway data.

• Field Observations: observe and identify traffic operations, adjacent land use, road

user demographics, and road user behaviors. This information supports the completion

of the Haddon Matrix for each target crash type.

19
 
 

 

 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

5. TOOLS AND RESOURCES FOR DIAGNOSIS

Tools and resources are available to support diagnosis, including guides and software. Some 

guides provide a discussion of the overall diagnosis process, while other tools relate to specific 

components of the process or specific safety issues. For example, tools such as the National 

Cooperative Highway Research Program (NCHRP) Report 500 series can help users identify 

common contributing factors for a given safety issue. The FHWA Road Safety Audit (RSA) 

website and related tools can help users conduct a comprehensive, multidisciplinary diagnosis. 

The FHWA Roadway Safety Data and Analysis Toolbox is a web-based repository of safety data 

and analysis tools. Use the Toolbox to identify an appropriate tool for your diagnosis needs. A 

Primer is available to understand the overall scope and functionality of the Toolbox as well as 

the roles, responsibilities, and tasks supported by tools in the Toolbox. 

USING THE ROADWAY SAFETY DATA AND ANALYSIS TOOLBOX 

There are two primary options for searching the Toolbox. The first is a predefined query using 

the four large icons in the upper right of Figure 12 (Manage, Analyze, Collect, and Research). 

The second is an advanced search option where users can search keywords and apply filters to 

customize their search as shown in the lower left of Figure 12. 

Figure 12. Screenshot. Roadway Safety Data and Analysis Toolbox. 

The following is a brief demonstration of the stepwise process to identify an appropriate tool 

to support diagnosis. 

1. Click the ‘Advanced Search’ icon, highlighted in the lower left of Figure 12.

2. From the advanced search page (Figure 13), leave the keyword blank and click the

search button. This returns a list of all tools in the Toolbox.

20
 
 

 

 

 

RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

Figure 13. Screenshot. Advanced search feature. 

Click the ‘Show/Hide Filters’ button, highlighted in the upper left of Figure 14. This reveals a list 

of filters to refine the general search. Use the ‘Safety Management Process’ filter to select 

‘Diagnosis’ as the primary area of interest as shown in Figure 14. Apply additional filters as 

needed to refine the results. For example, apply the ‘Tool Type’ filter to narrow the list of tools 

to application guides, information guides, software, information sources, or databases. 

Figure 14. Screenshot. Filter options from advanced search page. 

Using the stepwise process described in this section, the Toolbox returns guides such as the 

Road Safety Audit Guidelines, Integrated Safety Management Process, and Improving Safety on 

Rural Local and Tribal Roads: Site Safety Analysis User Guide. Related software tools from the 

Toolbox include AASHTOWare Safety AnalystTM, Interactive Highway Safety Design Model, 

and Pedestrian and Bicycle Crash Analysis Tool. 

21
 
RELIABILITY OF SAFETY MANAGEMENT METHODS: DIAGNOSIS
 

6. REFERENCES

1. American Association of State Highway and Transportation Officials (AASHTO),

Highway Safety Manual, First Edition, Washington, DC, 2010.

2. Srinivasan, R., F. Gross, B. Lan, and G. Bahar. Reliability of Safety Management Methods:

Network Screening, Report No. FHWA-SA-16-037, Federal Highway Administration,

Washington, D.C., October 2016.

3. Bahar, G., R. Srinivasan, and F. Gross. Reliability of Safety Management Methods:

Countermeasure Selection, Report No. FHWA-SA-16-039, Federal Highway

Administration, Washington, D.C., August 2016.

4. Srinivasan, R., F. Gross, and G. Bahar. Reliability of Safety Management Methods: Safety

Effectiveness Evaluation, Report No. FHWA-SA-16-040, Federal Highway Administration,

Washington, D.C., March 2016.

5. Federal Highway Administration, FHWA Road Safety Audit Guidelines, Publication

FHWA-SA-06-06, February 2005.

6. Hauer, E. (1996a), Detection of Safety Deterioration in a Series of Accident Counts.

Transportation Research Record 1542, 38-43.

7. Hauer, E. (1996b), Statistical Test of the Difference between Expected Accident

Frequencies, Transportation Research Record 1542, 24-29.

8. Heydecker, B. J., and J. Wu (1991), Using the Information in Road Accident Records

Proc., 19th PTRC Summer Annual Meeting, London.

9. 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.

10. Haddon, W., Jr. (1972). A logical framework for categorizing highway safety phenomena

and activity. Journal of Trauma 12: 193–207.

11. AASHTOWare Safety AnalystTM. Software Tools for Safety Management of Specific Highway

Sites: White Paper for Module 1—Network Screening, August 2010. Accessed September

19, 2016 from: http://www.safetyanalyst.org/whitepapers/module1.pdf

12. Kononov, J. and B.N. Janson (2002), Diagnostic Methodology for the Detection of Safety

Problems at Intersections, Transportation Research Record 1784, pp. 51-56, 2002.

13. Gross, F., T. Harmon, M. Albee, S. Himes, R. Srinivasan, D. Carter, and M. Dugas.

Evaluation of Four Network Screening Performance Measures, Report No. FHWA-SA-16-

103, Federal Highway Administration, Washington, D.C., October 2016.

14. Herbel, S., Laing, L., and McGovern, C. (2010), Highway Safety Improvement Program

(HSIP) Manual, Report FHWA-SA-09-029, Federal Highway Administration.

22
 
For More Information: 

Stuart Thompson 

Stuart.Thompson@dot.gov 

202-366-8090

Federal Highway Administration 

Office of Safety 

1200 New Jersey Avenue SE 

Washington, DC 20590 

http://safety.fhwa.dot.gov/ 

Publication # FHWA-SA-16-038