Road Safety

Information Analysis

A Manual for Local Rural Road Owners

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FHWA-SA-11-10

Roadway Safety Information Analysis: A Manual for 

Local Rural Road Owners

The Contract Officer’s Task Manager (COTM) for this document was Rosemarie Anderson, FHWA Office of 

Safety.

The Technical Oversight Working Group included John Dewar, Joe Hausman, Carol Tan, and Robert Pollack, 

FHWA; Jim Ellison, Pierce County, Washington (now retired); Kevin McGinnis, National Association of State 

EMS Officials; Roseanne Nance, Illinois DOT; Sara Weismann, Rutgers Transportation Safety Resource Center 

(now at NHTSA); and Joe Santos, Florida DOT.

According to the Federal Highway Administration, over 6 million lane-miles are in rural areas, and more 

than two-thirds of these rural roads are owned and operated by local entities. In 2008, 56 percent of 

highway fatalities occurred in rural areas. Rural areas face a number of highway safety challenges. Data is 

the foundation of any roadway safety improvement program and often this is lacking, especially in local 

rural areas. 

This document was developed to provide data collection and analysis techniques as well as other processes applicable to the local practitioner to help improve the safety of local rural roads. The information will 

acquaint local practitioners – regardless of background or experience level – with the sources, calculations, 

tools, and methods to make data-supported decisions regarding local rural road safety. 

Science Applications International Corporation (SAIC)

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Office of Safety

Federal Highway Administration

1200 New Jersey Ave, SE

Washington, DC 20590-9898

Golembiewski, G.A. and Chandler, B.

Data, Fatality, Injury, Traffic volumes, Rates, Segments, 

Corridors, Local rural roads, Trends, Field reviews, 

Implementation

No restrictions. 

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January 2011

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i | Road Safety Information Analysis

Table of Contents

Table of Contents................................................................................................ i

1 Introduction ..............................................................................................1

1.1 The Challenges on Local Rural Roads..................................................1

1.2 Local and Rural Guides..............................................................................1

2 Safety Information Collection................................................................4

2.1 Crash Data......................................................................................................4

2.1.1. Law Enforcement Crash Reports........................................................5

2.1.2. State Crash Database.................................................................................6

2.1.3. FARS Database................................................................................................7

2.1.4. Hospital Data...................................................................................................8

2.1.5. Public Notification of Safety Concerns............................................8

2.2 Roadway Characteristic Data............................................................... 11

2.3 Exposure Data ........................................................................................... 12

3 Analyzing Data........................................................................................13

3.1 Crash Frequency....................................................................................... 13

3.1.1. Crash Averaging.........................................................................................15

3.1.2. Trend Analysis..............................................................................................17

3.2. Crash Rates................................................................................................. 18

3.2.1. Road Segment Rate Calculation......................................................19

3.2.2. Intersection Rate Calculation.............................................................20

3.2.3. Crash Rates by Roadway Mileage ...................................................20

3.2.4. Using Crash Rates......................................................................................22

3.3. Crash Severity............................................................................................ 22

3.4. Identifying Potential Crashes............................................................... 23

3.5. Data Analysis Tools.................................................................................. 24

4 Countermeasure Selection...................................................................25

4.1. Types of Countermeasure Implementation ................................... 25

4.1.1. Systematic Approach..............................................................................25

4.1.2. Spot Location Approach.......................................................................26

4.1.3. Comprehensive Approach..................................................................27

4.2. Incorporating Roadway Characteristics........................................... 28

4.3. Countermeasure Evaluation ................................................................ 28

5 Case Studies ............................................................................................30

6 Summary .............................................................................................43

Appendix A State Crash Report Example................................................A-1

Appendix B Resources ................................................................................B-1
Road Safety Information Analysis | ii

List of Tables

Table 1 Sample Local Jurisdiction Crash Database .........................9-10

Table 2 Crash History for County Road 220 ...........................................15

Table 3 Rolling Crash Average for County Road 220............................16

Table 4. Using Route Comparison to Determine Crash Rates .............22

Table 5. Partial Listing of MPO Crash Rate Averages by Cross

 Section............................................................................................37

Table 6. Forecast Crash Frequency for Russell Street Project

 Alternatives....................................................................................42

List of Figures

Figure 1 Data Collection and Analysis Steps .............................................3

Figure 2 Relationship between Available Information and Analysis

 Possibilities ....................................................................................14

Figure 3 Location of Crashes on County Road 220 ................................15

Figure 4 County Road 220 Crash Trends, 2001-2009.............................18

Figure 5 MORPC’s Ranking Steps...............................................................34
1 | Road Safety Information Analysis

1 Introduction

1.1. The Challenges on Local Rural Roads

In 2008, the Fatality Analysis Reporting System (FARS) indicated that 56 

percent of the 37,261 fatalities on U.S. roadways occurred in rural areas.1 

Only 40 percent of all vehicles miles traveled nationally occur on rural 

roads, indicating the overrepresentation of severe rural traffic crashes.2

According to the Federal Highway Administration (FHWA), of the 8.4 

million lane-miles of roads in the United States, over six million lane-miles 

are rural and more than two-thirds of these rural roads are owned and 

operated by local entities.3 Rural areas face a number of highway safety 

challenges. For instance, rural crashes tend to be at relatively higher 

speeds than urban crashes; more victims of fatal crashes in rural areas 

were not wearing a safety belt at the time of the crash; and it can take 

first responders longer to arrive at the scene of a rural crash, requiring 

victims to wait longer for medical attention. Another challenge is that 

the geometry of many rural roads was never truly designed; instead, 

these roadways evolved over time – from dirt to gravel to pavement – 

without design upgrades or other safety improvements. Additionally, 

roadside hazards such as utility poles or trees close to the roadway as 

well as steep pavement drop-offs can contribute to severe crashes on 

rural roads.

1.2. Local and Rural Guides

This document is part of a series developed by FHWA to specifically 

address the safety issues experienced on local rural roadways. It was 

developed to provide processes and examples for typical information 

1 National Highway Traffic Safety Administration, Traffic Safety Facts: 2008 Data (Washington, DC: 

2009). Available at http://www-nrd.nhtsa.dot.gov/Pubs/811164.pdf.

2 Safe Transportation Research and Education Center (SafeTrec), Institute of Transportation Studies, 

University of California-Berkley, “Rural Road Safety website.” Available at

http://www.tsc.berkeley.edu/research/ruralroads.html.

3 U.S. Department of Transportation, Federal Highway Administration, Functional System Data: 

Lane-Miles by Functional System. , Highway Statistics Series, October 2008. Available at

http://www.fhwa.dot.gov/policyinformation/statistics/hm60.cfm?hm_year=2007.
Road Safety Information Analysis | 2

needs and analyses local practitioners can use to help improve the 

safety of local rural roads. Its purpose is to acquaint local practitioners–

regardless of background or experience level– with the information 

sources, calculations, tools, and methods to make data-supported 

decisions regarding local rural road safety.

Highway safety planning and policy decisions are generally made based 

on data gathered at the State, county, and municipal levels. Regulations 

for safety-related Federal funding and safety plan development 

require data for the decision making process. At the local level, these 

data requirements might seem daunting, particularly as they relate to 

funding and staff time needed for data collection, analyses, and overall 

assessments of safety improvements. It is important to note that a datasupported approach can increase the effectiveness of the distribution of 

limited funds to improve safety on local rural roads.

Once safety information is collected and needs are identified, analysis 

can further support practitioners during countermeasure selection. 

Practitioners can then choose strategies that target the areas of need 

based on analysis of crash types and other data. When applied directly 

to appropriate locations (both those with a history of crashes and those 

without a history but with a potential for future crashes), these targeted 

countermeasures can significantly improve traffic safety on the roadway 

network. 

Figure 1 depicts the steps for the data collection and analysis process. 

This process includes determining which information sources will be 

used, extracting and storing the data as appropriate, and conducting the 

appropriate analyses. The final step is to periodically review crash data 

to determine emerging trends and to assess the effectiveness of installed 

countermeasures.

Data collection and analysis should be the base of the safety 

management process. The importance of data should be clearly 

emphasized throughout all stages of a project’s life cycle.
3 | Road Safety Information Analysis

Information can be collected from:

•	 State and national crash databases

•	 Law enforcement crash reports

•	 Hospital records

•	 Observations by law enforcement and public works staff

•	 Public notifications

•	 State and local roadway databases

•	 Traffic count records

Compile information in a table to determine if patterns 

emerge and to track responses to known crash locations.

Data can be analyzed in the following ways, based on available 

information:

•	 Crash Frequency

•	 Crash Averaging

•	 Trend Analysis

•	 Crash Rate Calculations

•	 Identification of potential future crash locations

Details from the crash data can provide clues about what 

treatments are the most appropriate. Based on the availability 

of data, different approaches can be taken:

•	 Systematic Approach

•	 Spot Location Approach

•	 Comprehensive Approach

Step 1

Collect Safety, Roadway, and Exposure Information

(Manual: Section 2)

Step 2

Record Information for Safety Analysis

(Manual: Table 1)

Step 3

Analyze Data

(Manual: Section 3)

Step 4

Select Countermeasures

(Manual: Section 4)

Figure 1. Data Collection and Analysis Steps
Road Safety Information Analysis | 4

2 Safety Information Collection

Several types of data are needed to develop an effective safety program. 

This information can assist agencies in identifying locations with safety 

issues, prioritizing locations, and identifying the most appropriate 

treatments. It is difficult to solve a problem until information is collected 

and analyzed to determine the nature of the problem. There are three 

common types of data needs for a safety project or program:

1. Crash data;

2. Roadway characteristic data; and

3. Exposure data.

2.1. Crash Data

Crash history data is the primary source of information regarding the 

traffic safety environment, driver behavior, and vehicle performance. 

In order to address safety problems, traffic safety data should be 

timely, accurate, complete, consistent, integrated, and accessible. Good 

quality data have the potential to improve problem identification, 

prioritization of project selection, and evaluation of the effectiveness of 

countermeasures.4

Compared to Federal and State safety professionals, local practitioners 

tend to be more aware of locations with a history of crashes within their 

own jurisdictions due to the size of the area and news-worthiness of 

local traffic crashes. This knowledge, coupled with actual crash data, 

can aid the practitioner in identifying factors contributing to crashes 

and allow them to choose improvements to address these issues. The 

primary sources of crash data for local practitioners include:

•	 Law enforcement crash reports; 

•	 State crash database; 

•	 Fatality Analysis Reporting System (FARS) database;

•	 Hospital data; and

•	 Public notification of safety concerns. 

4 Federal Highway Administration, Crash Data Improvement Program Guide, April 2010. Available at: 

http://safety.fhwa.dot.gov/cdip/finalrpt04122010/index.cfm#toc
5 | Road Safety Information Analysis

2.1.1. Law Enforcement Crash Reports

The primary and most comprehensive sources of information used to 

populate crash databases are the crash reports completed by State and 

local law enforcement organizations. Crash reports are the record of a 

traffic crash that has occurred on a public roadway. The format of the 

report varies by State, and sometimes by jurisdiction within a State. See 

Appendix A for an example of a State crash report.

Crash reports generally include the following information about the 

incident:

•	 Date & time of crash;

•	 Crash severity;

•	 Crash type;

•	 Location & roadway information;

•	 Weather;

•	 Number and types of vehicles involved;

•	 Driver information (e.g., name, age, gender); and

•	 Contributing circumstances.

By reviewing the information provided on crash reports over a period 

of time, recurring variables may be discovered to help pinpoint which 

factors are contributing to crashes.

The types of information collected and data collection methods used 

by law enforcement officers vary. Documentation methods among 

individual officers in the same jurisdiction also vary at times. The local 

practitioner should be aware of these variances during the review and 

analysis of crash reports.

There are a number of crash reporting issues that should be considered 

by law enforcement officers and safety practitioners when working with 

crash reports. These include:

Location Coding. Exact location of a crash is sometimes difficult 

to determine because the location of impact is often not where the 

vehicles come to rest. Referencing crash locations is often dependent on 

the level of technology used, the experience of the officers at the scene, 

and the approach used by the jurisdiction. 

Typical methods include:

•	 Distance from known reference point;

•	 Street address;

•	 Milepost; and

•	 Global Positioning System (GPS) – latitude and longitude coordinates.
Road Safety Information Analysis | 6

Limited resources often preclude the use of expensive technology, and 

reference points may be long distances apart, affecting accuracy. In a 

situation where GPS data is available, Geographic Information Systems 

(GIS) tools may be available to use these data for in-depth analysis and 

mapping.

Regardless of the method used, the location of each crash is an 

important data element for safety analysis.

Crash Type Definitions. Various jurisdictions may define crash report 

terms differently. For example, whereas rear-end and head-on crash 

types may be standard classifications in some areas, other regions 

may interpret these classifications differently or include additional 

classifications. 

Threshold for Reportable Crashes and Severity Determination. 

States across the country use varying criteria to determine when a crash 

is deemed reportable and to define crash severity. Reportable crashes 

are crashes that include a fatality, injury, or property damage cost 

meeting a specified threshold. Most States have an estimated property 

damage cost threshold for a property damage only (PDO) crash to be 

considered reportable. In some States, if the vehicle can be driven away 

from the scene of the crash and no one is injured, it is not considered a 

reportable incident.

Jurisdictions are generally consistent with regard to the definition of a 

fatal crash; but as the severity of a crash diminishes, the classifications 

can vary by state. The severity of an injury crash is often difficult to 

determine at the scene and can be subjective based on the reporting 

officer if follow-up with hospital or Emergency Medical Services (EMS) is 

not conducted. Local practitioners should be aware of the issues in their 

States and local jurisdictions during crash data review and subsequent 

analysis.

Practitioners should also be aware that data issues such as missing, 

contradictory, and erroneous information have the potential to skew 

analysis and result in inefficient decision making. Coordination between 

law enforcement personnel and local road practitioners is important to 

ensure the efficient collection of crash data. 

2.1.2. State Crash Database

Statewide crash databases typically offer comprehensive records on all 

reported crashes. They are compiled from State, county, and municipal 

law enforcement agencies submitting crash reports to a central State 

repository. State crash databases typically include the information 

collected at the scene of the incident.
7 | Road Safety Information Analysis

However, State repositories can vary by agency, depending on the State. 

Data compilation and dissemination methods can also differ by State 

and not all States share their collected crash data with local jurisdictions. 

Moreover, local crash data in the repository can be incomplete. Local 

practitioners should be aware of the issues in their State and should 

identify the relevant points of contact necessary to obtain further 

information. 

State data on crash types can provide clues regarding the potential 

for future severe crashes and countermeasures that could help. For 

example, if only non-injury roadway departure crashes have been 

recorded at a particular curve over a period of time, these incidents 

could be a precursor to a severe crash in the future if this location is not 

treated, since rural roadway departure crashes tend to be more severe 

than other types. 

2.1.3. FARS Database

The Fatality Analysis Reporting System (FARS) is a national database of 

fatal crashes that occurred on the national public roadway network. 

It is maintained and operated by the National Highway Traffic Safety 

Administration (NHTSA).

FARS contains data derived from a census of all fatal traffic crashes 

within the 50 States, the District of Columbia, and Puerto Rico. FARS 

was developed to provide an overall measure of highway safety at 

the national level, to help identify traffic safety problems, to suggest 

solutions, and to help provide an objective basis upon which to evaluate 

the effectiveness of standards and highway safety programs among the 

States.5

Each State provides specific fatal crash information in a standard format 

to FARS. The data originate from State accident reports. Once collected 

by the FARS analysts, the data are recoded to match the standard FARS 

forms. FARS data are used to answer questions regarding the roadway, 

vehicle, and driver factors that contribute to traffic safety.6 The benefit to 

the FARS data is that it includes information on every fatal crash that has 

occurred within a State; so if a jurisdiction has no other reliable source of 

data, FARS can provide a starting point.

The FARS dataset is limited to those crashes involving a fatality. In a local 

rural setting, fatal crashes are relatively rare and random; therefore, FARS 

data may not provide the best indication of locations with identifiable 

safety issues.

5 U.S. Department of Transportation, National Highway Transportation Safety Administration, “Fatality 

Analysis Reporting System Fatal Crash Data Overview” brochure, DOT HS 809726, April 2005.

6 U.S. Department of Transportation, National Highway Transportation Safety Administration, 

“Fatality Analysis Reporting System (FARS) Encyclopedia” web site, http://www-fars.nhtsa.dot.gov
Road Safety Information Analysis | 8

2.1.4. Hospital Data

If on-scene crash data is unavailable, other potential sources of 

information are local Emergency Medical Services (EMS) and medical 

centers. EMS data can provide information regarding the locations and 

degree of EMS responses to traffic crashes. Similarly, hospitals may have 

some information available regarding emergency room visits or other 

admissions connected to traffic crashes. Due to a number of Federal 

and State regulations regarding the privacy of medical information, the 

availability of these data could be limited and will vary by State.

The Crash Outcome Data Evaluation System (CODES) links crash records 

to injury outcome records collected at the scene and en route by EMS; 

by hospital personnel after arrival at the emergency department or 

admission as an inpatient; or on the death certificate at the time of death. 

CODES is designed to foster and cultivate crash-outcome data linkages 

for highway safety applications by State and local practitioners.7

Analyses of linked data can help traffic safety professionals and coalitions 

to determine and implement data-driven traffic safety priorities. In many 

States, the State Department of Health (or similar agency) has access to 

the CODES linkage data, and this information in combination with the 

State crash database and FARS data, can be beneficial to local safety 

practitioners supporting safety initiatives.

2.1.5. Public Notification of Safety Concerns 

Potential infrastructure issues, perceived safety concerns, or other trafficrelated situations are sometimes reported to the local agency by citizens 

or citizen groups. The notification is typically delivered by telephone call 

or e-mail to the officials of a local jurisdiction, and a response is often 

requested.

The public official should compile records of these notifications so that 

multiple notifications of the same location can be identified. While 

this is primarily anecdotal information, public notifications can serve as 

indicators that a problem may exist and could warrant further review and 

analysis. Additionally, open communication with local citizens can help 

practitioners identify potential highway safety issues in the community.

Table 1 shows how a simple database could be designed to capture and 

store information from the various sources previously discussed. 2.2.

7 CODES data is collected in a limited number of states. Local practitioners should consult 

their State highway agency or Local Technical Assistance Program (LTAP) center for additional 

information.
9 | Road Safety Information Analysis

Table 1. Sample Local Jurisdiction Crash Database

Intersection/

Location

Source of 

Information

Date (MM/

DD/YYYY)

Type of Information Problem Crash?

Intersection - Rt 123 and 

Fox Mill Road

Local 

Newspaper

3/8/2008 Citizen 

Complaint

Speeding N

Route 123 West / 1/2 

mile south of intersection 

with Fox Mill Road to 

intersection

Local 

Newspaper

12/1/2007 Citizen 

Complaint

Drivers losing 

control at 

curve

N

Intersection - Route 657 

and Glade Drive

Local Police 2/1/2008 Police Report Crash Report Y

Route 657; 1/2 mile south 

of Glade Drive 

Local Police 4/1/2008 Police Report Y

Intersection - Route 657 

and Clifton Road

State Police 10/4/2008 Police Report Crash Report Y

Clifton Road; South of Veirs 

Mill Road

State Police 11/11/2008 Police Report Y

Intersection - Route 657 

and Glade Drive

Local Police 11/12/2009 Police Report Crash Report Y

Route 657; 1/4 mile South 

of Glade Drive

Local Police 11/24/2009 Police Report Y

Intersection - Middlebrook 

Pike and Waples Mill Road

Maintenance 

Crew

12/1/2009 Observation Missing Stop 

Sign

N

Middlebrook Pike; 1 mile 

North of Running Cedar 

Road

Maintenance 

Crew

12/12/2009 Observation N
Road Safety Information Analysis | 10

Nature of Crash Crash Type Time of Day 

(24 hr time)

Weather 

Conditions Action? Date of Action

NA NA Pending

NA NA Pending

Vehicle traveling North 

on Route 657 hit while 

making left turn onto 

Glade Drive

Right angle 7:22 Clear

Driver hit tree on 

shoulder; single vehicle

Roadway 

departure; 

hit stationary 

object

23:03 Snow

Vehicle traveling West on 

Clifton road rear-ended 

at intersection

Rear-end 19:21 Rain

Driver ran off road on 

curve; exceeding posted 

speed

Roadway 

departure; on 

curve

12:23 Rain

Vehicle traveling West 

on Glade collided with 

vehicle on Route 657

Right angle 10:06 Rain

Driver ran off road; single 

vehicle

Roadway 

departure

23:04 Rain

NA NA Replaced 

Stop Sign

12/19/2009

NA NA Advanced 

Curve 

Warning Sign 

Replaced

1/8/2010

Table 1. (continued) Sample Local Jurisdiction Crash Database
11 | Road Safety Information Analysis

2.2 Roadway Characteristic Data

It is also valuable to obtain information about the roadway infrastructure. 

The following roadway data are often used to assist practitioners in 

safety analyses:

•	 Roadway surface (dirt, aggregate, asphalt, concrete);

•	 Lane (number, width);

•	 Shoulder (type, width);

•	 Median (type, width);

•	 Roadway alignment (curve, straight);

•	 Intersection (number of legs, configuration); and

•	 Traffic control devices present (signs, pavement marking, traffic signals).

This information can be combined with crash data to help local 

practitioners identify appropriate locations and treatments to improve 

safety. For example, if a segment of roadway is experiencing a high 

number of roadway departure crashes, analysis of the inventory of 

roadway elements could reveal that the roadway does not have edgeline 

pavement markings. An appropriate countermeasure could be to install 

edgeline pavement markings to provide guidance to motorists to stay on 

the traveled way.
Road Safety Information Analysis | 12

2.3. Exposure Data

The raw number of crashes can sometimes provide misleading 

information about the most appropriate locations for treatment. 

Introducing exposure data helps to create a more effective comparison 

of locations. Exposure data provide a common metric to the crash 

data so roadway segments and intersections can be compared more 

appropriately.

The two most common types of exposure data used are traffic volume 

and roadway miles.

1. Traffic volume. A count of the number of vehicles along a 

segment of road or entering an intersection can provide information 

to the practitioner for comparison. For example, if two intersections 

have the same number of crashes but different entering traffic 

volumes, the location with fewer vehicles (i.e., less exposure) will have 

a higher crash rate, meaning that vehicles were more likely to have 

experienced a crash at that location. This rate reflects the fact that 

an increase in the number of vehicles has an effect on the expected 

number of crashes.

2. Roadway miles. In many cases, traffic count data is not available 

and the cost to collect the information may be prohibitive for local 

jurisdictions. An alternative method for analyzing the data is to use 

the route’s length in miles. If two roadways have a similar number of 

crashes but differing lengths, calculations will indicate the shorter road 

has experienced more crashes per mile. 
13 | Road Safety Information Analysis

3 Analyzing Data

Collected data should be analyzed and reviewed to identify locations 

with safety issues or locations with potential for future safety issues, 

and to select countermeasures to improve safety. Depending on 

the completeness, accuracy, and timeliness of available data, a local 

jurisdiction can analyze that information in a number of ways. Figure 

2 shows the relationship between data availability and the analysis 

potential for improved safety-related decision-making. 

In Figure 2, as more types of data become available to the safety 

practitioner (moving up in the figure on the left), the ability to perform 

more in-depth safety analyses is enhanced (the list on the right of the 

figure). For example, if only the county and route of a crash location are 

known, analysis is limited to analyses by county and route. But as more 

specific location information is collected and stored, including milepost 

location or GPS coordinates, options like pin map cluster analysis and 

location comparisons become available. If additional exposure and 

roadway characteristic information can be linked to the location (traffic 

counts, roadway width, shoulder type) then even more robust analyses 

can be performed.

As noted, several types of data analysis can be conducted to support 

roadway safety depending on available crash, roadway and exposure 

data. They include:

•	 Crash frequency;

•	 Crash averaging;

•	 Trend analysis; and

•	 Crash rates.

3.1. Crash Frequency

Crash frequency is one of the simplest forms of crash data analysis. It is 

defined as the number of crashes occurring within a specific jurisdiction, 

on a roadway segment, or at an intersection. Multiple crashes occurring 

at the same location over a period of time may be an indication of a 

safety issue and should be investigated and addressed appropriately. 

This is referred to as “clustering”. Crashes can be clustered by route, 

specific location on that route, or by intersection. 
Road Safety Information Analysis

| 14

With this information...

Roadway elements

(travelway width, shoulder type/width)

Contributing circumstances to crash

Crash severity

Crash type

Traffic levels

Crashes by route

Crash location (GPS milepost,

or distance from known point)

Countermeasure selection byroadway characteristicsCountermeasure selectionSevere crash rateFatal crash rateCrash rates by crash typeSystematic analysis by crash rateCrash rate by traffic levelsSystematic analysis by routeCluster analysisPin mapsGIS mapping

...this analysis is possible

Figure 2. Relationship between Available Information and Analysis Possibilities 
15 | Road Safety Information Analysis

Example: County Road 220, in Potter’s Grove, Rae County, is a 17-mile 

route that has 2,100 vehicles traveling on it each day. It had the following 

crash history over the past 5 years as shown in Table 2:

Year Crashes

2005 2

2006 1

2007 6

2008 1

2009 2

5-year total 12

Table 2. Crash History for County Road 2208

Beyond looking at the raw number of crashes on a route, a practitioner 

can plot the crash locations on a pin map to determine clustering, as 

shown in Figure 3. Data from this figure indicate that the CR220 & 1st 

Street and CR220 & Main Street intersections appear to have experienced 

multiple crashes over the 5-year study period.

Figure 3. Location of Crashes on County Road 220

3.1.1. Crash Averaging 

Crashes are relatively rare events, so it is important that a safety analysis 

includes an adequate time frame of study. Crash averaging allows the 

practitioner to normalize crash data over a longer period than one year 

8 Data from this table will be used throughout this manual to illustrate different analysis methods.

Traffic Crash

County Road 220

Main Street

1st Street
Road Safety Information Analysis | 16

to account for annual anomalies that can skew analyses. Due to the 

randomness of traffic crashes, it is likely that any one year could have a 

much higher or lower number of crashes than the typical year. A rule of 

thumb is to collect data from the previous 3 to 5 years, with 3 years as a 

working minimum. A longer period of time increases the statistical value 

of the data; however, if the period is too long, there is a chance that the 

situation (e.g., roadway configuration, traffic volume and patterns) may 

have changed.

For County Road 220 (Table 2, above), the 5-year average of crashes is 

calculated by adding the total crashes from 2005-2009 and dividing by 

the period of 5 years:

Results show that County Road 220 averaged 2.4 crashes per year during 

that time period. Note that in 2007, the route experienced six crashes 

(five more than the year before and the year after), which might have 

caused the route to be “flagged” based on that single year of crashes. 

Averaging data across the 5-year analysis period provides a number 

more consistent with actual roadway conditions over time.

A Rolling Crash Average can also be used to achieve some normalcy 

from crash data. A Rolling Crash Average looks at the previous 3 to 5 

years at more than one point in time. For example, the first data point 

could be 2001-2005 (a 5-year average). The next would be the 2002-

2006 average, and so on. This technique further flattens the curve in 

an attempt to avoid inappropriate reaction to one or two statistically 

insignificant data points.

Year Crashes 5-year period Rolling

Average

2001 1

2002 3

2003 2

2004 0

2005 2 2001-2005 1.6

2006 1 2002-2006 1.6

2007 6 2003-2007 2.2

2008 1 2004-2008 2.0

2009 2 2005-2009 2.4

Table 3. Rolling Crash Average for County Road 220

= 2.4 (2+1+6+1+2)

5
17 | Road Safety Information Analysis

Table 3 shows that the rolling average of County Road 220 stayed 

relatively steady up to 2006, and then increased slightly from 2007 to 

2009. A rolling average is commonly used to smooth out short-term 

fluctuations in the data and highlight longer-term trends. A study of the 

annual crashes shows one year with an atypically high number of crashes 

(six crashes in 2007). If crashes continue to hover near one or two 

crashes per year, the rolling average will quickly revert to that number as 

well. 

The difference between looking at crashes per year and the rolling 

average, as shown in Table 3, is that the “peak” of the rolling average is 

only 2.4 versus six when looking at one year at a time. This supports a 

broader view of analysis by looking at the big picture and not focusing 

on a single data point.

3.1.2. Trend Analysis 

A practitioner can also examine the trend of crashes over time to 

determine if crashes have been rising or falling. An increasing number of 

crashes may indicate an emerging safety issue. The crash history can be 

placed in a number of categories indicating both the number of crashes 

and the recent trend, such as:

•	 High and rising;

•	 High but falling;

•	 High and steady;

•	 Low but rising;

•	 Low and falling; and

•	 Low and steady.

For County Road 220, Figure 4 provides a graphic representation of 

the crashes from 2001-2009 and the trend for these crashes. The figure 

indicates the crash number fluctuates from year to year and the trend is 

rising. The linear trend line can be calculated by a computer software 

program (e.g., Microsoft Excel) to provide a general idea of the rise or 

fall of traffic crashes based on the patterns of change from year to year. 

In this case, it would be worthwhile for the practitioner to perform two 

additional steps:

1. Analyze the reason for the increase by reviewing the crash reports.

2. Note the trend for future analysis.
Road Safety Information Analysis | 18

0

1

2

3

4

5

6

7

2000 2002 2004 2006 2008 2010

Crashes

Trend

Figure 4. County Road 220 Crash Trends, 2001-2009

3.2. Crash Rates 

Crash rate analysis of the relative safety of a segment or intersection 

takes into account exposure data. The crash rate is calculated to 

determine relative safety compared to other similar roadways, segments, 

or intersections. Crash rate analysis typically uses exposure data in the 

form of traffic volumes or roadway mileage.

Typically, traffic volumes are expressed in the form of Annual Daily Traffic 

(ADT). As discussed above, traffic volume data is not always available 

at the local jurisdiction level. In these cases, rates can be calculated 

using other exposure data, such as roadway length. Information may be 

available from other agencies including county traffic or maintenance; 

the Metropolitan Planning Organization (MPO); the Regional Planning 

Organization (RPO); or from the State database.

The benefit of crash rate analysis is that it provides a more effective 

comparison of similar locations with safety issues. This allows for 

prioritization of these locations when considering safety improvements 

with limited resources. 
19 | Road Safety Information Analysis

3.2.1. Road Segment Rate Calculation

The crash rate for road segments is calculated as:

Where:

R = Crash rate for the road segment expressed as crashes per 100 

million vehicle-miles of travel (VMT).

C = Total number of crashes in the study period.

N = Number of years of data.

V = Number of vehicles per day (both directions).9

L = Length of the roadway segment in miles.

If County Road 220 was being assessed with the following values:

C = 12 crashes over the past 5 years on this segment.

N = 5 years of data.

V = 2,100 vehicles per day.

L = 17 miles.

The resulting segment crash rate would be:

Depending on the details of crash reporting methods and crash history 

in a particular jurisdiction, a value of 18.4 may or may not be cause 

for additional study. The most appropriate use of this crash rate is to 

determine the relative safety of a roadway segment when compared to 

similar segment within a specific jurisdiction. 

3.2.2.Intersection Rate Calculation

The most common equation used to calculate a crash rate at an 

intersection is as follows: 

9 It is possible that traffic counts by direction are available from different years. In this case, a growth 

factor should be applied to the earlier data (based on historic trends at that site) so that it will be 

consistent with the newer information.

R = 100,000,000 x 12

365 x 5 x 2,100 x 17 

= 18.4 crashes per 100 million vehicle 

miles of travel on County Road 220

R = 100,000,000 x C

365 x N x V x L
Road Safety Information Analysis | 20

Where:

R = Crash rate for the intersection expressed as accidents per million 

entering vehicles (MEV).

C = Total number of intersection crashes in the study period.

N = Number of years of data.

V = Traffic volumes entering the intersection daily.10

If, for example, an intersection were being assessed with the following 

values:

C = 5 total crashes over the past 5 years.

N = 5 years of data.

V = 1,500 entering vehicles per day.

The resulting intersection crash rate would be:

Depending on the details of crash reporting methods and crash history 

in a particular jurisdiction, a value of 1.82 may or may not be cause 

for additional study. The most appropriate use of this crash rate is to 

determine the relative safety of an intersection when compared to 

similar intersections within a specific jurisdiction.

3.2.3. Crash Rates by Roadway Mileage

On many local roadways traffic volume information is not available. 

In these cases, other data can be used to make comparisons on a 

jurisdiction’s system. As an example, route length can be used to 

develop a more accurate comparison of segment crashes than a simple 

crash frequency. Crashes per mile of roadway allow for an improved 

analysis across the system by improving the ability to compare crashes 

on roadways of differing lengths.

10 It is possible that traffic counts on the roadways in this analysis are available from different years. 

In this case, a growth factor should be applied to the data (based on historic trends at that site) so 

that it will be consistent with the newest available counts.

R = 1,000,000 x C

365 x N x V

R = 1,000,000 x 5

365 x 5 x 1,500 

= 1.82 crashes per million entering 

vehicles.
21 | Road Safety Information Analysis

For example, two roadways could have the same number of crashes 

but different roadway lengths. In this case, traffic volume data is not 

available. By factoring in a measure of exposure (in this case route 

length), the calculation indicates that County Road 220 may be a more 

promising roadway for safety treatments.

A “crashes per mile” rate for road segments is calculated as:

Where:

R = Crashes per mile for the road segment expressed as crashes per 

each 1 mile of roadway per year.

C = Total number of crashes in the study period.

N = Number of years of data.

L = Length of the roadway segment in miles.

If County Road 220 was being assessed with the following values:

C = 12 crashes over the past 5 years on this segment.

N = 5 years of data.

L = 17 miles.

The resulting segment crash rate would be:

If Route B was being assessed with the following values:

C = 12 crashes over the past 5 years on this segment.

N = 5 years of data.

L = 26 miles.

The resulting segment crash rate would be:

R =

C

N x L

R =

12

5 x 17

= 0.14 crashes per mile per year on 

County Road 220.

R =

12

5 x 26

= 0.09 crashes per mile per year on 

County Road 305.
Road Safety Information Analysis | 22

The most appropriate use of any crash rate is as a relative value to 

compare the safety of a segment or intersection to similar locations in a 

specific jurisdiction. As shown in Table 4, the crash rate of County Road 

220 is higher than County Road 305 due to its shorter route length.

Roadway Crashes (C) Years of Data 

(N)

Length of segment (L)Crashes per 

mile per year

County Road 

220

12 5 17 miles 0.14

County Road 

305

12 5 26 miles 0.09

Table 4. Route Comparison by Crashes per Mile

It is important to note that only roadways with similar cross-sections (e.g., 

two-lane, four-lane undivided, four-lane divided expressways) should be 

compared by section length.

3.2.4.Using Crash Rates

Crash rates can be used to compare the crash experience of similar 

locations in the jurisdiction, region, and state.11 One method of 

comparing intersections or segments within a jurisdiction is to develop 

an average crash rate for the network. By calculating crash rates at 

a number of locations (intersections and segments) in the region, a 

baseline average for the comparison of future targeted locations can be 

developed. If resources are not available for this type of analysis, another 

source could be State highway agencies. State agencies typically 

develop average crash rates for different types of intersections and 

roadway segment cross-sections for statewide analyses. 

3.3. Crash Severity

Knowledge of the severity of crashes in a jurisdiction can assist 

practitioners in determining their safety needs. For example, the 

frequency of crashes at urban intersections may be higher than at rural 

curves, but in many cases the rural curve crashes are more severe. In 

addition, if two similar locations had the exact same number of crashes, 

it may be appropriate to select the location with more severe crashes to 

address first.

11 Similar is defined as similar in cross section, relatively similar traffic volumes (even if counts are 

unavailable), and roadway use (i.e., arterial, collector, local).
23 | Road Safety Information Analysis

3.4. Identifying Potential Crashes

Local jurisdictions often do not have access to all the data desired for 

safety analysis. While much of the discussion focuses on examining a 

location’s crash history, it is also important to identify locations on local 

rural roads that show potential for future crashes. Identifying these 

locations and proactively implementing safety improvements can 

potentially save lives. 

Identifying and addressing locations with potential safety issues and no 

crash history can be accomplished in the following steps:

1. Identify intersections and roadway segments with a relatively higher 

number of crashes than average in the jurisdiction, if available.

2. Examine crash reports to determine crash attributes, including the 

contributing circumstances to each crash.

3. Identify common attributes in these crashes.

» Human factor examples include driver distraction, alcohol use, speeding, 

and non-use of safety belts.

» Infrastructure examples include roadway geometry, design features, and 

traffic volume.

4. Identify other intersections and roadway segments on the network that 

have similar roadway features. Examples may include curves, intersections 

with poor sight distance, passing zones, and high traffic volume.

» Even though they have yet to experience crashes, the roadway attributes 

of these locations are similar to intersections and segments that are 

already experiencing crashes. This can be indicative of a location with a 

potential for future crashes.
Road Safety Information Analysis | 24

5. Conduct a field review to assess the locations and determine need.

6. Implement low-cost safety improvements, as appropriate, at identified 

locations to prevent future crashes.

Federal and State studies have identified specific roadway features that 

can contribute to crashes. Additionally, these studies have identified 

tested and proven safety countermeasures to address these issues. Local 

practitioners should review available literature when considering these 

types of safety improvements on their network. See Appendix B for a list 

of resources.

3.5. Data Analysis Tools

Most States have developed data analysis tools that electronically 

analyze crash data and incorporate other types of data (e.g., roadway 

information, traffic volumes) to conduct comprehensive analyses. Often 

these tools are shared with local jurisdictions. For additional information, 

a local practitioner should contact the State highway agencies or State 

Local Technical Assistance Program (LTAP) center. Resources related to 

data analysis tools and methods currently in use are listed in Appendix B.
25 | Road Safety Information Analysis

4 Countermeasure Selection

The results of data analysis will provide a summary of the crash 

frequency and rates on the roadway network and can highlight 

associated crash factors. The next step is to determine the most 

appropriate treatments to install to reduce the number and severity of 

crashes. 

4.1. Types of Countermeasure Implementation

To make the most informed decisions regarding countermeasure 

selection, an agency should begin with the crash history data when 

available. Common crash types, the severity of the crashes, and the 

location of those crashes are important attributes of crash data for the 

countermeasure selection process. 

There are three main types of countermeasure implementation 

approaches – systematic, spot location and comprehensive. The 

quality and availability of data can assist in determining the most 

appropriate implementation approach. The spot location approach is 

the most dependent on data availability and quality. The systematic 

implementation of safety countermeasures may be the most 

effective approach for those roadways that lack comprehensive data. 

Systematically applying safety treatments based on factors other than 

crash location can prevent future crashes for locations that have yet to 

experience them.

If roadway characteristic information is known, systematic solutions 

can be installed effectively, even if there are gaps in the crash data. 

Analysis shows that, in some situations, a high proportion of crashes 

tend to occur at locations that share common geometric or operational 

elements.12 Installing the same countermeasure at multiple locations 

(where appropriate) could be an effective strategy to improve safety.

4.1.1. Systematic Approach

The basis for the systematic approach is not on specific crash locations, 

but on crash types and proven low-cost safety countermeasures. In 

one application of the systematic approach, common crash types are 

determined and countermeasures are selected that address the specific 

types. The network is screened for locations experiencing those crash 

types or that have the potential for them to occur. Selected safety 

solutions are then installed systematically at identified locations.

12 Low-Cost Safety Enhancements for Stop-Controlled and Signalized Intersections, Federal Highway 

Administration, FHWA-SA-09-020, May 2009.
Road Safety Information Analysis | 26

Another application of the systematic approach begins with the 

selection of low-cost, effective countermeasures to common traffic 

safety issues. Once strategies are identified, the crash data system is 

searched to find locations where the countermeasure can be costeffectively deployed. Estimates of the impacts of implementation can be 

made in terms of deployment cost and the benefits measured in traffic 

crash reduction. 

Benefits of the systematic approach may include:

•	 Widespread effect. The systematic approach can impact safety issues at 

a large number of locations on an entire local roadway network.

•	 Crash Prevention. Jurisdictions can address potential future crashes 

using this approach. Using predominant crash types with a high or 

moderate level of crashes, an agency can address locations that have not yet 

experienced these crash types, but have similar characteristics to locations 

with such crash histories.

•	 Cost-effectiveness. Implementing low-cost solutions across an entire 

system can be a more cost-effective approach to addressing system-wide 

safety.

•	 Reduced data needs. The systematic approach can be used without a 

detailed crash history, reducing data needs. 

Drawbacks of the systematic approach may include:

•	 Justifying improvements can be difficult. Because there is 

not always a direct correlation between crash location history and 

recommended treatments, it can be difficult to justify improvements at 

locations without crash history. The systematic approach will rarely include 

a recommendation for a large-scale safety improvement at a single location. 

Since these are the types of projects that often garner attention from 

decision makers, the media, elected officials, and the general public, it can 

require additional effort from safety professionals to explain the systematic 

approach and its benefits to those groups.

4.1.2. Spot Location Approach

The spot location approach has typically been based on an analysis 

of crash location history. Due to the fact that some locations in a 

jurisdiction will likely have a significantly higher number of crashes than 

most of the others, it is important to identify those locations and treat 

them accordingly. 
27 | Road Safety Information Analysis

The benefits to the spot location approach may include:

•	 Focus on Demonstrated Needs. The spot location approach focuses 

directly on locations with a history of crashes. 

Drawbacks of the spot location approach may include:

•	 Assumption that the past equals the future. This approach assumes 

locations with a history of crashes will continue to have the same number 

and type of crashes in the future, if not treated.

•	 Minimal overall benefit. This approach often focuses on specific 

locations with crash history. As a result, potential benefits are limited. 

The spot location approach to traffic safety can be implemented 

in parallel with the systematic approach to provide a combination 

of safety treatments in a jurisdiction. In addition, the spot location 

approach could be applied to those locations that have had low cost 

countermeasures installed systematically, but continue to show a higher 

than average crash rate.

4.1.3. Comprehensive Approach

The comprehensive approach introduces the concept of the “4 Es of 

Safety”:

•	 Engineering;

•	 Enforcement; 

•	 Education; and 

•	 Emergency Medical Services.

This approach recognizes that not all locations can be addressed solely 

by infrastructure improvements. 

Some locations will be identified that have frequent driving violations 

for which targeted enforcement is an appropriate countermeasure. 

In general, human behavior issues (e.g., speeding, aggressive driving, 

failure to wear safety belts, driving while impaired) are a factor in a high 

percentage of crashes. When locations are identified that have reports 

and observations of these violations, coordination with the appropriate 

law enforcement agencies is needed to deploy visible targeted 

enforcement to reduce the potential for future driving violations and 

related crashes. Additionally, educational efforts should supplement 

enforcement to improve the effect of each. 

Using the example of County Road 220, analysis showed that five of 

the 12 crashes involved alcohol on a weekend evening. In this case, the 

practitioner should coordinate with the local law enforcement agency to 

consider additional alcohol-focused enforcement on the weekends on 

this roadway.
Road Safety Information Analysis | 28

4.2. Incorporating Roadway Characteristics

Countermeasure selection is a data-driven process, focused on not 

only crash history, but also on roadway elements to determine the 

appropriate strategies. Roadway configuration contributing to crashes 

is often recorded in the crash database and/or police reports. Through 

examination of the reports or through field reviews these can be 

identified and factored into the countermeasure selection process. 

For example, County Road 220 had 12 crashes in the past 5 years. 

Further study of the crash database indicated that eight of those 12 

crashes occurred at intersections. With this information available, the 

practitioner can implement countermeasures that address safety issues 

at intersections on this route, and potentially at similar locations on other 

routes within the jurisdiction.

Additional information on data analysis for selecting countermeasures to 

be considered can be found in Appendix B.

4.3. Countermeasure Evaluation

It is important to evaluate traffic safety treatments after installation to 

determine their effectiveness. The effort that goes into conducting the 

assessment will help guide future decisions regarding safety treatment 

implementation.

A record of crash histories and countermeasure installations forms 

the foundation for assessing how well implemented strategies 

have performed. It is important to keep a current list of installed 

countermeasures with documented “when/where/why” information. 

Periodic assessments will provide the necessary information to make 

informed decisions on whether each countermeasure contributed to 

an increase in safety, whether the countermeasure could or should be 

installed at other locations, and which factors may have contributed to 

the strategy’s success.

To perform the assessment it is necessary to collect the required 

information for a certain amount of time after strategies were deployed 

at the intersection. The time period varies, but should be no less than 

one full year (with 3 years preferred). The most important information 

is crash data before and after implementation. Other information 

required may consist of public input and complaints, police reports, and 

observations from maintenance crews. 

It is important to keep the list of deployed strategies up to date since 

it will serve as a record of countermeasure history. By using this type of 

system, assessment dates can be scheduled to review the crashes and 

other pertinent information at locations where treatments have been 

installed. 
29 | Road Safety Information Analysis

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Road Safety Information Analysis | 30

5 Case Studies

This section contains descriptions of programs that illustrate the 

methods and processes used by State, district, and county staff to 

collaborate on improving data access and methods for conducting data 

analysis. These programs illustrate how improvements can be made. 

More examples are included in the sources listed in Appendix B.

The examples are shared in increasing order of the complexity of the 

data analysis method. The first two are basic examples using relatively 

simple equations to analyze route segments and intersections by the 

number and severity of traffic crashes. The third example provides an 

intermediate level of analysis, including crash rates in the calculations. 

The final example is more advanced, incorporating methodology from 

the recently-published Highway Safety Manual (HSM).
31 | Road Safety Information Analysis

Basic Example:

Analysis of Roadway Departure Crashes on Local Roads

Oregon DOT

Description

In 2010 the Oregon DOT and FHWA began a project to analyze roadway 

departure crashes on both State and local roads. The result of the study 

would be a Roadway Departure Safety Implementation Plan designed to 

reduce roadway departure crashes.

In Oregon, spot location data (by milepost or GPS coordinate) was 

available for crashes that occur on State-maintained routes. However, 

this level of detail was unavailable for crashes occurring on locally 

maintained roadways. Crashes could be located on a certain roadway, 

but not at a point on that roadway. At the time of the data analysis, 

neither traffic volume counts nor the length of each roadway were 

available for local roads.

Methodology (Basic)

Oregon faced two significant limitations to the ability to analyze their 

roadway departure crash data on the local system. First, the lack of 

specific location and roadway attribute information on local roads made 

it impossible to pinpoint specific curves or sections of roadway for 

treatment. Second, not having traffic counts or roadway lengths made it 

impossible to calculate crash rates.

Due to these limitations, it was necessary to focus instead on the 

available information. The local road crashes did have information on 

the contributing circumstances of the crash, including the following:

•	 Run off road right;

•	 Run off road left;

•	 Head-on; and 

•	 Opposite direction sideswipe.
Road Safety Information Analysis | 32

The crashes also included the following additional information:

•	 Occurrence on a curve or straight section;

•	 Wet, ice-covered, or snow-covered pavement; and 

•	 Lighting conditions (Day/Night).

Oregon DOT and FHWA used this information to identify systematic 

treatments for local roads, including curve signing and delineation, 

rumble strips, and tree removal. Cost estimates were based on an 

average length of 10 miles for each local road, which, when applied 

over the system, provided a sufficient estimate for preliminary program 

planning.

Next Steps

The Oregon DOT will proceed with low-cost safety treatments on the 

identified roadways based on the type of roadway departure crashes 

that occurred on each local road. They will also seek to improve data 

availability, starting with determining the length of each local roadway. 

Future efforts include determining the location of local road crashes 

along the roadway by coding all crashes by latitude and longitude.

Contact

Doug Bish

Oregon DOT

(503) 986-3594

douglas.w.bish@odot.state.or.us
33 | Road Safety Information Analysis

Basic Example:

Data Analysis at the Regional Planning Level

Mid-Ohio Regional Planning Commission

Description

Crash data in Ohio often lack important information, such as accurate 

locations, safety belt use, and contributing crash factors. Inconsistent 

location reporting also has compromised the ability to direct safety 

resources to the most hazardous locations. Law enforcement officers 

often indicate crash location by noting the closest intersection or nearest 

point on the road centerline file rather than by latitude and longitude. As 

a result, the State’s Strategic Highway Safety Plan (SHSP) identified traffic 

records as an area in need of improvement. The Mid-Ohio Regional 

Planning Commission (MORPC) took up the challenge to improve 

data collection by proposing an “Interagency Safety Partner Program” 

that would inform law enforcement of the need for high quality crash 

data and explain how these data are used to identify multidisciplinary 

transportation safety countermeasures.

MORPC partnered with local engineers, the Safe Communities program, 

the municipal court, and City of Columbus police to provide educational 

information regarding the current crash reporting process and discuss 

the balance between the high demands of reporting and the limited 

resources dedicated to it. Workshops were held with those groups to 

provide practical examples of work products that utilize crash statistics, 

which helped officers understand why information in crash reports is 

crucial for improving transportation safety. The RPC also explained how 

road safety improvements would reduce traffic crashes and potentially 

free up law enforcement resources. One key areas addressed was the 

lack of accurate crash data with regard to crash locations, crash types, 

and behavioral issues.

To address this issue on the state level, the Department of Public Safety 

developed an electronic crash reporting software that is made available 

for free to every law enforcement agency in Ohio, along with free 

training on how to use it. Currently about 25 percent of crashes are 

reported electronically by 150 agencies. 
Road Safety Information Analysis | 34

Methodology (Basic)

The data are used to create a list of Ohio’s high crash locations which are 

prioritized for consideration for treatments to reduce crashes, injuries, 

and fatalities. The methodology used is summarized below:

Figure 5. MORPC’s Ranking Steps.

Contact

Kerstin Carr

Manager, Active Transportation & Safety 

Mid-Ohio Regional Planning Commission

(614) 233-4163

kcarr@morpc.org

MORPC’s Ranking Steps

1) Determine top 100 intersections by frequency via GIS;

2) Calculate severity index and crash rate for each;

3) Provide ranks for each of these 3 variables, with 1 = worst location;

4) Add ranks to determine overall rank;

5) The intersection with the overall lowest score is considered the worst.

Crash frequency is defined as the total number of crashes occurring at each intersection.

Crash frequency = N N = Total number of crashes at the particular location

Crash severity index gives an indication of the crash severity at each intersection. Obviously, fatal 

and injury crashes are more severe than property-damage only (PDO) crashes and are weighted

accordingly in the following equation:

 F = Total number of fatal crashes at the particular location

 I = Total number of injury crashes at the particular location

 N = Total number of crashes at the particular location

Crash rate (per million entering vehicles) takes into account the total number of crashes

compared to the average traffic volume entering the intersection.

∑ADT = Sum of average daily traffic entering the intersection

N = Total number of crashes at the particular location

Severity Index = 12 * F + 3 * I +1 * PDO

N

Crash Rate = (∑ADT) * 3 years * 365 days * 10-6

N
35 | Road Safety Information Analysis

Intermediate Example:

High Risk Rural Roads Methodology with Less-than-perfect Data

New Jersey

Description

The High Risk Rural Roads Program (HRRRP) is an initiative of the Highway 

Safety Improvement Program (HSIP) in direct response to the unique 

challenges faced by local, rural road owners. Through this program, 

money is set aside for each state to construct and make operational 

improvements on high risk rural roads. In New Jersey, utilizing the 

authorized budget allocations has been difficult. The data necessary 

to calculate traditional crash rates are not available for local, rural roads, 

so the State could not determine what segments qualified under this 

program. 

Methodology (Intermediate)

New Jersey has many sources of good information, but it is incomplete 

for more advanced analysis. For example, there is a standardized crash 

report form that is compliant with the Model Minimum Uniform Crash 

Criteria (MMUCC)13 with an average 300,000 reportable crashes per year, 

but only 60 to 70 percent of the database entries have a map location. 

The state also has a roadway database (straight line diagrams) that 

is nearly compliant with the Model Inventory of Roadway Elements 

(MIRE)14, but most roads below the State level are missing traffic volume 

data. This makes calculation of traditional crash rates on rural roads 

difficult.

In 2007, the Transportation Safety Resource Center (TSRC) was asked 

to help brainstorm a way to determine segments that would qualify 

under the HRRRP funding given the State’s data limitations. TSRC 

developed an initial procedure that used functional class, speed limit, 

number of lanes, presence of shoulder, and crash locations. The output 

13 The MMUCC Guideline presents a model minimum set of uniform data elements for describing 

a traffic crash. States are encouraged to adopt as many recommended MMUCC data elements as 

possible when they next update their State’s official accident report. Additional information is 

available at http://www.mmucc.us.

14 The MIRE Guideline includes a listing of roadway inventory and traffic elements critical to safety 

management of all streets and highways across the country. By collecting roadway elements, a 

highway agency will have a more complete set of the safety and operations components of the 

streets and highways it owns and maintains. Additional information is available at

http://www.mireinfo.org.
Road Safety Information Analysis | 36

included segments labeled as high risk based on crash rates. In 2010, 

that procedure was updated to include crash severity and trending over 

six years of crash data. The following is the methodology used in New 

Jersey and approved by the FHWA Division office:

1. Divide all rural roads into similar cross-sections based on functional 

classification, number of lanes, speed limit, and presence of shoulders. This was 

done by the Metropolitan Planning Organization (MPO) for the State of New 

Jersey since three MPOs cover the entire state. 

a. Creating similar cross sections negates the need for AADT data since 

similar cross sections are assumed to have similar volumes of traffic. This 

assumption ameliorated the lack of AADT issue.

2. Individual crash rates were then calculated for each segment using the 

following formula:

Where:

CR = Crash rate.

F = Number of Fatal crashes.

II = Number of Incapacitating Injury crashes.

MI = Number of Moderate Injury crashes.

I = Number of Injury crashes.

PDO = Number of Property Damage Only crashes.

L = Length of segment in miles.

The above elements were taken from “Occupant Physical Condition” on 

New Jersey’s crash report form – the most severe value was assigned to 

the entire crash. If occupant physical condition was missing, the severity 

value of the crash was assigned (Fatal = F, Injury = I, PDO = PDO). In 

the case for injury, the lowest injury value was used; fatal and property 

damage only crashes remained the same.

CRsegment= 5(F) + 4(II) + 3(MI) + 2(I)+ PDO

L
37 | Road Safety Information Analysis

3. Next, an aggregate average for each cross-section was calculated. For 

example, all of the segments with the same functional class, speed limit, 

number of lanes and presence of shoulder were averaged to find a MPO 

average crash rate for that cross-section.

Functional

Class

# 

Lanes Speed Shoulder

Crash 

Rate

Fatal Injury PDO

7 2 30 0 16.318 1 12 26

7 2 30 1 9.524 0 1 3

7 2 35 0 7.965 0 4 5

7 2 35 1 5.392 0 1 10

7 2 40 0 9.355 0 8 22

7 2 40 1 7.695 0 18 62

7 2 45 0 7.611 2 29 79

7 2 45 1 11.869 2 104 219

Table 5. Partial listing of MPO crash rate averages by cross section

4. Lastly, individual segment rates were compared to the corresponding MPO 

average rate. If the individual rate was higher than the average rate, it was 

flagged as high risk. If it was lower, it was not. From this, an output of the 

high risk rural road segments was provided to each MPO to implement as 

they saw fit.

IF CR1 > CRMPO, then CR1 = high risk.

IF CR1 < CRMPO, then CR1 ≠ high risk.

CRMPO =

CR1 + CR2 + ...CRχ

χ
Road Safety Information Analysis | 38

Note: This can be done two ways depending upon available data:

1) The above procedure can be done for a three (3) year period.

2) Or, the above can be done to six (6) years, using the first three (3) years 

as ‘before’ and the last three (3) years as ‘after’. This yields the following 

breakdown of high risk segments:

a. Segments that were high risk before and after – worsening condition.

b. Segments that were only high risk after – worsening condition.

c. Segments that were high risk before and after – improving condition.

The benefit to doing this is to add information for prioritization purposes – 

making of short lists.

Next Steps

In order to proceed, it was necessary to gain approval from the FHWA 

Division Office for this new process. Once approved, an output list for 

each MPO was produced that included all roadway segments in their 

individual regions that were considered high risk. From there, each MPO 

was responsible for developing a short list that would be submitted for 

HRRR funds. An example of this success can be found in the NJTPA case 

study listed in FHWA’s report entitled Implementing the High Risk Rural 

Roads Program, FHWA-SA-10-012.15

Contact

Transportation Safety Resource Center

Center for Advanced Infrastructure and Transportation

Rutgers, the State University of New Jersey

(732) 445-3919

15 FHWA-SA-10-012 can be accessed at http://safety.fhwa.dot.gov/local_rural/training/fhwasa10012.
39 | Road Safety Information Analysis

Advanced Example:

Predictive Method Analysis using the Highway Safety Manual16

Missoula, Montana

Background, Issues, and Objectives:

The Russell Street corridor in Missoula, Montana, is 1.5 miles long, 

connecting residential and industrial uses across a river to the downtown 

business district. It is an important vehicle and bicycle commuter route. 

The average daily traffic volume along this route ranges from 20,000 

to 25,000 vehicles per day. The corridor has received funding for major 

geometric improvements. This study was conducted to evaluate the 

traffic operations and safety impacts of various design alternatives 

for the entire corridor. Several options were considered as part of the 

project, including converting the 2-3 lane roadway to a five-lane road, or 

converting the roadway to a three-lane road. Each case would include 

a mix of traffic signals and roundabouts at the intersections. This project 

example demonstrates the quantitative safety analysis of two alternatives 

on a small portion of the corridor. 

Data Requirements: 

Segments Intersections

•	 Segment Length (miles).

•	 Through Lanes (number).

•	 Median Type (divided/undivided).

•	 Median Width (feet).

•	 On-Street Parking (yes/no).

•	 Fixed Object Density (obj/mile).

•	 Average Offset of Fixed Objects (feet).

•	 Roadway Lighting (yes/no).

•	 Speed Limit (mph).

•	 Traffic Volume (veh/day).

•	 Number/Types of Driveways.

•	 Number of Intersection Legs (number).

•	 Traffic Control (signal, stop, roundabout).

•	 Left Turn Lanes and Phasing (protected, permitted, 

protected/permitted).

•	 Right Turn Lanes and Control of Right Turn 

(permitted on red, prohibited on red).

•	 Lighting (yes/no).

•	 Maximum Number of Traffic Lanes. Crossed by 

Pedestrians (number).

•	 Nearby Bus Stops, Schools, and Alcohol Sales 

Establishments (number).

•	 Entering Traffic Volumes (veh/day).

•	 Pedestrian Activity (yes/no).

16 Adapted from “An Introduction to the Highway Safety Manual,” AASHTO, 2010.
Road Safety Information Analysis | 40

Analysis Methodology Overview:

The crash frequency for each segment and intersection is predicted 

using an iterative 18-step method in Highway Safety Manual - Chapter 

12: Urban/Suburban Arterials. In summary, this method consists of 

initially calculating multiple- and single-vehicle fatal/injury and property 

damage only crashes; these values are added to obtain base predicted 

vehicle crashes. The next step is to adjust the base predicted vehicle 

crashes with crash modification factors (CMFs) based on the roadway 

characteristics. Finally, this value is added to predicted bicycle and 

pedestrian crashes. If a calibration factor was available, or historical data 

was available to apply the Empirical Bayes method, these two steps 

would be included. The base equation and a sample calculation using 

the base equation for prediced average crash frequency is shown below. 

Sample calculations are shown for the Main Street/3rd Street intersection 

no-build conditions. 

Nbi = Nspf int x (CMF1i x CMF2i x ... CMF6i) x C

Nbi = 12.97 x (0.66 x 0.96 x 0.88 x 1.00 x 0.91 x 1.00)=6.63 crashes/

year.

Where:

Nbi = Predicted average crash frequency for an intersection. 

Nspf int = Predicted average crash frequency for base conditions

(Nspf int = 12.97, see below).

CMF1i – CMF6i = Crash modification factors for left-turn lanes

(CMF1i = 0.66), left-turn phasing (CMF2i = 0.96), right-turn lanes

(CMF3i = 0.88), right-turn-on-red (CMF4i = 1.00), lighting

(CMF5i = 0.91), and red light camera (CMF6i = 1.00).

C = Calibration factor (C = 1.00).
41 | Road Safety Information Analysis

Note, as this is a multi-step process there are multiple equations that 

are used to calculate Nspf int, these steps are not detailed in this example. 

A sample equation used in that process for the Main Street/3rd Street 

intersection no-build condition is illustrated below. Results are shown in 

Table 6.

N’bimv(FI) = exp(a + b x ln(AADTmaj) + c x ln(AADTmin)) 

N’bimv(FI) = exp(-13.14 + 1.18 x ln(33,910) + 0.22 x ln(25,790)) =

4.07 crashes/year.

Where:

N’bimv(FI) = Multiple vehicle intersection fatal/injury crashes.

a, b, and c = Regression coefficients (-13.14, 1.18, and 0.22 for 4-leg 

signalized intersections).

AADTmaj = Annual average daily traffic on major road (33,910).

AADTmin = Annual average daily traffic on minor road (25,790).

Results:

•	 Changes in crash frequencies are quantified and compared to the no-build 

scenario, as shown in Table 6. The resulting forecast crash frequencies for 

Alternatives 1 and 2, 15.7 and 25.8 crashes respectively, are compared to the 

no-build crash frequency, 29.6. The difference is quantified as a percentage. 

•	 The change in crash frequency can now be considered as one of the tradeoffs similar to traffic operations, environmental impacts and pedestrian and 

bicycle mobility. 
Road Safety Information Analysis

| 42

2035 Forecast Crash Frequency (Crashes/Year1)

No-Build Alternative 1 (Mix 3 and 5-Lane) Alternative 2 (5-Lane)

Intersection/ Segment2 Facility AADT4 Crashes/ Year Facility AADT4 Crashes/ Year Facility AADT4 Crashes/ Year

Int: Main & Oak Stop 35,730/ 

3,650

3.26 Roundabout 35,730/ 

3,650

1.67 Signal 39,080/ 

5,280

6.93

Seg: Oak to 3rd St. 3-Lane 34,580 8.30 3-Lane 34,580 5.74 5-Lane 38,150 9.32

Int: Main & 3rd Signal 33,910/ 

25,790

6.63 Roundabout 33,910/ 

25,790

3.43 Roundabout 36,900/ 

29,400

3.86

Seg: 3rd to 5th 5-Lane 33,270 5.05 5-Lane 33,270 1.51 5-Lane 37,310 1.74

Int: Main & 5th Signal 33,200/ 

5,940

6.40 Roundabout 33,200/ 

5,940

3.32 Roundabout 37,860/ 

7,230

3.99

UNCALIBRATED Total Prediction 29.6 15.7 25.8

Change Relative to No-Build 47% Decrease 13% Decrease3

1 Total Crashes Per Year2 For the purposes of presenting the results, crashes estimated for minor street intersections along the two segments (Oak St. to 3rd St. and 3rd St. to 5th St.) were added into the segment crash totals.3 Under the 5-lane scenario, the corridor has more capacity, therefore more regional traffic is drawn to this corridor. The decrease shown is for overall crashes, so a normalized analysis would show a 

slightly greater decrease.

4 Major Street AADT/Minor Street AADT for intersections

Table 6. Forecast Crash Frequency for Russell Street Project Alternatives
43 | Road Safety Information Analysis

6.Summary

Highway safety decisions are generally made based on data. Regulations 

for safety-related Federal funding and safety plan development require 

data for the decision making process. Recent national initiatives, such as 

State-level Strategic Highway Safety Plans (SHSPs) and the High Risk Rural 

Roads Program (HRRRP) have contributed to improved rural road safety 

by fostering the implementation of effective countermeasures. One 

aspect of this Federal support is an emphasis on data-driven processes to 

help jurisdictions determine the best course of action for making safety 

decisions on local road networks. For example, the HRRRP requires that 

rural major collectors, minor collectors, and local roads exhibit a history 

of crashes above the statewide average or are expected to experience 

a higher-than-average number of crashes in the future to be eligible for 

Federal funding.

At the local level, these data requirements can seem daunting, 

particularly as they relate to funding and staff time needed for data 

collection, analyses, and overall assessments of safety improvements. It 

is important to note that a data-supported approach can increase the 

effectiveness of the distribution of limited funds to improve safety on 

local rural roads.

A comprehensive set of crash data is desirable for effective roadway 

analysis and countermeasure selection, but a lack of such databases 

should not deter road safety practitioners from addressing safety issues 

on their roadways. The types of information that should be collected 

for roadway safety analysis include crash data (location, type, severity, 

roadway conditions, weather, time of day, day of week, month), exposure, 

and roadway elements. The primary sources of these data are local law 

enforcement crash reports, emergency medical service information, and 

State and Federal databases. In addition, observational data often serves 

as an important supplement to formal data sources.

Collected data should be analyzed to identify locations with safety 

issues or locations with potential for safety issues, and to select 

countermeasures to improve safety. Depending on the completeness, 

accuracy, and timeliness of available data, a local jurisdiction can 

analyze that information in a number of ways. Analyses can range 
Road Safety Information Analysis | 44

from simple cluster analysis to more advanced calculations of crash 

rates, determination of crashes by type and severity, and analysis of 

combinations of contributing factors and roadway elements. By using 

a data-driven approach, local practitioners will have the information to 

make informed decisions about the type and location of strategies to 

optimize effectiveness. Many states have developed data analysis tools; 

these are often shared with local practitioners to assist their local road 

safety programs. 

Crash data, when available, can assist practitioners in making the 

most informed decisions regarding countermeasure selection. 

Common crash types, the severity of the crashes, and the location 

of those crashes are some important attributes of crash data for the 

countermeasure selection process. There are three main types of 

countermeasure implementation approaches – spot location, systematic, 

and comprehensive. The quality and availability of data can assist in 

determining the implementation approach undertaken. The spot 

location approach is the most dependent on data availability and quality. 

The systematic implementation of safety countermeasures may be the 

most effective approach for those roadways that lack comprehensive 

data. Analysis shows a high proportion of crashes tend to occur at 

locations that share common geometric or operational elements. 

Installing the same countermeasure at multiple locations, where 

appropriate, could be an effective strategy to improve safety on the 

overall network.

The effectiveness of specific road safety countermeasures is important 

to an overall safety program at any level, due to resource limitations 

of highway agencies. The assessment will help guide future decisions 

regarding the selection and implementation of safety countermeasures. 

Looking at the number and type of crashes before and after the 

implementation of a safety strategy will provide a basic effectiveness of 

the strategy. However, to understand the full effect, changes in traffic 

volumes and roadway attributes should be considered. 

Local highway agencies have unique responsibilities and challenges 

related to the safety of their roadway system. By beginning any traffic 

safety effort by first looking at the data, those agencies will be in a better 

position to address their highway safety needs.
A-1 | Road Safety Information Analysis

Appendix A. State Crash Report Example
Road Safety Information Analysis | A-2
A-3 | Road Safety Information Analysis
Road Safety Information Analysis | A-4
B-1 | Road Safety Information Analysis

Appendix B. Resources

Analytical Tools/Methods

Federal Highway Administration, Highway Safety Facts and Statistics 

website. Available at http://safety.fhwa.dot.gov/facts_stats/.

Green, E.R. and Agent, K.R., Crash Rates at Intersections, University of 

Kentucky College of Engineering, Kentucky Transportation Center. 

Research Report KTC-03-21/SPR258-03-2I (2003). Available at

http://www.ktc.uky.edu/Reports/KTC_03_21_SPR258_03_2I.pdf.

Gross, F. and Yunk, K. U.S. “Using CRFs to Improve Highway Safety.” 

Department of Transportation, Federal Highway Administration, Public 

Roads, May/June 2009, pp. 26-31. Available at

http://www.fhwa.dot.gov/publications/publicroads/09june/04.cfm.

Federal Highway Administration, “Desktop Reference for Crash Reduction 

Factors.” (Washington, DC: 2007). Available at

http://www.ite.org/safety/issuebriefs/Desktop%20Reference%20Complete.pdf.

Federal Highway Administration, Crash Reduction Factors website. 

Available at http://safety.fhwa.dot.gov/tools/crf/.

Federal Highway Administration, Crash Modifications Factors 

Clearinghouse website. Available at http://www.cmfclearinghouse.org/.

NCHRP Report 500 Series: Guide for Implementation of the 

AASHTO Strategic Highway Safety Plan

Relevant information is assembled into single concise volumes, each 

pertaining to specific types of highway crashes or contributing factors. 

Data-focused volumes include:

"Volume 19: A Guide for Collecting and Analyzing Safety Highway 

Safety Data.” http://safety.transportation.org/guides.aspx?cid=40.

"Volume 21: Safety Data and Analysis in Developing Emphasis Area 

Plans” http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_500v21.pdf.

“NCHRP – Synthesis 321: Roadway Safety Tools for Local Agencies: A 

Synthesis of Highway Practice”

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_321.pdf.
Road Safety Information Analysis | B-2

Databases

National Highway Traffic Safety Administration, Center for Statistics and 

Analysis, Fatality Analysis Reporting System (FARS). Available at 

http://www-fars.nhtsa.dot.gov/Main/index.aspx.

National Highway Traffic Safety Administration, Crash Outcome Data 

Evaluation System (CODES). Available at

http://www.nhtsa.gov/Data/State+Data+Program+&+CODES.

State and Local Programs and Initiatives

Iowa Department of Transportation. Crash Analysis. Accessed at:

http://www.intrans.iastate.edu/pubs/traffichandbook/5CrashAnalysis.pdf.
For More Information

Office of Safety

Federal Highway Administration

1200 New Jersey Ave, SE

Washington, DC 20590-9898

http://safety.fhwa.dot.gov

FHWA-SA-11-10