UNIT 3: Measuring Safety

Road Safety 

Fundamentals

Concepts, Strategies, and Practices that 

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

FHWA-SA-18-003

4. Title and Subtitle

Road Safety Fundamentals:

Concepts, Strategies, and Practices that Reduce Fatalities and Injuries

on the Road

7. Author(s)

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

Unit Authors: 

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

Research Center 

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

Center 

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

Research Center 

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

Engineer; Daniel Carter 

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

Manager, UNC Highway Safety Research Center

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

Safety Research Center

Technical Editors: Jennifer Palcher-Silliman; Patty Harrison, 

Communications Specialist, UNC Highway Safety Research Center; 

Caroline Mozingo, Senior Manager of Communications, Education and 

Outreach, UNC Highway Safety Research Center

9. Performing Organization Name

and Address

University of North Carolina,

Highway Safety Research Center

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety

1200 New Jersey Ave., SE

Washington, DC 20590

14. Sponsoring Agency Code

15. Supplementary Notes

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

16. Abstract

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

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

rather than providing instruction in detailed analysis procedures.

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

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

students in a university setting.

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

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

disciplines will benefit from the concepts presented here.

17. Key Words

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

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

data, multidisciplinary approaches

18. Distribution Statement

No restrictions

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(of this report)

Unclassified

20. Security Classif.

(of this report)

Unclassified

21. No. of Pages

188

22. Price

FREE

5. Report Date

November 2017

6. Performing Organization Code

8. Performing Organization Report No.

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11. Contract or Grant No.

13. Type of Report and Period Covered

Text book

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

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

TABLE OF CONTENTS

ROAD SAFETY FUNDAMENTALS i

UNIT 3 Measuring Safety Chapter 7: Importance of Safety Data

 Chapter 8: Types of Safety Data

 Chapter 9: Improving Safety Data Quality

UNIT 4 Solving Safety Problems

 Chapter 10: Road Safety Management Process

 Chapter 11: Site-Level Safety Management

 Chapter 12: System-Level Safety Management

UNIT 5 Implementing Road Safety Efforts

 Chapter 13: Who Does What

 Chapter 14: Road Safety Research

 Chapter 15: Strategic Communications

 Chapter 16: Advancing Road Safety

UNIT 1 Foundations of Road Safety

 Chapter 1: Context of Road Safety

 Chapter 2: Road Safety Through the Years

 Chapter 3: Multidisciplinary Approaches

 Chapter 4: Road Users

UNIT 2 Human Behavior and Road Safety

 Chapter 5: Understanding Human Behavior

 Chapter 6: Changing Human Behavior
3-b UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

After reading the chapters and completing 

exercises in Unit 3, the reader will be able to:

J DESCRIBE why measuring safety 

is important

J IDENTIFY the different types 

of available data

J UNDERSTAND the challenges 

and accuracy of data

J SELECT data for different 

road safety objectives

LEARNING OBJECTIVES

Measuring 

Safety

UNIT 3
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-1

Importance of Safety Data

Good quality safety data are the core 

of any successful effort to improve 

road safety. Local, State, and Federal 

agencies use crash data as well as 

roadway, vehicle, driver history, 

emergency response, hospital, and 

enforcement data to improve road 

safety. All of these data sources can 

be used, in isolation or jointly, to 

produce projects, programs, and 

guide policies that reduce injuries 

and save lives. These types of data 

are collectively categorized as safety 

data in this book. 

Safety professionals in many 

disciplines – highway design, 

transportation planning, 

operations, road maintenance, 

law enforcement, education, 

emergency response services, 

policy makers, infrastructure 

program management, road safety 

management, and public health – 

use safety data to identify problem 

areas, select countermeasures, and 

monitor countermeasure impact.

Road safety management and 

project development has become 

increasingly data-driven and 

evidence-based. This approach 

to road safety emphasizes safety 

performance (i.e., number of 

crashes), rather than solely adhering 

to engineering standards, personal 

experience, beliefs, and intuition. 

For example, in the past, road 

improvements were considered 

“safe” if the improvements met 

the standards contained in the 

Manual on Uniform Traffic Control 

Devices (MUTCD) and A Policy on 

Geometric Design of Highway and 

Streets, also known as the Green 

Book1,2. However, most of these 

standards are engineering based 

(i.e., nominal safety as discussed 

in Unit 1), and were not necessarily 

based on an evaluation of actual 

road safety performance. Presently, 

transportation professionals use 

safety data (such as crash data, 

road characteristics, and traffic 

volume) to evaluate road safety 

performance and inform their 

decisions. This substantive approach 

challenges professionals to quantify 

the expected consequences and 

outcomes of safety strategies in real 

measurements, such as the expected 

number of crashes, injuries, 

and fatalities. 

The selection of road safety 

measures and treatments can 

benefit from an understanding of 

the intricacies and limitations of 

safety data. This unit presents many 

kinds of safety data, explores the 

current process used to collect data, 

and discusses the impact that these 

processes have on data quality (i.e., 

accuracy and reliability). The unit 

also discusses ways to improve data 

quality and analysis.

Relating Nominal and 

Substantive Safety to Data

The concepts of nominal and 

substantive safety were first 

introduced in Unit 1 of this textbook. 

Nominal safety refers to whether 

CHAPTER 7 MEASURING SAFETY

Manual on Uniform 

Traffic Control 

Devices (MUTCD), 

Federal Highway 

Administration, 

2009.

A Policy on 

Geometric Design 

of Highways and 

Streets, American 

Associations of 

State Highway 

Transportation 

Officials, 6th 

edition, 2011.

1

2
3-2 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

or not a design (or design element) 

meets minimum design criteria 

based on national or State standards 

and guidance documents, such as 

the AASHTO Green Book or the 

MUTCD. Substantive safety refers to 

the actual safety performance, such 

as expected number of collisions by 

type and severity on a road.

The contrast of these concepts is 

directly linked to this discussion of 

safety data. To determine if a road 

is nominally safe we do not need 

safety data; we only need to know if 

all design standards were followed. 

However, we need high quality 

safety data and data analysis to 

determine if a road is substantively 

safe. Typically, the analysis includes 

estimating the expected number of 

crashes and comparing it against the 

road’s actual safety performance. 

More information on safety analysis 

is presented in Unit 4, Solving Safety 

Problems.

Use of Safety Data in Road 

Safety Management

Data are integral to safety decision 

making, both in prioritizing 

investments and in identifying 

analyzing the most effective 

techniques and interventions. 

The more comprehensive and 

accurate the data, the better the 

resulting decisions. Understanding 

contributing factors to crashes and 

how best to implement potential 

countermeasures is complex, and 

it may involve a variety of agencies 

and historical data challenges. 

Because of this complexity, both 

accurate data and high quality data 

analysis is necessary for road safety 

management. A great database is 

only as useful as the analysis and 

application of that data. Table 3-1 

explores the relationship between 

data quality and data analysis quality 

and shows why agencies should 

strive to improve both of these areas. 
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-3

Crash data analysis using 

quantifiable metrics and 

scientifically defensible methods can 

help decision makers improve road 

safety by reducing more injuries and 

saving more lives at a lower cost. 

Accurate crash data help determine 

crash and severity trends, such as 

increases or decreases in certain 

types of crashes. Data also help 

safety professionals pinpoint high 

crash locations and identify highrisk users, such as younger drivers, 

older drivers, impaired drivers, 

and motorcyclists. Examining 

the characteristics of crashes 

allows road safety professionals to 

identify contributing crash factors 

related to roadway environment, 

design, or behavioral adaptations. 

This type of analysis will lead 

to a more effective selection of 

countermeasures that will reduce 

future crash occurrences or crash 

severity. Planners and engineers can 

use crash data to show quantitative 

information to decision makers on 

how specific planning guidance, 

design proposals, or engineering 

countermeasures can save lives. 

Safety professionals could seek to 

improve safety by relying merely on 

their gut judgment. The results of 

such an approach, however, would 

be quite unreliable. As shown in 

Table 3-1, safety professionals can 

improve their decision making 

process by using high quality data 

together with robust analysis 

processes. This unit will focus on 

the data itself. The use of the data in 

safety management is presented and 

discussed in Unit 4.

Good quality safety data and 

analysis are the keys to identifying 

real safety issues on roads and 

evaluating the best methods for 

improving safety. The following 

chapters provide an overview of 

different types of safety data and of 

ways in which agencies can improve 

the quality of their data. 

TABLE 3-1: Data and Analysis Quality Comparison

HIGH QUALITY ANALYSIS LOW QUALITY ANALYSIS

HIGH QUALITY DATA

BEST CAS

The agency is likely to reach the best 

safety decisions. Analysts are aware 

of data capabilities and limitations. 

This is the most expensive to achieve, 

due to the need for good data and 

training on how to conduct analyses.

MISSED OPPORTUNITY

The agency needs to invest in high 

quality analysis. Otherwise, the agency 

has wasted money in databases 

that are not being utilized to their 

potential. Good data with poor 

analysis will lead to poor decisions. 

LOW QUALITY DATA

PROMISING

A robust analysis that recognizes 

the limitations of the data can 

still produce useful results. 

The agency should focus on 

improving data quality.

WORST CASE

Poor data and poor analysis will lead 

to bad decisions. The agency may be 

better off relying on judgment.
3-4 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

As highway safety analysis 

methods continue to evolve, it 

is equally important to focus on 

quality data to conduct these safety 

analyses. Transportation agencies 

can and should incorporate road 

characteristics, traffic volume, and 

enforcement and citation data, 

and other information into their 

safety analysis processes. This will 

enable them to better identify safety 

problems and prescribe solutions 

that improve safety and make more 

efficient use of safety funds.

Single sources of safety data also 

do not give a complete picture 

of the safety risks on our roads. 

For example, using crash data by 

itself leaves safety practitioners 

with purely reactive approaches—

identifying locations where 

crashes have already happened. 

By combining crash data with 

other types of data, more details 

begin to emerge. For example, by 

combining crash data and detailed 

road inventory information, safety 

practitioners can develop a more 

in-depth understanding of the road 

attributes that contribute to crash 

risk. This will allow them to adopt 

a proactive approach, seeking out 

those factors associated with a high 

risk of crashes and addressing sites 

that share those “elements” before a 

crash occurs.

Crash, roadway, and traffic data 

should be integrated or combined 

using common or “linking” 

reference systems, such as mileposts 

or geospatial position. These data 

should also have the ability to be 

linked to the State’s other road 

safety databases, including citation 

data or injury surveillance systems. 

Additionally, commercial motor 

vehicle data could also be linked 

based upon common data elements 

involved in crashes and inspections. 

Not all types of safety data are 

available or used by all practitioners. 

Safety data exist in distinct 

databases that are maintained by 

different agencies and often are 

accessible only to those agencies. 

One role for safety professionals is 

to bring together safety databases 

Types of Safety Data

CHAPTER 8 MEASURING SAFETY

Roadway 

elements

Physical 

features of the 

road such as 

travel lanes, 

shoulder width, 

pavement 

condition, 

and roadside 

characteristics

Chicago’s Use of Injury Data 

to Benchmark Safety Goals 

and Progress

Chicago DOT completed a 

comprehensive pedestrian crash 

analysis in 2011 to inform the citywide 

Chicago Pedestrian Plan. This analysis 

evaluated various crash types, 

contributing environmental factors, and 

different age groups using the Illinois 

Department of Transportation crash 

data files. The findings present crash 

density citywide, by ward, and around 

schools. The data also highlighted 

key crash conditions and served as a 

benchmark for measuring the City of 

Chicago’s road safety goals.

Reference: City of Chicago 2011 Pedestrian 

Crash Analysis, Summary Report, 

Chicago Department of Transportation, 

Accessed September 2016 at https://www.

cityofchicago.org/city/en/depts/cdot/supp_

info/2011_pedestrian_crashanalysis.html
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-5

and analyze them using logical and 

statistically robust processes. 

Safety data can be categorized into 

two groups based on criteria of core 

data needs for safety evaluations, 

data availability, accuracy, and 

usefulness to safety practitioners 

and researchers. Some safety data 

are used often and are critical to 

safety analysis for many agencies. 

Other safety data are used less 

often but can be supplemental to 

specific safety analyses. This chapter 

provides general information on 

safety data in these two groups:

Critical data

J Crashes

J Traffic volume

J Road characteristics

Supplemental data

J Conflicts and 

avoidance maneuvers

J Injury surveillance and 

emergency medical systems

J Driver history

J Vehicle registrations

J Citations and enforcement

J Naturalistic

J Driving simulator

J Public opinion

J Behavioral observation 

Crash Data

Description

Crash data is the most widely used 

type of safety data, and it is essential 

in road safety analysis. Crashes 

are currently viewed as the most 

objective and reliable measurements 

of road safety. However, there 

are challenges with crash data, 

such as human error in reporting, 

unreported crashes, and the length 

of time it often takes for crashes to 

be entered into a database. Crash 

data is also the primary measure 

of effectiveness for safety efforts, 

since the goal is to decrease crash 

occurrences and lower the severity of 

crashes that do occur. Crash records 

typically provide details on events 

leading to the crash, vehicles, and 

people involved in crashes, as well as 

the consequences of crashes, such as 

fatalities, injuries, property damage, 

and citations. 

Data collection process

Crash data collection begins when a 

State highway patrol trooper or local 

police officer arrives at the crash 

scene. The officer completes a crash 

report, documenting the specifics 

of the crash. While the specifics 

and level of detail of the crash data 

vary from State to State, in general, 

the most basic crash data consist of 

where and when the crash occurred, 

what type of crash it was, and who 

was involved. The specific data 

collected on crashes is determined 

by State agencies, local government 

agencies, and often a coalition of law 

enforcement agencies. The exact data 

fields and coding differ from State to 

State. The level of detail in a crash 

report may also differ by the severity 

of a crash. For instance, in some 

States property damage only crashes 

(PDO) are self-reported and, thus, 

often have less information than 

injury crashes, which are reported by 

law enforcement officers. 

States also differ in the threshold 

of what is required for a crash to be 

reported. Reporting of crashes can 

vary by threshold requirements, such 

as “only injury crashes” or “PDO 

crashes over an estimated $2,000 

in damage.” These thresholds are 
3-6 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Case identifier: The unique identifier 

within a given year that identifies a 

Crash county: The county or equivalent specific crash within a State.

entity where the crash physically occurred.

A

Contributing 

circumstances, 

road: Apparent 

condition of the 

road that may 

have contributed 

to the crash.

Light 

conditions: 

The type/level of 

light that existed 

at the time of 

the motor 

vehicle crash.

Relation to junction: The coding of 

this data element is based on the location 

of the first harmful event of the crash. It 

identifies the crash’s location with respect 

to presence in a junction or proximity to 

components typically associated with 

junction or interchange areas.

Roadway 

surface 

conditions: The 

roadway surface 

condition at the 

time and place 

of a crash.

Type of intersection: An intersection 

consists of two or more roadways that 

intersect at the same level.

Contributing 

circumstances, 

environment: 

Apparent 

environmental 

conditions 

which may have 

contributed to 

the crash.

Weather 

conditions: 

The prevailing 

atmospheric 

conditions that 

existed at the 

time of the 

crash.

Crash location: The exact location on 

the road where the first harmful event 

of the crash occurred. It is best if this 

information includes a geolocation based 

on a Geographic Information System 

(GIS) or Linear Referencing System (LRS) 

location coordinates.

Crash city/place: City/place (political 

jurisdiction) in which the crash occurred.

Crash date and time: The date (year, 

month, and day) and time (00:00-23:59) 

when the crash occurred. 

Crash classification: Used to identify 

ownership of the land where the crash 

occurred and identify the characteristics of 

the crash with respect to its location on or 

off a trafficway.

D

A

D,E,F,G,H

Driver 

information

Vehicle 

information

E

F

G

H

C

B

B,C

I,J

K,L,M

I

K

M

L

J
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-7

FIGURE 3-1 (above, left): Data elements on a crash report form. (Source: North Carolina DOT)

First harmful event: The first injury or 

damage-producing event that 

characterizes the crash type.

Location of first harmful event relative 

to the trafficway: The location of the first 

harmful event as it relates to its position 

within or outside the trafficway.

Manner of crash/collision impact: The 

identification of the manner in which two 

motor vehicles in transport initially came 

together without regard to the direction 

of force. This data element refers only 

to crashes where the first harmful event 

involves a collision between two motor 

vehicles in transport.

Work zonerelated: A crash 

that occurs in 

or related to a 

construction, 

maintenance, or 

utility work zone, 

whether or not 

workers were 

actually present 

at the time of 

the crash. Work 

zone-related 

crashes may also 

include those 

involving motor 

vehicles slowed 

or stopped 

because of 

the work zone, 

even if the first 

harmful event 

occurred before 

the first 

warning sign. School bus-related: Indicates whether 

a school bus or motor vehicle functioning 

as a school bus for a school-related 

purpose is involved in the crash. The 

school bus, with or without a passenger 

on board, must be directly involved as 

a contact motor vehicle or indirectly 

involved as a non-contact motor vehicle 

(children struck when boarding or 

alighting from the school bus, two vehicles 

colliding as the result of the stopped 

school bus, etc.).

Source of 

information: 

Affiliation of 

the person 

completing the 

crash report.

O

Q

Q

R

R

S

S

N

P

Crash sketch/

diagram

Crash 

narrative

N,O,P
3-8 UNIT 

3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

unrelated to the number of crashes 

that are actually occurring on a road, 

but the reported numbers could look 

quite different. Changes to the crash 

reporting thresholds can happen 

abruptly and may significantly affect 

the crash data. Consider how the 

safety of a road, based on reported 

crashes, would appear in the years 

before and after a crash reporting 

threshold change from $1,000 to 

$4,000. You would expect to see 

fewer reported crashes after the 

change, since crashes with damage 

below $4,000 would no longer be 

reported, even though there may 

be no real change in the number of 

crashes occurring. 

After the crash investigation is 

completed by the officer for the 

investigating agency, it usually 

undergoes an internal quality review. 

Passing the internal review, the 

crash report is sent to the State crash 

database. In some cases, the data is 

transmitted electronically, while in 

other cases the State agency receives 

a paper copy of the crash report. 

The agency that maintains crash 

data for the State may be the State 

department of transportation 

(DOT), the department of motor 

vehicles (DMV), or a State law 

enforcement agency. This agency 

will in turn make the data available 

to various other agencies. Federal, 

State, and local governments, as 

well as metropolitan planning 

organizations, advocacy groups, auto 

and insurance industries, and private 

consultants request crash data to 

conduct various transportation 

planning activities and analysis. 

The agency maintaining the 

data may provide raw or filtered 

datasets to local agencies and to 

national databases, such as the 

National Highway Traffic Safety 

Administration’s (NHTSA’s) Fatality 

Analysis Reporting Systems (FARS). 

The time between the crash 

occurrence and the availability 

of the crash data from the State 

crash database varies and typically 

depends on the type of crash 

reporting system and the State 

and local government capabilities. 

This time period between crash 

occurrence and the report’s 

availability for analysis defines the 

timeliness of the crash data. While 

some agencies can provide complete 

data with a very short turnaround 

(i.e., less than a month), others take 

significantly longer (i.e., up to two 

years) due to backlogs and personnel 

shortages. Agencies who have the 

majority of their crashes reported 

electronically from law enforcement 

typically have shorter turnarounds 

on the crash data.

Common data elements

Common data elements for crash 

data include information on date, 

location, injury severity, types of 

vehicles, and characteristics of 

persons involved. Crash narratives 

and diagrams are typically found in 

the original crash reports, though 

generally not in the crash database. 

Narratives and diagrams are most 

useful when the safety professional 

desires to know the exact location 

of the crash, such as the particular 

approach of an intersection. 

NHTSA developed the Model 

Minimum Uniform Crash Criteria 

(MMUCC) in 1998 as a model set 

of data elements that should 

be collected to enable safety 

professionals to conduct data-driven 

analyses. States are encouraged 

to adopt MMUCC standards, 
ROAD SAFETY FUNDAMENTALS UNIT 

3: MEASURING SAFETY

3-9

though they are not required to 

match these recommendations. 

MMUCC, currently in its fourth 

edition, recommends the crash data 

elements listed below. Chapter 9 

presents further information on 

MMUCC on page 3-30. 

Data sources and custodians

Crash details may be available from 

different sources or systems. State 

agencies and institutions typically 

maintain the State crash database. 

These include State departments 

of transportation, departments of 

motor vehicles, departments of 

public safety, or in some cases, State 

universities under contract to a 

specific department. Local agencies, 

such as cities or metropolitan 

planning organizations, may also 

maintain their own crash databases 

within local record management 

systems. These local systems 

are most frequently housed by 

the local police, public works, or 

transportation departments.

Transportation safety applications

Crash data serve as the primary 

observable measure of safety 

(or lack thereof) on the road. 

Transportation professionals can use 

crash data to analyze a single crash, 

a specific site, an entire corridor, 

or a large area, such as in regional 

or Statewide planning. Crash data 

can be used to provide guidance to 

transportation decision makers and 

to guide the formation of safety 

legislation. 

Coordination or integration with 

other data sets

In transportation departments, 

other data elements frequently used 

along with crash data include road 

characteristics and traffic volume 

data. For example, by combining 

road characteristics with crash 

data, safety professionals are able 

to identify road elements that may 

lead to higher frequency or injury 

severity of crashes, and therefore 

develop a systemic approach to 

reduce that crash risk at many of 

the locations that have those risk 

elements. Using traffic volume, 

agencies can calculate crash rates 

(e.g., crashes per road vehicle) to 

better identify locations requiring 

safety improvements.

Data challenges and gaps 

Some of the most common issues 

found in crash reporting include 

incomplete data (for example a 

driver’s blood alcohol content is 

often missing), delays in entering 

the data into databases, inaccurate 

crash locations, and wrongly 

assigned fault and wrong choice of 

crash type. Some of these issues can 

be fixed by training police officers 

and those who enter the data into 

the database, as well as by using 

technology checks in data collection. 

Agencies should periodically conduct 

independent quality checks on the 

accuracy and reliability of their data.

Caution on the use of crash rates

Crash rate calculation (crashes per 

amount of traffic) is a simplistic 

measure that may be useful when 

comparing sites with similar 

characteristics and traffic volumes. 

However, the relationship between 

crashes and volume is not linear and 

can therefore lead to wrong conclusions 

if that assumption is made when 

considering volume increases on a road 

or comparing roads of different types. 

Unit 4 discusses how an analyst can use 

safety performance functions to avoid 

this error.
3-10 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Traffic Volume Data 

Description

Traffic volume data indicates how 

many road users travel on a road or 

through an intersection. The most 

prevalent type of volume data is 

a count of daily use by motorized 

vehicle traffic. This type of traffic 

volume data can be measured 

in many ways depending on the 

intended use. Volume measurements 

include: 

J Annual average 

daily traffic (AADT)

J Average daily traffic (ADT)

J Total entering vehicles (TEV) 

for intersections

J Turning movement counts

J Vehicle miles traveled (VMT)

J Pedestrian counts

J Bicyclist counts

J Percentage of traffic for specific 

vehicle types (e.g., heavy trucks 

or motorcycles)

AADT is the average number of 

vehicles passing through a segment 

from both directions of the mainline 

route for all days of a specified 

year. As AADT requires continuous 

year-round counting, these data 

are often unavailable for many road 

segments. In these cases, ADT is 

used to estimate AADT by using 

shorter duration counts of that road 

and then adjusting those volumes 

by daily and seasonal factors. Other 

data used for crash analysis include 

turning movement counts and 

TEV at intersections and VMT on a 

road segment, which is a measure 

of segment length and traffic 

volume. VMT are useful for highway 

planning and management, and a 

common measure of road use. Along 

with other data, VMT is often used 

to estimate congestion, air quality, 

and expected gas tax revenues, and 

can serve as a proxy for the level of 

a region’s economic activity. Volume 

data is also occasionally collected 

for bicyclists and pedestrians at road 

segments and crossing locations.

The Oregon DOT identified pedestrian and 

bicycle crashes as one of its primary focus 

areas for infrastructure funding. While 

pedestrians and bicyclists account for more 

than 15% of all traffic fatalities statewide, 

the locations of serious injuries and 

fatalities appear to be random. Therefore, 

in 2013, ODOT set out to develop a 

program that focuses the limited available 

funding for infrastructure countermeasures 

on locations with the greatest crash 

potential. In order to identify these higherrisk locations, ODOT is working to discover 

behavioral patterns and road conditions 

that lead to pedestrian and bicycle crashes. 

While a promising approach, this analysis 

is constrained by the limited availability 

of road information (e.g., bicyclist and 

pedestrian volumes, the presence of 

crosswalks, turn lanes, driveway activity, 

and sight distances). While the lack of 

these data does not preclude such an 

analysis, it does reduce the certainty of the 

findings. An additional benefit from this 

effort is that it has helped ODOT identify 

current data deficiencies which ODOT is 

currently working to fix.

Reference: Pedestrian and Bicycle Safety 

Implementation Plan, Oregon Department 

of Transportation, February 2014. Accessed 

September 2016 at https://www.oregon.gov/

ODOT/HWY/TRAFFIC-ROADWAY/docs/

pdf/13452_report_final_partsA+B.pdf

Challenges with the Use of Crash Data to Systemically Identify High Risk Locations
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-11

Data collection process

Volume data can be collected 

automatically or manually. Vehicle 

volume data is typically collected 

using automated counters, 

such as magnetic induction 

loops, pneumatic tube counters, 

microwave, radar, or video detection. 

These automated counters can also 

be configured to classify vehicles 

and produce counts by vehicle 

type (e.g., trucks, single passenger 

vehicles, etc.). For shorter durations 

or occasional counts, transportation 

agencies use manual traffic counts 

performed by observers, either in 

the field or through video cameras. 

Manual counting is also used often 

for bicyclist or pedestrian counts, 

although there are a number of 

additional technologies, such as 

infrared beams, that can be used 

to collect non-motorized volume 

data. These manual counts can range 

in length from one-hour counts 

to full-day counts, depending on 

the agency’s needs and practices. 

Fitness tracking apps may also 

provide additional information 

to jurisdictions regarding where 

bicyclist and pedestrian activity is 

occurring. Some care is needed when 

using these data due to the selfselection bias present from users 

having to opt-in to the tracking 

and only using for specific types of 

activities (e.g., fitness cycling rather 

than commuting).

Each State has its own traffic data 

collection needs, priorities, budget, 

and geographic and organizational 

constraints. These differences 

cause agencies to select different 

equipment for data collection, use 

different data collection plans, 

and emphasize different data 

reporting outputs. The FHWA Traffic 

Monitoring Guide (TMG) highlights 

best practices and provides guidance 

to highway agencies in traffic 

volume data collection, analysis, 

and reporting3

. The TMG presents 

recommendations to improve 

and advance current programs 

with a view towards the future of 

traffic monitoring. Traffic data is 

used to assess current and past 

performance and to predict future 

performance. Some States are 

utilizing traffic data from intelligent 

transportation systems (ITS) to 

support coordination of planning 

and operations functions at the 

Federal and State levels. 

Common data elements

Volume data must include the 

counted volume, location, date, and 

duration of the count. Depending 

on the method used, the volume 

data may also contain information 

on vehicle classification, speed, 

or weight; lane position; weather; 

and directional factors. From these 

data, transportation professionals 

can calculate the average number of 

Traffic Monitoring 

Guide, Federal 

Highway 

Administration, 

Office of Highway 

Policy Information, 

September 2013.

3

What’s the difference between 

ADT and AADT?

Short term traffic counts are typically 

collected at a location for a 12-, 18-, or 

24-hour period. Average Daily Traffic 

(ADT) is the count of traffic calculated 

to reflect the 24-hour (daily) volume 

of the date it was collected. The 

Annual Average Daily Traffic (AADT) is 

calculated for an entire year from the 

ADT by adjusting that simple average 

traffic volume to take into account the 

different travel patterns that occur 

during short duration count periods. For 

example, a summer traffic count taken 

in a beach vacation town would need 

to be adjusted downward to reflect the 

average traffic volume for the year, since 

traffic would be much higher in 

the summertime.
3-12 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

vehicles that traveled each segment of 

road and daily vehicle miles traveled 

for specific groups of facilities, 

vehicle types, and vehicle speeds.

Data sources and custodians

State highway agencies collect 

and maintain traffic volume data 

for State-controlled roads. These 

data are shared with the U.S. 

Department of Transportation in 

order to monitor road usage and 

safety trends. Local jurisdictions 

also collect and maintain traffic 

volume data; the scope, consistency, 

and quality of these data varies by 

jurisdiction. 

Transportation safety applications

Agencies use volume data to support 

activities in design, maintenance, 

operations, safety, environmental 

analysis, finance, engineering, 

economics, and performance 

management. For instance, total 

traffic volume estimates or forecasts 

on a section of road are used to 

generate State and nationwide 

estimates of total distance traveled. 

Annual traffic volumes are also 

essential in network screening, 

diagnosis, and the selection of 

countermeasures (see further 

presentation of these processes in 

Unit 4). When selecting appropriate 

crash modification factors (CMFs) 

to estimate the benefit of potential 

countermeasures, a safety 

practitioner must use traffic volumes 

to confirm that the CMFs are suitable 

for the site in consideration. 

Coordination or integration with 

other data sets

Other data elements frequently 

used with traffic volume data 

in safety applications include 

road characteristic inventories 

and crash data. For example, an 

agency that uses traffic volume and 

crashes together can identify sites 

with highest potential for safety 

improvements and target specific 

crash types. This allows them to 

better identify and prioritize locations 

for safety improvements.

Spatial Data and Road Safety

Many of the types of data presented in this 

chapter can be stored in a spatial format 

and displayed in a GIS. GIS is a particularly 

powerful tool designed to store, 

manipulate, analyze, and visualize data 

that is linked to a location. This makes it 

valuable to highway safety practitioners 

who can use a common referencing system 

for much of their highway data and link it 

together in GIS. For example, a single GIS 

database can contain road attributes, such 

as number of lanes, pavement condition, 

and lighting; crash information; and 

traffic volumes. This information can then 

be used to analyze crash hotspots and 

trends, such as multi-vehicle crashes in the 

vicinity of signalized intersections. 

This GIS map displays signalized 

intersections as squares and crashes 

as dots and allows the analyst to easily 

identify crashes occurring within 150 feet 

of a signalized intersection (denoted by 

circular areas around each intersection).
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-13

Data challenges and gaps 

One of the biggest challenges in 

collecting accurate volume data is 

implementing a quality assurance 

process to ensure that counts are 

accurately recorded. Traffic volume 

for most roads is also based on 

sampling, which leads to estimates 

of volume on much of the road. As 

technology continues to develop and 

become more prevalent on our roads 

and in our vehicles, the accuracy will 

improve considerably. Additionally, 

pedestrian and bicyclist counts are 

more susceptible to higher variability 

due to their lower volumes; 

thus, longer count durations and 

additional locations are required for 

accurate data applications. 

Road Characteristics Data 

Description

Road characteristics data is also 

referred to as road inventory data. 

The most basic road characteristics 

data typically includes road name or 

route number, road classification, 

location coordinates, number of 

lanes, lane width, shoulder width, 

and median type. Intersection 

characteristics typically include 

road names, area type, location 

coordinates, traffic control, and 

lane configurations. The collection 

of these data elements supports 

an enhanced safety analysis and 

investment decision making when 

combined with other datasets, such 

as crash information.

Data collection process

Road characteristics data can be 

collected through several methods 

including photo or video logs, field 

surveys, aerial surveys, integrated 

GIS and global positioning systems 

(GPS) mapping, and vehiclemounted Light Detection and 

Ranging (LIDAR) technology. Some 

States find it more cost effective to 

purchase these data from third party 

providers.

Common data elements

Transportation agencies typically 

collect those road characteristics 

that they need or can be collected 

based on the available funds. Road 

characteristics are collected for many 

different purposes, such as road 

maintenance and improvement 

projects. Given that States have 

different priorities and funding 

structures, the elements of road 

characteristics data is not the 

same from State to State or among 

local agencies.

To provide guidance on road 

characteristics that are the most 

needed for safety analysis, the FHWA 

developed the Model Inventory of 

Roadway Elements (MIRE). MIRE 

provides a recommended (but not 

required) list of road characteristics 

elements specifically for safety 

analysis. The elements are divided 

into the categories shown in Table 

3-2. Chapter 9 presents further 

FIGURE 3-2: This image from the FHWA 

Model Inventory of Roadway Elements 

(v. 1.0) illustrates roadway elements.
3-14 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

information on MIRE on page 3-32. 

Data sources and custodians

Road characteristics data are 

collected at both the local and 

Statewide levels. At the local level, 

having data on details, such as 

traffic control devices, sidewalks, or 

the number of travel lanes, can be 

beneficial for safety evaluations and 

safety project prioritization. These 

data are maintained by the city or by 

a higher level agency such as a MPO.

State road characteristics data 

include physical road attributes, 

traffic control devices, rail grade 

crossings, and structures, such 

as bridges and tunnels. Each 

State highway agency, some 

local transportation and public 

works departments, and regional 

planning agencies collect and 

maintain road characteristics data. 

In addition, most States also have 

supplemental inventory data for 

bridges as part of the National 

Bridge Inventory and railroad grade 

crossings as part of the Federal 

Railroad Administration’s Railroad 

Grade Crossing Inventory. These 

databases usually can be linked to 

the Statewide road inventory.

Transportation safety applications

Road safety professionals can 

use road characteristics data to 

access data about the physical 

characteristics of crash sites 

or other priority sites. Road 

characteristics data is essential for 

network screening, development 

or calibration of crash prediction 

models, and related applications. 

These data are also valuable on the 

large scale level to estimate where 

crashes are expected to occur on the 

system. 

Coordination with other data sets

Road characteristics data can be 

linked with crash and volume data to 

improve safety analysis and problem 

identification. Combining datasets in 

this way allows safety professionals 

to identify areas with a high 

TABLE 3-2: Categories of MIRE Elements

CATEGORY EXAMPLES OF MIRE DATA ELEMENTS

Roadway Segment Roadway classification

Paved surface characteristics

Number and type of travel lanes

Shoulder, median, and roadside descriptors

Pedestrian and bicyclist facilities

Traffic volumes

Roadway Alignment Curve and grade information

Roadway Junction Traffic control devices

Intersection features

Interchange and ramp descriptors
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-15

potential for safety improvements 

(by means of a network screening 

process) and identify appropriate 

countermeasures. However, the 

road characteristics data must share 

a common reference system with 

the crash and volume data in order 

to link them together. The most 

common methods of linking road 

data with crash or volume data use 

a linear referencing system, such as 

routes and mileposts, or a spatial 

referencing system, where all files 

share the same coordinate system.

Data challenges and gaps 

Collecting accurate road 

characteristics data can be a timeconsuming and expensive process. 

Data collection that is done only 

for part of a road network results 

in gaps in inventories of road 

features such as the location of 

guardrails, shoulder widths, and 

rumble strips. Transportation 

agencies are continually looking for 

newer technologies to streamline 

the collection of this detailed data. 

Also, it is more common for road 

characteristics data to be fuller 

and more detailed for State system 

roads compared to local roads, 

since local agencies typically have 

less funding, fewer staff, and less 

general prioritization for collecting 

road characteristics data.

CRASH DATA

Determine proportional 

issues from specific 

vehicle types

Calculate 

crash rates

Calculate 

predicted crashes 

from safety 

performance 

functions

Prioritize maintenance 

activities

Prioritize systemic 

improvements

Determine 

risk factors

Develop safety 

performance 

functions for 

predicting crashes

ROAD DATA

VOLUME DATA

FIGURE 3-3: Using Safety Data Together
3-16 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Supplemental Safety Data

In addition to the critical 

transportation safety datasets 

(crashes, road characteristics, and 

traffic volume), there are many 

other datasets that can be used and 

combined to conduct additional 

types of evaluations on the 

effectiveness of programs, human 

behaviors and safe decision making, 

and public opinions.

Conflicts, Avoidance Maneuvers, 

and Other Interactions

Observing conflicts between road 

users, avoidance maneuvers, such as 

swerving or hard braking, and other 

interactions, such as failures to yield 

can provide valuable information 

on road safety. These other 

measures of safety are referred to 

as surrogate measures. They occur 

more frequently than actual crashes 

and therefore enable agencies to 

identify safety risks more quickly 

and in a proactive manner (i.e., 

before the crash occurs). However, 

by their nature of being surrogates, 

there is potential for inaccuracy in 

determining which types of conflicts 

are good indicators of crashes. 

Surrogate safety data is collected 

by in-field observers or through 

recordings that capture the 

behaviors and interactions of road 

users. These recordings can be 

made through stationary cameras or 

dashboard-mounted video cameras. 

Observing interactions between road users, like these drivers and crossing pedestrians, 

can be a good way to gain supplemental data about safety effects.
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-17

Increasingly, researchers are using 

programs to automatically identify 

potential events. This eliminates the 

need to scan visually through the 

entire video.

Observing interactions between 

road users can provide valuable 

information on the safety effect 

of certain road elements, such 

as signals or signs, and help 

identify the probability of crashes 

under different conditions. If a 

reliable relationship between the 

observations and crashes is known, 

such studies may also provide 

insights into the potential for safety 

issues between road users, such 

as between vehicle drivers and 

pedestrians.

However, one of the biggest 

challenges for using observations of 

road user interactions is that they 

are surrogate measures of safety. 

To date, we lack good research 

that would quantitatively equate 

surrogate measures of safety to 

crash data. If such relationships 

were known, safety professionals 

could conduct evaluations with a 

large number of surrogate measures 

in a relatively short period of time. 

This contrasts with the need to wait 

for years for sufficient crash data to 

support a good analysis.

Injury Surveillance and Emergency 

Medical Systems Data 

Injury surveillance systems 

(ISS) typically provide data on 

emergency medical systems (EMS), 

hospital emergency departments, 

hospital admissions/discharges, 

trauma registry, and long-term 

rehabilitation. This information 

is used to track injury causes, 

severity, costs, and outcomes. 

Although an injury associated with 

a traffic crash is only one type of 

injury in these medical systems, 

traffic crash injuries can be a useful 

source of data in bridging the gap 

between traditional traffic safety 

and public health issues. Hospital 

records are also often the only 

source of information on bicycle 

and pedestrian crashes that are not 

recorded by the police, such as those 

that occur in non-roadway locations 

like parking lots and driveways.

Evaluation of Children Involved 

in Off-Roadway Crashes Using 

Trauma Center Records

Many off-roadway crashes are not 

reported by law enforcement and 

are thus missed when conducting 

safety evaluations using police crash 

reports. One group that is particularly 

affected by this lack of data is young 

children injured by passenger vehicles 

in driveways and parking lots. This 

lack of information provides safety 

professionals with little knowledge 

about crash risk factors and actual 

incident rates that could be used 

to allocate resources and promote 

safety interventions and good design 

and behaviors. A 2010 study (Rice et 

al.) in California used records from 

eight trauma centers to identify the 

frequency and characteristics of these 

crashes. This study highlighted the 

inconsistencies with external causeof-injury codes used by emergency 

departments, but suggests that there 

is value to surveillance of off-roadway 

pedestrian injuries at trauma centers as 

a way of identifying incidents that are 

not captured by other data sources.

Reference: Rice TM, Trent RB, Bernacki 

K, Rice JK, Lovette B, Hoover E, Fennell 

J, Aistrich, AZ, Wiltsek D, Corman E, 

Anderson CL, Sherck J. (2012). Trauma 

center-based surveillance of nontraffic 

pedestrian injury among California 

children. Western Journal of Emergency 

Medicine; 13.2.
3-18 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Hospitals often use the external 

cause of injury classifications to 

code causes of patient injuries, 

including those from traffic crashes. 

These data can provide a description 

of injury severity, type of crash (e.g., 

motor vehicle passenger, bicyclist), 

and, in some cases, the location of 

incident. However, the data is often 

incomplete or non-specific. In order 

to provide a more comprehensive 

understanding of motor vehicle 

crash outcomes, NHTSA developed 

the Crash Outcome Data Evaluation 

System (CODES), which links crash, 

vehicle, and behavior characteristics 

to their specific medical and 

financial outcomes. Hospital injury 

data most often includes date, injury 

severity, cause, and demographic 

information. Personal identifying 

information is not included.

Hospital data can be used by 

a variety of governmental and 

non-governmental agencies to 

investigate the causes of injuries. 

Based on this analysis, the agencies 

can develop a safety campaign 

to reduce injuries to particular 

demographics. It can also be used 

to identify the full magnitude of 

crashes for a specific user group or 

demographic that is not recorded 

or reported by law enforcement. 

For example, hospital data can 

help safety professionals better 

understand the number of bicyclist 

crashes, since many bicycle-related 

crashes are not reported to law 

enforcement.

Hospital data are often difficult 

to use for those who administer 

roads, primarily the State DOT. The 

data is time consuming to acquire 

and may not contain complete 

data. Additionally, since there are 

no personal identifiers relating 

hospital injury data to specific crash 

records, the linkage is difficult and 

is seldom done. For these reasons, 

State DOTs rarely use these data; it 

is most often employed by public 

health researchers. However, there 

continues to be efforts at both 

Federal and State levels to develop 

better ways to integrate injury 

surveillance and emergency medical 

systems data with crash data.

Hospital data 

can be used 

to investigate 

causes of 

injuries, and 

is often the 

only source of 

information on 

some bicycle 

and pedestrian 

crashes.
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-19

Driver History Data 

Departments of Motor Vehicles 

(DMVs) maintain driver history data 

on all licensed drivers in the State. 

DMVs typically create a driver record 

when a person enters the State 

licensing system to obtain a driver’s 

license or when an unlicensed 

driver commits a violation or is 

involved in a crash. State driver 

history databases interact with the 

National Driver Register (NDR) 

and the Commercial Driver License 

Information Systems (CDLIS) to 

prevent drivers with a history of atfault crashes or inordinate number 

of citations from obtaining multiple 

or subsequent licenses. 

The driver history data contain 

information such as:

J Basic identifiers (e.g., name, 

address, driver license number)

J Demographics (e.g., age, 

birth date, gender)

J Information relevant to license 

and driver improvement actions 

(e.g., license issue, expiration 

and renewal dates, license 

class, violation dates, 

suspension periods) 

One challenge with using these data 

is that they are almost never shared 

outside a DMV. State or local DOTs 

do not have access to these data 

while developing their HSIPs (or 

conducting location specific safety 

studies). Sharing driver history data 

nationally is limited and could be 

improved by creating inter-agency 

data sharing partnerships that 

address privacy concerns and allow 

State DOTs to work with the data.

Vehicle Registration Data

Vehicle registration data includes 

information about registered 

vehicles in a State and is also 

typically maintained by the DMV. 

Vehicle registration systems may 

also contain information regarding 

commercial vehicles and carriers 

registered in a particular State and 

licensed to travel in other States. 

These data can provide information 

on the vehicle population within a 

State or county to be used in large 

scale safety analysis. These data 

can also help identify owners in the 

event of a crash or traffic violation.

Typical vehicle registration data may 

include owner information, license 

plate number, vehicle make, model, 

and year of manufacture, body type, 

vehicle identification number, and 

miles traveled. Common data for 

commercial vehicles may include 

U.S. Department of Transportation 

(DOT) number, carrier information, 

and inspection or out-of-service 

information.

Citations and Enforcement

Citation data refers to data on 

individual drivers that records any 

illegal actions that were cited by a 

law enforcement officer. It includes 

traffic violations, such as reckless 

driving, driving under the influence, 

and not carrying adequate car 

insurance; traffic crashes; driver’s 

license suspensions, revocations, 

and cancellations; and failures to 

appear in court. The data can also 

include the traffic infractions that 

have been adjudicated by the courts.

These data are helpful in identifying 

and tracking those individuals 

with a higher potential for unsafe 

driving behaviors. In an attempt to 

control crash occurrences, States 

may monitor high-risk drivers 

by reviewing their driver history 
3-20 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

records, paying particular attention 

to driver citations. Ideally, States 

track a citation from the time it is 

issued by a law enforcement officer 

through its disposition in a court of 

law. Citation information tracked 

and linked to driver history files 

enable States to screen drivers with 

a history of frequent citations for 

actions known to increase crash 

risk. States have found citation 

tracking systems useful in detecting 

repeat traffic offenders prior to 

conviction. It can also be used to 

track the behavior of particular law 

enforcement agencies and the courts 

with respect to dismissals and plea 

bargains. Many law enforcement 

agencies use citations as a method 

of tracking and measuring the 

effectiveness of enforcement efforts.

Some constraints exist with the 

use of citation and enforcement 

data to help prevent crashes. Some 

States have difficulty in maintaining 

accurate citation information 

because local jurisdictions may 

collect different data elements 

from varying citation forms. 

Obtaining and managing judicial 

information is also a challenge 

because of the various levels of court 

administration and jurisdiction. 

Unfortunately, in some States 

judges do not have access to the 

offender’s driver history at the time 

of sentencing, so many offenders 

escape the stricter penalties 

sanctioned for repeat offenses. 

In addition, the traffic safety 

community often lacks access to 

adjudication information due to 

privacy concerns. 

Naturalistic Driving Data

Naturalistic driving data are driver 

behavior data collected during 

actual driving trips through 

technology placed in the vehicle. 

This technology typically includes 

video camera views of the driver, 

speed and vehicle motion sensors, 

and location tracking equipment. 

Data such as video might be 

collected on a continuous basis, or 

only after certain events like hard 

braking. Using data collected by 

this equipment, researchers are 

able to gather information on the 

underlying causes of crashes by 

observing drivers in a natural driving 

situation. Frequently collected 

data include road environment 

information, such as weather; 

driver information, such as eye 

movements; and information 

on vehicle movement including 

location on the road, acceleration, 

deceleration, and speed. 

These data are used to evaluate how 

drivers interact with and react to 

the road, other road users, and other 

environmental features. Driver 

observation is used to understand 

fundamental issues of driver 

behavior and to develop improved 

safety countermeasures. The data 

are primarily used in research 

studies on a variety of topics. The 

data from the SHRP2 program 

have been used to study safety 

Strategic Highway

Research Program 2

The largest naturalistic study in the 

United States to date is the second 

Strategic Highway Research Program 

(SHRP2), which included over 3,400 

drivers participating in the study. 

SHRP2 data includes over 5,400,000 

individual trips and over 36,000 crash, 

near crash, and baseline driving events. 

FHWA provides more information on 

SHRP2 at https://www.fhwa.dot.gov/

goshrp2.
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-21

issues including prevention of road 

departures, driver reaction to posted 

speed limits, and driver response 

to curves in the road, in addition to 

many non-safety-related topics.

A challenge with collecting a large 

amount of naturalistic data is the 

high cost of recruiting participants, 

instrumenting vehicles, and 

reducing and analyzing the data. 

The process of coding (observing) 

the behaviors of the driver while 

driving is time-consuming and is 

typically conducted on a frame-byframe basis, leading to expensive 

data collection and lengthy study 

periods. The data are highly private 

(i.e., contains videos of driver faces), 

and therefore are typically difficult 

to access or distribute. Despite these 

challenges, naturalistic driving data 

provides a unique and extremely 

insightful look at fundamental 

issues of road safety.

Driving Simulator Data

Due to the high cost of naturalistic 

driving studies and the rarity of 

traffic crashes, driving simulators 

are often used to efficiently and 

safely evaluate driver behavior 

under different conditions. 

Researchers are able to study 

many different conditions and 

complex environments without 

exposing drivers to danger 

through replicating a wide range 

of road, traffic, and environmental 

conditions, as well as driver 

behaviors such as distractions, 

impairment, and fatigue. New types 

of road designs can be guided by 

the use of simulators, particularly 

complex features, such as urban 

highway interchanges.

Driving simulators like this one are often used to evaluate driver behavior under specific 

conditions and in a cost-effective way.
3-22 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Simulators can also be used 

for driver education to teach 

people about the effects of driver 

distractions or to prepare young 

drivers for different conditions 

before they encounter them on the 

road. Truck simulators are used to 

replicate the driving environment 

for different types of commercial 

trucks and used to safely train 

new drivers.

Public Opinion Data 

Feedback from the general 

public can be a useful source of 

information for safety professionals. 

Safety professionals can use 

information on road safety issues 

and concerns from the public to 

identify specific locations or types of 

conditions where people have real or 

perceived traffic safety concerns. 

There are many different ways to 

collect this information, such as a 

phone-based survey, web-based 

tools (pins on maps or online 

forms), meetings, or intercept 

surveys. Common data collected 

are the type of concern, location, 

and type of mode (i.e., walking, 

bicycling, transit user, or driving).

These data are typically collected at 

the local level, frequently as part of 

a transportation planning process 

or as a collaborative effort with law 

enforcement. Bringing residents 

and police officers to join the road 

safety audit teams or diagnosis 

teams during their field visits is also 

a beneficial way to learn about the 

experiences of the road users in the 

study area.

These data may provide valuable 

insights about what the travelling 

public perceives as dangerous; 

however, it may be a biased sample 

based on those who self-select 

to provide the information to the 

researching agencies. Findings 

will be subjective as each person 

perceives a condition based on 

their individual experiences only. 

Different persons perceive different 

issues and recommend different 

“best” solutions for the same 

condition. Conclusions based on 

survey findings should be used with 

care.

Behavioral Observation

Observational surveys of road user 

behaviors are an effective method 

of data collection on information 

that may otherwise be inaccurately 

recorded due to self-reporting bias 

or are difficult to capture through 

other means. Several examples 

of data typically recorded using 

direct observation are the use of 

mobile devices (texting or calling), 

right turn on red, safety belt use, 

motorcycle or bicycle helmet use, 

and traffic control violations, such 

as rolling through stop signs.

0

20

40

60 64.3%

60.7%

2014

DOT-Compliant

Helmets

Noncompliant

Helmets

No

Helmet

2015

4.8%

10.6%

30.9% 28.7%

FIGURE 3-4: Observations of 

motorcyclists showed how many were 

wearing DOT-compliant helmets (Source: 

National Occupant Protection Use Survey)
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-23

These data are collected through 

observing road users on the road. 

Large scale surveys collecting a high 

number of observations will provide 

the most accurate sample of the road 

user population in the study area. 

Additionally, robust observation data 

will cover differing road types and 

land use characteristics and contain 

observations at different times of 

day, week, and season. An example 

of a large-scale data collection 

effort is the National Occupant 

Protection Use Survey conducted 

annually by NHTSA. In 2013, over 

52,000 occupants were observed in 

nearly 40,000 vehicles. The data, 

summaries, and evaluations from 

this program may be viewed on the 

NHTSA website4

.

Data Users

While many agencies use safety 

data, most of them have different 

goals. For example, a city traffic 

engineer may have a specific scope 

for identifying and treating specific 

high priority sites, whereas a safety 

analysist with a State may be focused 

on safety at the system level. 

Moreover, safety researchers and 

graduate students may be focused on 

a whole range of safety evaluations 

that are not intended to be action 

plans to improve safety at a specific 

site or system. Each type of data user 

may have different levels of access to 

these various types of safety data. 

The following tables provide 

common uses and data needs for 

these different types of data users.

https://crashstats.

nhtsa.dot.gov

4

TABLE 3-3 (above/next page): Data Use by Safety Professionals, Academics and Researchers ACADEMICS AND RESEARCHERS

DATA TYPE USEFULNESS ACCESSIBILITY OFTEN PAIRE

Crash Essential High Road characteristics,

Traffic volumes

Road characteristics Essential High Crash,

Traffic volumes

Traffic volumes Essential High Crash,

Road characteristics

Naturalistic driving Supplemental Moderate

Conflicts/avoidance 

maneuvers Supplemental Low Road characteristics,Traffic volumes

Citations Supplemental Low Crash, Traffic volumes,

Road characteristics

Driving simulator Supplemental Low Road characteristics

Behavior observation Supplemental Low Crash,

Road characteristics

Injury surveillance Supplemental Very low

Driver history Supplemental Very low

Vehicle registration Supplemental Very low

Public opinion Supplemental Very low
3-24 UNIT 

3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

SAFETY PROFESSIONALS 

FOR SPECIFIC SITES

DATA TYPE USEFULNESS ACCESSIBILITY OFTEN PAIRE

Crash Essential High Road characteristics,

Traffic volumes

Road 

characteristics Essential High Crash,Traffic volumes

Traffic volumes Essential High Crash,

Road characteristics

Public opinion Supplemental High Crash,

Road characteristics

Conflicts/avoidance 

maneuvers Supplemental Low Road characteristics,

Traffic volumes

Citations Supplemental Low Crash, Traffic volumes,

Road characteristics

Behavior 

observation Supplemental Low Crash,Road characteristics

Injury surveillance Non-essential Very low

Driver history Non-essential Very low

Vehicle registration Non-essential Very low

Naturalistic driving Non-essential Very low

Driving simulator Non-essential Very low

SAFETY PROFESSIONALS 

FOR SYSTEM LEVEL ANALYSIS

DATA TYPE USEFULNESS ACCESSIBILITY OFTEN PAIRE

Crash Essential High Road characteristics,

Traffic volumes

Road 

characteristics Essential High Crash,Traffic volumes

Traffic volumes Essential High Crash,

Road characteristics

Public opinion Supplemental High Crash,

Road characteristics

Conflicts/avoidance 

maneuvers

Non-essential Low Road characteristics,

Traffic volumes

Citations Non-essential Low

Behavior 

observation Non-essential Low

Injury surveillance Non-essential Very low

Driver history Non-essential Very low

Vehicle registration Non-essential Very low

Naturalistic driving Non-essential Very low

Driving simulator Non-essential Very low
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-25

Insurance data

(e.g., carrier, policy number, expiration 

date, claims cost)

These data can provide insights into 

associations between insurance status and 

safety.

Demographic data

(e.g., population by gender, age, rural/

urban, residence, and ethnicity)

These data can be used for normalizing 

crash data to a state’s general population.

Safety program evaluation data

(e.g., surveys, assessments, inspections)

These data can provide feedback on the 

effectiveness of a new safety program.

Maintenance data

(e.g., guardrail replacement)

These data may indicate where unreported 

crashes are occurring.

J IDENTIFY possible relationships 

between the safety data presented 

in this chapter and census data (e.g., 

traffic safety vs. population density). 

J CONSIDER if, in the future, vehicles 

store pre-crash data in a “black box” 

type of event recording device. What 

types of data would you like it to store 

and how would you use this data (i.e., 

what types of analysis would you 

recommend conducting)?

J EXPLORE what type of safety analysis 

could be made possible using 

communication between vehicles 

(V2V) and also between vehicles 

and infrastructure (V2I - i.e., roads, 

intersections, etc.).

J DETERMINE how safety professionals 

can incorporate operational data, such 

as those from dynamic tolling lanes and 

speed sensors, into a safety analysis 

program.

EXERCISES

Other Types of Road Safety Data 

Additional types of data can also be useful to road safety professionals. These types of 

data may include:
3-26 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Quality Measures Of Data

The previous chapters in this unit 

have made the case that data are 

critical when seeking to improve 

road safety. However, simply having 

data is not enough. Good decisions 

require good data. When collecting, 

recording, maintaining, and 

analyzing safety data, road safety 

professionals must focus on the 

quality of data. Data-driven analysis 

tools are continually advancing and 

can help set priorities and select 

appropriate safety strategies, but 

the need for quality data to drive 

these tools is clear. Professionals 

commonly recognize that data 

quality can be measured on six 

criteria – timeliness, accuracy, 

completeness, uniformity, integration, 

and accessibility. Each of these 

criteria are presented in this chapter. 

Timeliness 

Timeliness is a measure of how 

quickly an event is available within 

a data system. State and local 

agencies can use technologies to 

automate crash data collection and 

quickly process police crash reports 

for analytic use. However, some 

agencies still rely on traditional 

methods, such as paper form data 

collection and manual data entry; 

these data collection methods can 

result in significant time lags. Many 

States, however, are moving closer 

to real-time data collection methods 

by using electronic reporting to 

improve the timeliness of data 

collection and submission.

Accuracy

Accuracy is a measure of how 

reliable the data are and whether 

they correctly represent reality. For 

example, exact crash location is 

an important detail for accuracy. A 

crash occurring at the intersection 

of First Street and Main Street 

should be recorded as occurring at 

that intersection. Accurate data are 

crucial during the analysis phase to 

generate road safety statistics and 

to pinpoint safety problems. Errors 

may occur at any stage of the data 

collection process. Common data 

accuracy errors include:

J Typographic errors (for data 

entered manually )

J Inaccurate and vague 

descriptions of the crash location

J Incorrect descriptions or entry 

of road names, road surface, 

level of accident severity, 

vehicle types, etc.

J Subjectivity on details that rely 

on the opinion of the reporting 

officer (e.g., property damage 

thresholds, excessive speed 

for conditions)

Technology can and is currently 

being used to improve accuracy and 

reduce errors. Automatic internal 

data quality checks are important for 

this purpose. These types of checks 

would determine if two data fields 

contain possibly conflicting data, 

and if so, bring it to the attention 

of the data analyst. An example of 

Improving Safety Data Quality

CHAPTER 9 MEASURING SAFETY
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-27

conflicting data fields would be a 

crash type recorded as “rear end” 

but the crash report says that one 

car was hit on the “side”.

Completeness

Completeness is a measure of 

missing information. It may range 

from missing data on the individual 

crash forms to missing information 

due to unreported crashes. 

Unreported crashes, particularly 

non-injury crashes, present a 

drawback to crash data analysis. 

Without knowing about these 

crashes, we cannot recognize the 

full magnitude of certain types of 

crashes (e.g., pedestrian involved 

crashes). Non-injury crashes, or 

property damage only (PDO) crashes, 

involve damage less than a specified 

threshold (e.g., $1,000); these 

thresholds vary from State to State. 

The parties involved in PDO crashes 

Many police officers now use in-car computers to complete and submit electronic crash 

reports, increasing the timeliness of data availability. (Source: Town of Hanover, NH)

are typically not required to report 

the crash and often agree to work 

out the financial damages personally 

or through their automobile 

insurance policies. In some States, 

even when PDOs are reported, they 

are not always added into the crash 

database. 

In addition to the limitations from 

absent data due to unreported 

crashes, fluctuations in the 

thresholds (i.e., dollar amounts) can 

make it difficult to compare data 

from previous years. Unreported 

PDO crashes are one of many 

measures of “completeness” 

that road safety professionals 

must consider when collecting 

and analyzing data. A lack of 

complete data hinders the ability to 

measure the effectiveness of safety 

countermeasures (e.g., safety belts, 

helmets, and red light cameras) or 

change in crash severity.
3-28 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Crash Data Improvement Program

The Federal government established 

the Crash Data Improvement Program 

(CDIP) to provide states with a 

means to measure the quality of the 

information within their crash database. 

It is intended to provide the states with 

metrics that can be used to establish 

measures of where their crash data 

stand in terms of its timeliness, the 

accuracy and completeness of the 

data, the consistency of all reporting 

agencies reporting the information in 

the same way, the ability to integrate 

crash data with other safety databases, 

and how the state makes the crash data 

accessible to users. Additionally, CDIP 

was established to help familiarize the 

collectors, processors, maintainers, and 

users with the concepts of data quality 

and how quality data help to improve 

safety decisions. CDIP also included a 

guide that presents information on each 

data quality characteristic and how to 

measure them.

Reference: Crash Data Improvement 

Program, National Highway Traffic Safety 

Administration, https://safety.fhwa.dot.

gov/cdip/summary.cfm

Uniformity

Uniformity is a measure of how 

consistent information is coded in 

the data system or how well it meets 

accepted data standards. Numerous 

law enforcement agencies within 

each State, some of which are not 

the primary users of the crash 

data, are responsible for crash data 

collection. The challenge for States 

is ensuring there is consistency 

among the various agencies when 

collecting and reporting crash data. 

One example of inconsistent or nonuniform data can be the location of 

a crash. If one agency, for example 

the State highway patrol, uses 

GPS to document a crash at one of 

several entrances (driveways) to a 

shopping center, but the city police 

use a linear reference system (e.g., 

distance from an intersection), there 

is a potential for inconsistent crash 

location data. 

The Model Minimum Uniform 

Crash Criteria (MMUCC) is used by 

States to ensure uniform crash data. 

MMUCC is an optional guideline 

that presents a model minimum 

set of uniform variables or data 

elements for describing a motor 

vehicle crash. This uniformity 

assists transportation safety 

professionals and governments in 

making decisions that lead to safety 

improvements. Similarly, MIRE 

provides a recommended list of 

elements to use when reporting road 

and traffic characteristics, thereby 

increasing uniformity of road 

network data. More information on 

MMUCC and MIRE is presented at 

the end of this chapter.

Integration

Data integration is a measure of 

whether different databases can 

be linked together to merge the 

information in each database into 

a combined database. Each State 

maintains its own crash database. 

However, crash data alone do not 

typically provide sufficient details 

on issues like environmental risk 

factors, driver experience, or medical 

consequences. Linking crash data 

to other databases, such as road 

characteristics, driver licensing, 

vehicle registration, and hospital 

outcome data assists analysts 

and planners in evaluating the 

relationship of the circumstances 

of the crash and other factors (e.g., 

human, road, medical treatment) at 

the time of the crash. In addition, 

integrated databases promote 

collaboration among agencies, which 
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-29

Moving Ahead for 

Progress in the 21st 

Century Act, Section 

1112, §148(f)(2)

5

can lead to improvements in the 

data and the data collection process. 

Some data are more challenging to 

integrate with other data sets. For 

example, hospital data are difficult 

to integrate with crash data due to 

the lack of a common identification 

system (as well as medical privacy 

laws). This is different from crash, 

road characteristics, and volume 

data, which can share a common 

referencing system on the road and 

thus be integrated and linked more 

easily for analysis.

Spatially-located data in a GIS 

system can be integrated simply 

based on spatial position. This 

geographic integration can assist 

agencies in bringing together data 

that were gathered by various 

departments or agencies that may 

use different data storage standards 

and reference systems.

Accessibility

Accessibility is a measure of how 

easy it is to retrieve and manipulate 

safety data in a system, in particular 

by those entities that are not the 

data system owners. Complete, 

accurate, and timely data easily 

made available to localities, MPOs, 

and other safety partners can greatly 

enhance transportation planning 

and safety investments. Agencies 

or departments who house safety 

data, especially crash data, should 

consider how accessible the data 

are to external parties and how the 

process of obtaining data could be 

streamlined.

Data Improvement Strategies 

Local, State, and Federal agencies, 

as well as non-governmental 

organizations, require accurate 

data to be available for analysis and 

problem solving. Thus, programs to 

improve data should be in the work 

programs of all agencies invested in 

road safety. Data could be improved 

by changes in policy, technology, 

assessments, and training.

Policy

With so many agencies and 

organizations involved in the 

data collection process, published 

policy is a necessity. A standard 

set of procedures can provide a 

clear expectation of each agency’s 

roles and responsibilities in data 

collection. Federal guidance and 

State legislation or administrative 

policy and regulations generally 

form a basis for policy. An 

example of Federal guidance 

comes from the provision in the 

MAP-21 transportation legislation 

that requires States to collect a 

comprehensive set of roadway and 

traffic fundamental data elements 

(FDEs) on all public roads5

. 

Technology 

Technology plays an important role 

in data collection improvement. 

Federal legislation provides funds 

that allow States to improve their 

data collection systems with 

the latest technology for quality 

data collection and integration. 

Technology is not static and is 

always changing. Some technology 

examples that help facilitate data 

collection include electronic crash 

reporting systems, GPS location 

devices, barcode or magnetic 

strip technologies, wireless 

communications, error checking, 

and conflicting fields.

Assessments

Assessments are official evaluations 

that government agencies conduct 
3-30 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

Federal Highway 

Administration 

Roadway Safety 

Data Program, 

http://safety.fhwa.

dot.gov/rsdp/

technical.aspx

6

to determine the effectiveness of a 

traffic safety process or program. A 

team of outside experts conducts a 

comprehensive assessment of the 

highway safety program using an 

organized, objective approach and 

well-defined procedures that:

J Provide an overview of the 

program’s current status 

in comparison to 

pre-established standards

J Note the program’s strengths 

and weaknesses

J Provide recommendations for 

improvement

Both FHWA and NHTSA provide 

these types of assessments, such 

as the Roadway Data Improvement 

Program (RDIP), which can improve 

the quality of an agency’s data 

through expert technical assistance 

and fresh perspectives. When 

State agencies request an RDIP 

assessment, an FHWA team reviews 

and assesses a State’s roadway 

data system for the content of the 

data collected; for the ability to 

use, manage and share the data; 

and to offer recommendations for 

improving the road data. The RDIP 

also examines the State’s ability to 

coordinate and exchange road data 

with local agencies, such as those in 

cities, counties, and MPOs6

.

Training

Education and training of 

transportation professionals play 

a vital role in improving data 

and data collection. For example, 

law enforcement officers create 

the crash data that is used by 

safety professionals to conduct 

studies and evaluate road safety. 

Thus, law enforcement need to 

understand how crash data are 

used in policy development and 

investment decisions, infrastructure 

improvements, and safety planning. 

Through proper education and 

training programs, law enforcement 

can have a broader perspective 

of their contribution to reducing 

crashes through improved 

data reporting. Other examples 

include training transportation 

professionals on the latest data 

collection tools and technology, 

advising court officials and 

adjudicators on important changes 

to safety legislation and penalties, 

and training personnel on how to 

handle crash reports with inaccurate 

or missing information.

Federal Guidance

The following two sections present 

examples of Federal guidance that 

leads State agencies into improving 

the quality of their safety data.

Model Minimum Uniform Crash 

Criteria

Statewide motor vehicle traffic 

crash data systems provide the 

basic information necessary for 

effective road safety efforts at any 

level of government—local, State, 

or Federal. Unfortunately, the use of 

State crash data is often hindered by 

the lack of uniformity between and 

within States. Data definitions, the 

number and type of data elements, 

and the threshold for collecting 

data varies from jurisdiction to 

jurisdiction. The Model Minimum 

Uniform Crash Criteria (MMUCC) 

was developed to help bring greater 

uniformity to crash data collection 

and provide national guidance to 

data collectors. MMUCC represents 

a voluntary and collaborative effort 

to generate uniform, accurate, 

reliable, and credible crash data to 
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-31

MMUCC Example Element

The following is the MMUCC format for 

“Person Data Element Derived from 

Collected Data.” 

PD1. Age 

Definition: The age in years of the person 

involved in the crash 

Source: This data element is derived from 

Date of Birth (P2) and Crash Date and 

Time (C3).

Attribute: 

• Age in years 

Rationale: Age is necessary to 

determine the effectiveness of safety 

countermeasures appropriate for various 

age groups.

support data-driven highway safety 

decisions at a State and a national 

level. MMUCC serves as a foundation 

for State crash data systems. 

Since MMUCC is a minimum set of 

recommended crash data, States 

and localities may choose to collect 

additional motor vehicle crashrelated data elements if they feel 

the data are necessary to enhance 

decision-making. Implementation 

of MMUCC is a collaborative effort 

involving the Governors Highway 

Safety Association, FHWA, NHTSA, 

and the Federal Motor Carrier Safety 

Administration (FMCSA). 

The MMUCC Guideline is updated 

every four or five years to address 

emerging highway safety issues, 

simplify the list of recommended 

data elements, and clarify 

definitions of each data element.

MMUCC Data Elements

MMUCC consists of data elements 

recommended to be collected by 

investigators at the crash scene. 

From the crash scene information, 

additional data elements can be 

derived to assist law enforcement. 

Additional data elements are 

available through linkage to driver 

history, hospital and other health/

injury data, and road inventory data. 

Each group of data elements has a 

unique identifier that describes the 

type of data element and whether it 

is derived or linked data.

MMUCC data elements are divided 

into four major groups that describe 

various aspects of a crash: crash, 

vehicle, person, and roadway. Each 

data element includes a definition, 

a set of specific attributes, and a 

rationale for the specific attribute. 

For the entire list of MMUCC data 

elements, refer to the latest edition 

of the MMUCC Guideline located at 

www.mmucc.us.

The MMUCC data elements represent 

a core set of data elements. The 

fourth edition (2012) of the MMUCC 

Guideline contains 110 data elements 

and recommends that States collect 

all 110 data elements. To reduce the 

data collection burden, MMUCC 

recommends that law enforcement 

at the scene should collect 77 of the 

110 data elements. From crash scene 

information, 10 data elements can 

be derived, while the remaining 23 

data elements should be obtained 

after linkage to other State data 

files. States unable to link to other 

State data to obtain the MMUCC 

linked data elements should collect, 

at a minimum, those linked data 

elements feasible for collecting on 

the crash report. At the same time, 

States should work to develop data 

linkage capabilities so they eventually 

are able to obtain, via linkage, all of 

the information to be generated by 

the MMUCC linked data elements.
3-32 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

FIGURE 3-5: MIRE Data Elements Category Descriptors (Source: MIRE version 1.0)

ROADWAY SEGMENT ROADWAY ALIGNMENT

Segment location/linkage elements

Segment classification

Segment cross section

Segment roadside descriptors

Other segment descriptors

Segment traffic flow data

Segment traffic operations/control data

Other supplemental segment descriptors

Horizontal curve data

Vertical curve data

ROADWAY JUNCTION

At-grade intersection/junctions

Interchange and ramp descriptors

Model Inventory of Roadway Elements

Critical safety data include not 

only crash data, but also road 

inventory data, traffic data, and 

other information. State DOTs need 

accurate and detailed data on road 

characteristics as they develop 

and implement strategic highway 

safety plan (SHSPs) and look toward 

making more data driven safety 

investments.

With the need for and availability of 

so many types of data, the question 

becomes “How can transportation 

agencies be sure that they are 

collecting the necessary roadway 

data to make effective road safety 

decisions?” MIRE is a vitally 

important resource that defines the 

data needed to help transportation 

agencies build a road characteristics 

database that will lead to good 

safety analysis. MIRE defines 202 

individual characteristics of the road 

system that should be collected. 

These characteristics are referred to 

as data elements. The elements fall 

into three broad categories: 

J Roadway segment descriptors

J Roadway alignment descriptors

J Roadway junction descriptors

Most State and local transportation 

agencies do not have all the data 

needed to use analysis tools such 

as SafetyAnalyst, the Interactive 

Highway Safety Design Model, and 

other tools and procedures identified 

in the Highway Safety Manual. 

MIRE provides a structure for road 

inventory data that allows State and 

local transportation agencies to use 

these analysis tools with their own 

data rather than relying on default 

values that may not reflect local 

conditions. 

As the need for road inventory 

information has increased, new and 

more efficient technologies to collect 

road characteristics have emerged. 

However, the collected data need a 

framework for common information 

sharing. Just as MMUCC provides 

guidance for consistent crash data 

elements, MIRE provides a structure 

for roadway inventory data elements 

using consistent definitions and 

attributes. It defines each element, 

provides a list of attributes for 

coding, and assigns a priority status 

rating of “critical” or “value added” 

based on the element’s importance 

for use in analytic tools, such as 
ROAD SAFETY FUNDAMENTALS UNIT 3: MEASURING SAFETY 3-33

SafetyAnalyst. 

The latest version of MIRE can be 

viewed and downloaded from the 

FHWA Office of Safety site, http://

safety.fhwa.dot.gov/tools/data_tools/

mirereport/.

Figure 3-5 displays a breakdown of 

the major data element categories 

and subcategories contained in 

MIRE. MIRE further breaks down 

each subcategory into individual 

data elements. For a complete listing 

of MIRE data elements, refer to the 

MIRE publication. 

While the complete list of MIRE 

elements is rather extensive, there 

are a basic set of elements within 

MIRE called the Fundamental Data 

Elements (FDE) that an agency 

needs to conduct safety analyses 

regardless of the specific analysis 

tools used or methods applied. As 

discussed, the need for improved 

and more robust safety data is 

increasing due to the development 

of a new generation of safety data 

analysis tools and methods. 

Linking Data Through A 

Referencing System

The types of road safety data 

presented in this unit are only useful 

as much as they are capable of being 

linked through a common geospatial 

relational location referencing 

system. States recognize that they 

must have a common relational 

location referencing system (i.e., 

geographic information system or 

linear referencing system) for all 

public roads if they are going 

to integrate different types of 

safety data. If all safety data are 

referenced to the same system, the 

road characteristics data can be 

linked with the crash data, which 

would permit the State to identify 

locations on all public roads where 

crash patterns are occurring that 

can be reduced through known 

countermeasures. 

In most States, development of a 

common referencing system for all 

public roads will require significant 

effort and cooperation with local 

agencies. The Federal Highway 

Performance Monitoring System 

requires GIS-based referencing for 

all roads in the Federal-aid highway 

system, interstate highways, and 

public roads not classified as local 

roads or rural minor collectors.7

However, significant travel occurs 

on local roads and rural minor 

collectors. Some local agencies 

have or are developing, their own 

GIS-based referencing systems for 

roads in their inventory data. Light 

detection and ranging (LIDAR) 

systems are often used to accurately 

survey the road network. The State 

should work with local agencies 

to incorporate these referencing 

systems into the State base map. 

Once the referencing systems 

are combined, attribute data for 

additional mileage can be added 

when either State or local agencies 

develop or expand inventories. 

Moreover, as stated above, this will 

lead to the ability to link crashes 

with inventory and traffic data, 

enabling the State to use the more 

advanced problem identification 

methods on more and more miles of 

public roads.

Memorandum on 

Geospatial Network 

for All Public Roads, 

Office of Highway 

Policy Information, 

August 7, 2012. 

Accessed October 

2017 at https://

www.fhwa.dot.gov/

policyinformation/

hpms/arnold.pdf

7
3-34 UNIT 3: MEASURING SAFETY ROAD SAFETY FUNDAMENTALS

J SELECT a scenario below. Assume that 

you are using crash data as your primary 

data to inform your decisions. Explain 

how each of the six quality criteria 

discussed in this chapter could affect 

your evaluation of the current safety 

situation and your recommendations. 

J You are prioritizing intersections in 

a city to be treated with enhanced 

visibility treatments, such as 

larger signs, wider markings, and 

additional signal heads. 

J You are developing a public 

outreach effort to communicate 

the need to yield to pedestrians at 

crosswalks. You wish to focus your 

efforts to the areas of the city where 

failing to yield to pedestrians is the 

most rampant.

J You are recommending safety 

improvements to an interchange 

that was identified based on having 

a higher number of expected 

crashes than other interchanges of 

the same type.

Given the scenario you selected above, 

how do you think the availability of 

other types of data could affect your 

recommendations? Such data may 

include any of the data types covered 

in Chapter 3.2. (e.g., EMS and hospital 

injury data, enforcement citations, 

public complaints, or other data). What 

additional information could this reveal?

J IDENTIFY programs or policies that exist 

in your state or local agency to improve 

data. This may include any of the types 

of safety data covered in Chapter 3.2.

EXERCISES

Conclusion

Data are crucial to improving road 

safety. Safety data consist of various 

kinds of data that can be used 

to identify safety problems and 

priorities so that safety partners 

in many agencies can address 

important issues. Data such as crash 

data, traffic volume data, and road 

characteristics data are often used 

and are critical for safety analysis 

by many agencies. Other data, such 

as conflict observations, emergency 

medical data, and citation data, can 

be useful in a supplemental role for 

specific studies. Regardless of the 

type of safety data, the quality of the 

data is vitally important. Agencies 

that collect safety data should 

strive to improve their timeliness, 

accuracy, completeness, uniformity, 

integration, and accessibility to 

maximize their potential to drive 

good decisions.

FHWA, Office of Safety

Felix H. Delgado, P.E.

Felix.Delgado@dot.gov

FHWA-SA-18-003

Road Safety Fundamentals is available for free at: 

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