Roadway Data Improvement Program:

Supplemental Information Resource

June 13, 2012
Roadway Data Improvement Program: Supplemental Information Resource

ii

Notice

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

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

liability for the use of the information contained in this document.

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

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

objective of the document.

Quality Assurance Statement

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

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

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

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

and processes to ensure continuous quality improvement.
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ACKNOWLEDGEMENTS 

The authors of this document were Ms. Barbara Hilger DeLucia and Dr. Robert Scopatz of 

Data Nexus Inc (DNI), supported by Ms. Nancy Lefler of Vanasse Hangen Brustlin, Inc (VHB) 

under contract DTFH61-10-D-00022. Mr. Robert Pollack was the Federal Highway 

Administration (FHWA) Contract Manager for this project. 

The project team would like to thank the following individuals for their participation in the 

Roadway Data Improvement Program (RDIP) Noteworthy Practices Working Group Meeting: 

• Peggi Knight, Iowa Department of Transportation 

• Brian Mayhew, North Carolina Department of Transportation

• Brian Murphy, North Carolina Department of Transportation 

• John Arnold, North Carolina Department of Transportation

• Jon Farley, North Carolina Department of Transportation

• Chris Harris, Tennessee Department of Transportation

• In-Kyu Lim, Virginia Department of Transportation

• Ralph Jones, Virginia Department of Transportation

• James Hall, University of Illinois-Springfield, retired Illinois Department of 

Transportation

• Dale Lighthizer, Consultant, retired Michigan Department of Transportation

• David Harkey, University of North Carolina Highway Safety Research Center 

The project team would also like to thank the FHWA RDIP Advisory Group: 

• Joseph Glinski, Resource Center

• Stephan Gaj, Office of Asset Management

• Joseph Hausman, Office of Highway Policy Information

• Rick Drumm, Indiana Division Office

• Lincoln Cobb, Office of Safety Research and Development

• Brian Gardner, Office of Planning

• Heather Rothenberg, Office of Safety

Their contributions helped shape the guidance that States will be receiving to improve their 

roadway data to help support data driven safety decisions. 
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TECHNICAL REPORT DOCUMENTATION FORM

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

4. Title and Subtitle 5. Report Date

Roadway Data Improvement Program: Informational Resource June 2012

6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. Barbara Hilger DeLucia, Robert A. Scopatz, Nancy Lefler

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc.

8300 Boone Boulevard

Suite 700

Vienna, VA 22182

10. Work Unit No.

11. Contract or Grant No.

DTFH61-10-D-00022 

12. Sponsoring Agency Name and Address 13. Type of Report and Period

Federal Highway Administration Office of Safety

1200 New Jersey Ave., SE

Washington, DC 20590

Final Report, June 2011 - August 2012

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Robert Pollack. 

16. Abstract

17. Key Words: 18. Distribution Statement

benefit cost analysis, safety data, roadway inventory data, traffic 

data, data collection, data quality No restrictions.

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages: 22. Price

Unclassified Unclassified 106 N/A

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

CHAPTER 1—INTRODUCTION TO THE ROADWAY DATA IMPROVEMENT 

PROGRAM (RDIP)........................................................................................................................ 1

Purpose of the RDIP .................................................................................................................................1

Purpose of the RDIP Supplemental Information Resource...............................................................................................................1

Intended Audience .........................................................................................................................................................................................1

Background..................................................................................................................................................1

Traffic Safety Databases............................................................................................................................3

CHAPTER 2—THE ROADWAY DATA COMPONENT.......................................................... 9

Roadway Physical Characteristics....................................................................................................... 11

Roadway Access Control...................................................................................................................... 11

Roadway Intersections........................................................................................................................... 11

Road Inventory Maps............................................................................................................................. 12

Roadway Inventory Database............................................................................................................... 13

Traffic Characteristics Database.......................................................................................................... 14

Structures Database............................................................................................................................... 14

Highway-Railroad Grade Crossing Database ................................................................................... 15

Pavement Management Systems (PMS).............................................................................................. 15

Roadway Assets ...................................................................................................................................... 16

Highway Performance Monitoring System (HPMS) ........................................................................ 18

Highway Safety Information System (HSIS)....................................................................................... 19

Data-Driven Decisions.......................................................................................................................... 19

CHAPTER 3—TRANSPORTATION PLANNING AND COORDINATION ....................... 20

Context Sensitive Solutions (CSS)...................................................................................................... 21

Roadway Design...................................................................................................................................... 22

State Transportation Improvement Program (STIP) ...................................................................... 23

Highway Safety Improvement Program (HSIP) ................................................................................ 24

Strategic Highway Safety Plan (SHSP)................................................................................................. 24

NHTSA Traffic Records Planning........................................................................................................ 24

Data Integration ...................................................................................................................................... 28

Distributed Data Systems..................................................................................................................... 29

Centralized Systems............................................................................................................................... 29

Enterprise-Wide Data Systems............................................................................................................ 30

Route-Milepost........................................................................................................................................ 35

Route-Reference ..................................................................................................................................... 36
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Link-Node................................................................................................................................................. 36

Route-Street Reference......................................................................................................................... 37

Geographic Coordinates....................................................................................................................... 38

Global Positioning System (GPS).............................................................................................................................................................38

Roadway Stationing ....................................................................................................................................................................................38

Roadway Geo-coding / Address Interpolation ....................................................................................................................................38

Roadway Realignment...............................................................................................................................................................................39

Geographic Information System (GIS) ............................................................................................... 41

CHAPTER 6—DATA COLLECTION TOOLS AND METHODS.......................................... 45

Roadway Photolog.................................................................................................................................. 45

Automating the Roadway Inventory................................................................................................... 46

Light Detection and Ranging................................................................................................................. 47

Global Positioning System (GPS)......................................................................................................... 48

Aerial Photography and Satellite Imagery.......................................................................................... 49

Traffic Counters...................................................................................................................................... 50

Additional Data Collection Systems for Roadway Information ................................................... 53

Quality Planning....................................................................................................................................... 55

Quality Assurance................................................................................................................................... 55

Quality Control....................................................................................................................................... 56

Benefit/Cost ............................................................................................................................................. 56

Customer Service ................................................................................................................................... 57

Data Quality Characteristics................................................................................................................ 60

Timeliness......................................................................................................................................................................................................60

Accuracy.........................................................................................................................................................................................................60

Completeness...............................................................................................................................................................................................60

Consistency/Uniformity..............................................................................................................................................................................60

Integration .....................................................................................................................................................................................................60

Accessibility....................................................................................................................................................................................................61

Data Quality Metrics.............................................................................................................................. 61

Data Collection and Management Tools........................................................................................... 71

Model Inventory of Roadway Elements (MIRE)................................................................................................................................71

Highway Safety Information System (HSIS).......................................................................................................................................71

Safety Data Analysis Tools.................................................................................................................... 71

Highway Safety Manual (HSM).............................................................................................................................................................71

Interactive Highway Safety Design Model (IHSDM).......................................................................................................................73

Fundamental Data Elements (FDE)......................................................................................................................................................73

Safety Analyst...............................................................................................................................................................................................73

Pedestrian and bicycle safety tools ..................................................................................................... 75

Pedestrian and Bicycle Crash Analysis Tool (PBCAT) ......................................................................................................................75
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Pedestrian Safety Guide and Countermeasure Selection System (PEDSAFE).........................................................................75

Bicycle Countermeasure Selection System (BIKESAFE)..................................................................................................................75

Intersection/Interchange Safety Analysis Tools................................................................................ 75

Interchange Safety Analysis Tool (ISAT)..............................................................................................................................................75

Surrogate Safety Assessment Module (SSAM)..................................................................................................................................76

CHAPTER 10—SUMMARY........................................................................................................ 77

APPENDIX A—EXPLANATION OF DATA FLOW DIAGRAMS ........................................ 78

APPENDIX B—EXAMPLE SYSTEM BUSINESS FUNCTIONS............................................ 80
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LIST OF TABLES

Table 1. Expanded Haddon Matrix with example highway safety categories. ...................................7

Table 2. Roadway inventory data quality metrics. .................................................................................62

Table 3. Traffic volume data quality metrics. ..........................................................................................62

Table 4. Crash data quality metrics...........................................................................................................65

Table 5. HSM data elements. ......................................................................................................................71

Table 6. IHSDM data requirements...........................................................................................................73

Table 7. SafetyAnalyst data elements..........................................................................................................74

LIST OF FIGURES

Figure 1. TRS Components. ..........................................................................................................................4

Figure 2. Traffic records system data flow diagram.................................................................................5

Figure 3. Sources of roadway data. .......................................................................................................... 10

Figure 4. Road inventory straight-line diagram...................................................................................... 12

Figure 5. Automated road inventory map............................................................................................... 13

Figure 6. Roadway view using LiDAR by Tennessee DOT................................................................. 17

Figure 7. Example of asset management reporting. .............................................................................. 18

Figure 8. An example of a context-sensitive process........................................................................... 22

Figure 9. Data integration process. .......................................................................................................... 28

Figure 10. Illinois DOT information flow for asset management. ..................................................... 31

Figure 11. Example of a roadway feature location measured by route and milepost................... 35

Figure 12. Example of a roadway feature location measured by route-reference. ....................... 36

Figure 13. Example of link-node method of identifying locations...................................................... 37

Figure 14. Example of a realignment that shortened a roadway. ...................................................... 39

Figure 15. Example of a realignment that lengthened a roadway. ..................................................... 39

Figure 16. Example of a segmented road model. .................................................................................. 40

Figure 17. Example of a linear referencing system (LRS). ................................................................... 40

Figure 18. Example of geographical analytical tools.............................................................................. 41

Figure 19. Iowa DOT example of using multiple location methods. ................................................ 44

Figure 20. A typical van used to collect the video images................................................................... 47

Figure 21. Aerial low-altitude LiDAR....................................................................................................... 48

Figure 22. Examples of passive infrared devices.................................................................................... 51
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Figure 23. Example of passive magnetic device...................................................................................... 52

Figure 24. Michigan DOT sharing maps on the web. ........................................................................... 58

Figure 25. Example of continuous hot spot examination. .........Error! Bookmark not defined.

Figure 26. Example of sliding window hot spot examination. ..Error! Bookmark not defined.
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ACRONYMS 

4 Es Engineering, Education, Enforcement, Emergency Medical Services

AADT Annual Average Daily Traffic

AASHTO American Association of State Highway and Transportation Officials

ADT Average Daily Traffic

AH Milepost Ahead (denotes the original mileposts on a realigned roadway)

ALDOT Alabama Department of Transportation

ANSI American National Standards Institute

BIKESAFE Bicycle Countermeasure Selection System

BK Milepost Back (refers to new mileposts on a realigned roadway)

C&P Condition and Performance 

Caltrans California Department of Transportation 

CDIP Crash Data Improvement Program

CFR Code of Federal Regulations

CHP California Highway Patrol

CMF Crash Modification Factor

CODES Crash Outcome Data Evaluation System

CRF Critical Rate Factor 

CSD Context Sensitive Design

CSS Context Sensitive Solution

DFD Data Flow Diagram

DGPS Differential Global Positioning System

DHMS Digital Highway Measurement System

DOT Department of Transportation

EMS Emergency Medical Services

FARS Fatality Analysis Reporting System

FDE Fundamental Roadway and Traffic Data Elements 

FHWA Federal Highway Administration

FRA Federal Railroad Administration

GIS Geographic Information System

GPS Global Positioning System

GUI Graphical User Interface 

HAL High Accident Location
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HD High Definition

HOV High Occupancy Vehicle

HPMS Highway Performance Monitoring System

HRGX Highway-Railway Grade Crossing

HRRR High Risk Rural Roads

HSIP Highway Safety Improvement Program

HSIS Highway Safety Information System

HSM Highway Safety Manual

IHSDM Interactive Highway Safety Design Model

ISAT Interchange Safety Analysis Tool

IT Information Technology

LiDAR Light Detection And Ranging

LRM Linear Referencing Method

LRS Linear Referencing System

MAP-21 Moving Ahead for Progress in the 21st Century

MDEV Million Daily Entering Vehicles

MIDRIS Model Impaired Driving Records Information System

MIRE Model Inventory of Roadway Elements

MIS Management Information System

MIRE Model Inventory of Roadway Elements

MMUCC Model Minimum Uniform Crash Criteria 

MOU Memoranda of Understanding

MPO Metropolitan Planning Organization

MVM Million Vehicle Miles Traveled

NBI National Bridge Inventory

NCHRP National Cooperative Highway Research Program

NEMSIS National Emergency Medical Services Information System

NHPN National Highway Planning Network

NHS National Highway System

NHTSA National Highway Traffic Safety Administration

NS Non-regulatory Supplement to the U.S. Code

PBCAT Pedestrian and Bicycle Crash Analysis Tool

PDA Personal Digital Assistant

PEDSAFE Pedestrian Safety Guide and Countermeasure Selection System
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PMS Pavement Management Systems

RDIP Roadway Data Improvement Program

RI Roadway Inventory

RPC Regional Planning Commission

RSDP Roadway Safety Data Program

SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for 

Users 

SHSP Strategic Highway Safety Plan

SPCS State Plane Coordinate System

SSAM Surrogate Safety Assessment Model 

STIP State Transportation Improvement Program

TASAS Traffic Accident Surveillance and Analysis System

TAT Technical Assistance Team

TFHRC Turner-Fairbank Highway Research Center

TRCC Traffic Records Coordinating Committee

TRS Traffic Records Systems

U.S. DOT United States Department of Transportation

USC United States Code (as in Title 23 USC 109)

VHS Video Home System (Analog Recording Videotape-Based Cassette)

VIN Vehicle Identification Number

VMT Vehicle Miles Traveled
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CHAPTER 1—INTRODUCTION TO THE ROADWAY DATA IMPROVEMENT 

PROGRAM (RDIP)

PURPOSE OF THE RDIP

The purpose of the Roadway Data Improvement Program (RDIP) is to help transportation 

agencies improve the quality of their roadway data to support their safety initiatives. These 

improvements may be in terms of the data elements collected, data collection practices, geospatial data referencing, data storage, data maintenance, and linkage of roadway-related data 

with other safety data. 

RDIP provides roadway database managers and other traffic safety professionals a tool to assist 

them in identifying, defining, measuring, and ultimately improving, the quality of the data within 

their roadway databases. The quality of the data can be characterized by the timeliness, 

accuracy, completeness, consistency, integration, and accessibility of the roadway data.

Purpose of the RDIP Supplemental Information Resource

The intent of the RDIP Supplemental Information Resource is to provide an overview of data 

collection practices, transportation planning and coordination, data management, and using 

roadway data for safety. The information provided in this Supplemental Information Resource

is a result of a thorough review of current and emerging roadway data practices, and 

incorporates information provided by a Technical Working Group made up of representatives 

from States on the forefront of roadway data collection, analysis, management, and integration.

These States included Illinois, Iowa, Michigan, North Carolina, Tennessee, and Virginia. 

Intended Audience

The intended audience of this Supplemental Information Resource is any State or local 

department of transportation (DOT) professional interested in improving the roadway and 

traffic data in their agency. This includes data collectors, managers, and users from safety, 

planning, roadway inventory, traffic, asset management, operations, maintenance, pavement, 

information technology (IT), Traffic Records Coordinating Committees (TRCC) members, and 

others. While the primary focus is on improving roadway data for use in safety, improving the 

quality of data can benefit all involved in the collection, management, and use of roadwayrelated data. 

BACKGROUND 

Highway safety analysis is evolving, and the importance of quality data has never been more 

apparent. Quality safety data are the foundation for highway safety decisions. Much of the 

effort in the safety community in previous years has concentrated on crash data; however, 

crash data are only part of the picture. Roadway and traffic data are also essential. By 
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incorporating roadway and traffic data into their network screening, prioritization, and 

countermeasure selection analysis, agencies can better identify safety problems and prescribe 

solutions to improve safety and make more efficient and effective use of their safety funds. 

Crash data alone are useful, but leave safety practitioners with purely reactive approaches—

identifying the locations where crashes have already happened. With the addition of traffic 

volume data it is possible to develop estimates of the expected crash frequency and compare 

crash rates for roadways with vastly different levels of service. As detailed roadway inventory 

information is added to the mix, safety practitioners can now develop a more in-depth 

understanding of the roadway attributes that contribute to crash risk thus allowing them to 

adopt a proactive approach seeking out those factors associated with a high risk of crashes and 

addressing sites that share those features in common. This process is known as the “systemic 

approach” to safety.

In addition to numerous safety analysis tools and methods developed by the States, a new 

generation of tools is being developed to help States to identify safety issues and provide 

recommendations for improvements. These tools include the 2010 Highway Safety Manual 

(HSM), the Interactive Highway Safety Design Model (IHSDM), Safety Analyst, as well as 

American Association of State Highway and Transportation Officials’ (AASHTO) National 

Cooperative Highway Research Program (NCHRP) Series 500 Data and Analysis Guide, which 

all require crash, roadway, and traffic data to achieve the most accurate results. 

The Federal Highway Administration (FHWA) has been on the forefront of the effort to 

improve the quality of safety data, including roadway and crash data. In 2007, FHWA released 

the initial Model Minimum Inventory of Roadway Elements (MMIRE), a recommended listing of 

roadway and traffic data elements crucial for States and local agencies to collect and 

incorporate into their safety programs. A recently released version, Model Inventory of 

Roadway Elements - MIRE Version 1.0, shows FHWA’s continued commitment to the evolution 

of quality safety data, see http://safety.fhwa.dot.gov/rsdp/.

As part of this evolution, the FHWA Office of 

Safety developed the Roadway Safety Data 

Program (RSDP) to advance State and local safety 

data systems and expand safety data analysis and 

evaluation capabilities. The RSDP includes data 

guidance, resource development, technical 

assistance and training all aimed at improving the 

collection, analysis, management, and expansion 

of roadway data for use in safety programs and 

decision-making. Technical assistance programs 

that fall under the RSDP umbrella, in addition to 
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RDIP, include the Roadway Data Extraction Technical Assistance Program (RDETAP), and 

customized Technical Assistance. More information about these programs may be found at 

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

FHWA patterned the RDIP after the Crash Data Improvement Program (CDIP), which it 

initiated to help States improve the quality of their crash data. The CDIP teaches a State about 

methods to improve their crash data. The CDIP is now operated by the National Highway 

Traffic Safety Administration (NHTSA). Similar to CDIP, the RDIP concentrates on providing 

recommendations for improving the quality of their roadway-related data. The RDIP also 

provides examples of State and Federal methods and tools for safety analyses to illustrate to 

practitioners the necessity of quality roadway and crash data. 

In 2012, FHWA completed the Roadway Safety Data Capabilities Assessment, which

documented the status of State roadway data. The assessment allowed FHWA to identify 

national gaps and focus resources accordingly, see http://safety.fhwa.dot.gov/rsdp/. The RDIP 

builds upon the findings from the Capabilities Assessment. It conducts more in-depth 

examinations of state practices and procedures surrounding the main Capabilities Assessment 

topics.

These recent efforts at FHWA build on a strong history at the State and federal levels of 

developing the data necessary for data-driven decision making for safety. The following 

sections provide further background and historical context for the present efforts.

TRAFFIC SAFETY DATABASES

At the time of passage of the Highway Safety Act of 1966, State centralized Traffic Records 

Systems (TRS) generally contained basic files on crashes, drivers, vehicles, and roadways. Some 

States added data on traffic safety-related education, either as a separate file or as a subset of 

the driver file. As traffic safety programs matured, many States incorporated emergency 

medical services (EMS) and citation/conviction files for use in safety program decision making. 

Additionally, some States and localities maintain a safety management file that consists of 

summary data from the central files that can be used for problem identification and safety 

planning.

As the capabilities of computer hardware and software systems increased and the availability of 

powerful systems expanded to the local level, many States adopted a more distributed model of 

data processing. For this reason, the model of a TRS needs to incorporate a view of 

information and information flow, as opposed to focusing only on the files in which that 

information resides.

Under this more distributed model, it does not matter whether data for a given system 

component are housed in a single database on a single computer, or spread throughout the 

State on multiple local systems. What matters is whether the information is available to users, 
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in a form they can use, and that these data are of sufficient quality to support its intended uses. 

Figure 1 shows the major data components of a TRS that include:

• Crash Information.

• Roadway Information.

• Exposure (traffic volumes). 

• Driver Information.

• Vehicle Information.

• Citation/Adjudication Information.

• Injury Surveillance Information.

Together, these components provide information 

about places, property, and people involved in 

crashes and about the factors that may have 

contributed to the crash or traffic stop. The TRS 

also contains information to help judge the relative 

magnitude of problems identified through analyses 

designed to account for differences in exposure 

(normalization), cost effectiveness, and 

performance level data to support countermeasure management.

Figure 2 is a data flow diagram (DFD) that provides an overview of the potential sources of 

these data and data flows. The DFD provides an example of the possible interactions among all 

of the TRS data sources and serves as a first step in designing a system to integrate the various 

data sources. See Appendix A for a key to interpreting the DFD.

Figure 1. TRS Components.
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Figure 2. Traffic records system data flow diagram.

Enforcement/

Adjudication

Information

Driver License

History

Driver

History

Driver

Information

Convictions

Dispositions 

Crashes, Operator

Reports 

Crash

Information

Injury

Surveillance

Information

Vehicles, Titles,

Registrations

Titles, Registrations,

Inspections

Driver

History

EMS Run Reports,

Trauma Data,

Discharge Summaries, 

Rehabilitation

Vital Statistics,

Cost Data

VIN, Tag,

Owner 

Roadway

Information

Traffic, Pavement

Condition,

Inventory, Ratings

Reports

Road

Characteristics,

Location,

Structures

Crash

Characteristics 

Citations,

Arrests 

Reports

EMS Response,

BAC, Drug, Death

Information Analysis,

Reports 

Reports

Crash

Characteristics 

Roadway

and Traffic

Data

Inspections,

Carrier Data

Vehicle

Information
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In each of the major components of the TRS, there is an obvious need for aggregate data to 

“roll up” from local, to State, to national levels. Agencies meet these needs at the State level by 

imposing standards on data collection. The Federal level was slower to evolve such 

standardization, but there are now recommended guidelines for crash data (the Model 

Minimum Uniform Crash Criteria—MMUCC), impaired driver tracking system (the Model 

Impaired Driving Records Information System—MIDRIS), and roadway data (the MIRE guideline 

already referenced). Further, there are national standards for fatal crashes (the Fatality Analysis 

Reporting System—FARS), commercial motor vehicle crashes (the SAFETYNET system), and 

EMS run reports (the National Emergency Medical Services Information System—NEMSIS).

There have also been advances in how we approach safety analysis and approach the problem 

of reducing the frequency and severity of crashes. The Haddon Matrix, named after its 

developer William Haddon, the first Administrator of the National Highway Traffic Safety 

Administration (NHTSA), provides an overview of the contents of a TRS. It provides a valuable 

framework for viewing the primary effects of human, vehicle, and environmental factors and 

their influence before, during, and after a crash event. Table 1 shows an expanded Haddon 

Matrix that was developed by R. Quinn Brackett, Ph.D. and staff from Data Nexus, Inc., for the 

original Traffic Records Assessment reports starting in the early 1990s. This matrix has been 

incorporated in numerous safety-related publications since that time.

The Haddon Matrix provides a meaningful way to examine primary effects of contributing 

factors on crash frequency and severity. It helps decision makers consider countermeasures 

designed to address specific contributing factors. In recent years, with availability of more 

detailed data analyses, awareness has grown about the interactions among contributing factors. 

For example, the factors that exist prior to a crash may result in a crash while post-crash 

measures may affect time required to transport injured parties to a medical facility for 

treatment.
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Table 1. Expanded Haddon Matrix with example highway safety categories.

Human Vehicle Environment

Pre-Crash

⋅ Age

⋅ Gender

⋅ Experience

⋅ Alcohol/Drugs

⋅ Physiological Condition

⋅ Psychological Condition

⋅ Familiarity with Road & 

Vehicle

⋅ Distraction

⋅ Conviction & Crash 

History

⋅ License Status

⋅ Speed

⋅ Crash Avoidance

⋅ Vehicle Type

⋅ Size & Weight

⋅ Safety Condition, Defects

⋅ Brakes

⋅ Tires

⋅ Vehicle Age

⋅ Safety Features Installed

⋅ Registration

⋅ Visibility

⋅ Weather/Season

⋅ Lighting

⋅ Divided Highways

⋅ Signalization

⋅ Geographic Location

⋅ Roadway Class, Surface, 

Cross-Section, Alignment, etc.

⋅ Structures

⋅ Traffic Control Devices, Signs, 

Delineations, and Markings

⋅ Roadside Appurtenances, 

Buildups, Driveways, etc.

⋅ Volume of Traffic

⋅ Work Zone

⋅ Animal Range Land & Seasonal 

Movements

Crash

⋅ Belt Use

⋅ Human Tolerance

⋅ Size

⋅ Seating Position

⋅ Helmet Use

⋅ Crash-Worthiness

⋅ Passenger Restraints

⋅ Airbags and Airbag Shutoff

⋅ Guardrails

⋅ Median Barriers

⋅ Breakaway Posts

⋅ Rumble Strips and Other 

Safety Devices

⋅ Maintenance Status of 

Roadway and Devices

Post-Crash

⋅ Age

⋅ Physical Condition

⋅ Insurance Status

⋅ Access to Health Care

⋅ Driver Control Actions

⋅ Court Actions

⋅ Probation

⋅ Post-Crash Fires

⋅ Fuel Leakage

⋅ Power Cell Securement

⋅ Hazardous Materials

⋅ Title

⋅ Traffic Management

⋅ Bystander Care

⋅ EMS System

⋅ First Responders

⋅ Hospital Treatment

⋅ Long-Term Rehabilitation
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The Haddon Matrix remains the pre-eminent summary of the factors that contribute to the 

frequency and severity of crashes. It also must be recognized that the factors interact. Modern 

safety analysis looks at all the factors, and their interactions, seeking to find not the single cause 

of any individual crash, but the multiple factors that combine and interact to produce the 

pattern of crashes and crash outcomes that we see.

The tools and strategies for safety analysis have been growing both in number and

sophistication since Haddon’s pioneering days. The HSM, and the associated tools and 

resources including Safety Analyst, the IHSDM, the Crash Modification Factors (CMF) 

Clearinghouse, summarize these advances. These advanced methods and tools represent the 

current state of the practice arrived at through decades of research and implementation by 

States and the federal government. They are covered in greater detail later in this document.
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CHAPTER 2—THE ROADWAY DATA COMPONENT

The roadway data component of a TRS refers to the collection, storage, and use of various 

databases that contain information about how an agency constructed and maintained the 

roadway throughout its lifecycle. In the more 

specific area of traffic records and safety analyses, it 

is crucial to know the location where a crash 

occurs and any roadway and traffic characteristics 

that may have contributed to the crash. As part of 

a traffic records system, roadway data helps to 

identify the environment where motor vehicle 

crashes occur, any characteristics of the location 

that may have contributed to the occurrence or 

severity of the crashes, and suggest where engineering and/or enforcement may be changed to 

improve the safety of that location. In advanced safety analysis, the knowledge of where 

crashes have not occurred is also crucial to our ability to identify those roadway characteristics 

that are associated with increased crash risk. There must be a method of linking roadway data 

with crash data to support analyses, research, and public policy decisions.

Roadway data is also essential for agencies to be able to use the systemic approach to safety. 

The systemic approach involves implementing improvements based on the identification of highrisk roadway features correlated with specific crash types. This approach helps agencies 

broaden their safety efforts and consider crash risk as well as crash history when making 

determinations for safety improvements.

Figure 3 illustrates the sources of roadway data for the TRS and how these sources could 

potentially be linked to form a comprehensive roadway data management system. The roadway 

DFD is an expansion of the roadway process shown as one of the integrated processes in the 

overview DFD of Figure 2. This roadway DFD provides greater detail of the sources of 

roadway-related information and the interactions among them.

The roadway data component is a 

means by which States can describe the 

location and physical characteristics of 

roadways where crashes occur.
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Field Data 

Collection Process

Roadway Data

Process

Traffic Data 

Process

MPOs &

Localities

Traffic Control, Speed,

Signs, Inventories

Project Plans, Change 

Notices, As-built Plans

Local Inventory

& Feature Data

Traffic Data Reports

Crash Export

ATR, WIM, manual counts

Traffic Statistics

Off-System Network 

& Inventories 

GIS Mapping 

Process

Traffic Data Export

Data Exports

Pavement Database 

Digitized Data

 Crash Database

Bridge Database 

PMS Data Export

Bridge Data Export

Map Exports

Spatial Data 

HPMS Sample Export

Roadway 

& 

Traffic Extract

Roadway Analyses

Web Portal Data

Problem ID, Project Selection,

Program Evaluation

Roadway Integrated Database

Figure 3. Sources of roadway data.
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The following provide examples of various roadway databases and what elements may be 

included in each of them.

ROADWAY PHYSICAL CHARACTERISTICS

Data about a roadway’s physical characteristics include elements such as:

• Lanes (e.g., total number of lanes, number by direction, average width).

• Shoulders (e.g., shoulder type and average width).

• Medians (e.g., median type and width).

• Speed limit (e.g., design speed, posted speed).

• Design features (e.g., curbs, sidewalks, striping, bikeways, structures).

• Pavement (e.g., age and type, serviceability index, roughness index).

ROADWAY ACCESS CONTROL

Data about a roadway’s access control may include elements such as:

• Access control (e.g., publicly, or privately controlled).

• Toll type and charge (e.g., toll road charge or not).

• High occupancy vehicle (HOV) operations (e.g., identifies segments with HOV 

operations).

• Land control (e.g., control of land through which a roadway segment passes).

ROADWAY INTERSECTIONS

Data about a roadway’s intersections may include elements such as:

• Intersection type (e.g., circle, roundabout, T-intersection, on or off ramp, etc.).

• Number of lanes (e.g., number of mainline through lanes or turn lanes).

• Traffic control (e.g., signal, sign).

• Annual average daily traffic (e.g., AADT of intersection and legs, year of count).

These various roadway databases may be located at a city or county public works department, 

a regional planning agency, or a State department of transportation. These inventories 

generally will exist in the form of a relational database used with a geographic information 

system (GIS), an asset management system, or individual legacy systems that focus on one type 

of roadway component (e.g., bridges, rail grade crossings, pavement management). In some 

instances, GIS shapefiles are transported among jurisdictions such as, from local systems to 

state systems. The following are examples of sources of roadway-related data. Not all of these 

roadway data sources are available in every jurisdiction or for every section of roadway.
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ROAD INVENTORY MAPS 

Road inventory (RI) maps, or straight-line charts, are the linear representation of a roadway 

segment drawn from as-built plans or the most current construction or design plans1. Agencies 

often identify locations in terms of control/section and job number. Whether drawn manually 

or with specialized software, these diagrams and the as-built plans are valuable sources of 

roadway characteristics because of their portability and accessibility by field personnel. Figure 4 

is an example of a manual RI map, see 

http://onlinemanuals.txdot.gov/txdotmanuals/szn/developing_strip_maps.htm. Figure 5 is an example 

of a digitized road inventory map from Michigan’s RoadSoft software, see 

http://www.roadsoft.org/asset-management/road-inventory.

Figure 4. Road inventory straight-line diagram.

 

1 For the purposes of this document, the term “as-built plans” refers to the as-built plans or the most current 

construction or design plans available. 
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Figure 5. Automated road inventory map.

ROADWAY INVENTORY DATABASE

An example of one way an agency could automate its roadway inventory records is by using the 

characteristics derived from the roadway inventory maps. Each record describes characteristics 

of a homogeneous roadway segment. A new section number indicates a change in the physical 

characteristics or geometry of the roadway; for example, transition to wider lanes, more lanes, 

a bridge feature, different pavement material, etc. If future construction or maintenance 

changes only a portion of the roadway segment, agencies break the roadway inventory record 

into two records representing homogeneous segments. Traditionally, some agencies may have 

coded roadway inventory records with the control/section numbers matching the inventory 

maps, and the beginning and ending milepoint or reference marker for the segment. Agencies 

add the new location coding to reflect the new segments and allow the changes to be reflected 

on any GIS mapping. Aspects of a roadway inventory database may include: 

• Milepoints are represented to thousandths of a mile from the beginning of a semi-fixed 

reference point (e.g. a state or county boundary line). As a document-based calculation 

of distance, measures are useful in determining distances from automated database 

records. Note: While .001 miles is a common point of accuracy, it is not always used. 

States generally measure their roads in a cardinal direction, starting at a jurisdictional 

point that is stable, like a State or County line.

• Reference markers (also called mileposts) are physical markers or tags that can be found 

along the roadway at pre-defined intervals; e.g., on interstate highways these are located 
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consistently at some interval from one-tenth mile to one-mile. On other types of 

roadways, the markers may be further apart and may not be as visible to the traveling 

public (e.g., a metal tag attached to the back of other signage. Using reference markers, 

a location is defined in terms of some fixed distance from the marker. Reference 

markers are useful to law enforcement officers or highway workers who must identify a 

location while in the field. However, reference markers are not always geospatially 

accurate.

• Coordinates (latitude/longitude), obtained global positioning systems (GPS) or 

automated map locators, are becoming more commonly used to identify roadway 

locations. This may be for initial creation of a roadway network for use with a GIS or 

may be used to identify the location of individual incidents. Even when coordinates are 

used to identify a location, other location coding methods may be needed to link to 

older legacy databases. In fact, most highway departments maintain extensive crossreference tables to link data stored with different methods of location coding.

TRAFFIC CHARACTERISTICS DATABASE

Traffic data reflects the volume and characteristics of vehicles that travel along the roadway. 

Agencies often store traffic counts, such as AADT, in a manner that allows analysts to integrate 

traffic data with the roadway inventory database. The FHWA maintains an extensive library of 

research and operational studies about traffic characteristics see 

http://www.fhwa.dot.gov/policyinformation/hpms.cfm. 

Data about a roadway’s traffic may include elements such as:

• AADT (e.g., whether actual/estimated and year recorded).

• Counts (e.g., average daily traffic [ADT] hourly/weekly).

• Adjustments (e.g., compensation, seasonal and directional factors, etc.).

• Site (e.g., location of counts, project, interstate corridor, etc.).

• Vehicle classification (e.g., number of buses, trucks, axles, trailers, etc.).

STRUCTURES DATABASE

The FHWA National Bridge Inventory (NBI) is a database of approximately 600,000 of the 

Nation's bridges located on public roads, as well as publicly-accessible bridges on Federal lands. 

States provide data to FHWA for the NBI. There is a unique identification code (e.g. structure 

file number) for each bridge that allows it to be located spatially for mapping. The database 

includes:

• Material of the bridge components, deck, and deck surface.

• Operational conditions such as structure age, construction year, rehabilitation year.
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• Type of services and traffic carried over and/or under the structure.

• Number of the lanes over and/or under the bridges.

• ADT.

• Average daily truck traffic.

• Information relating to bypass or detours.

Furthermore, the bridge inventory contains information about inspection data, ratings assigned 

by inspectors and appraisal results. For additional information about bridges and other 

structures, see http://www.fhwa.dot.gov/bridge.

HIGHWAY-RAILROAD GRADE CROSSING DATABASE

FHWA established a formalized Highway Safety Improvement Program (HSIP) database to 

ensure that safety programs are carried out in an organized, systematic manner where the 

greatest benefits can be achieved. The Highway-Rail Grade Crossings Program is one of the 

infrastructure-related HSIP programs. 

Data about a highway’s railroad grade crossings may include elements such as:

• Crossing number (i.e., from U.S. DOT / American Association of Railroads).

• Crossing signal type (e.g., flashing lights, bells, signs, etc.).

• Number of tracks (e.g., mainline and other).

• Train speeds (e.g., maximum speed or lower speed for trains at crossing).

• Materials (e.g., road surface before or between tracks or rails).

All crossings (i.e., public, private and pedestrian, both at-grade and grade separated underpasses 

and overpasses) have a U.S. DOT Crossing Inventory Number assigned and posted at the 

crossing. In addition to State-maintained databases, the Federal Railway Administration (FRA) 

maintains railroad safety data including accidents and incidents, inspections, and highway-rail 

crossing data. Agencies include coordinates (longitude and latitude) in the record to identify 

the location along with the crossing inventory number. For more detailed information about 

these data, see http://safety.fhwa.dot.gov/xings.

PAVEMENT MANAGEMENT SYSTEMS (PMS)

GIS can be used as a platform for a pavement management system (PMS) to visually display the

pavement attributes for a section of roadway and its location. Reference markers may be 

included because of the need to monitor or test pavement sections in the field. A PMS may 

include video logs, as well as data about pavement type, pavement condition, friction, failures, 

and serviceability. For more detailed information, see FHWA’s Pavement Publications at 

http://www.fhwa.dot.gov/pavement/pub_listing.cfm.
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A PMS is one element of roadway maintenance. Pavements represent the largest capital 

investment in any modern roadway system. Maintaining and operating pavements on any 

roadway system typically involves complex decisions about how and when to resurface or apply 

other treatments to keep the roadway performance and operating costs at reasonable levels. A 

PMS usually consists of three major components:

1. A system to collect roadway condition data on a regular basis.

2. A computer database to sort and store the collected data.

3. An analysis program to evaluate repair or preservation strategies and to suggest cost 

effective projects to maintain roadway conditions.

Most jurisdictions will combine the PMS analysis results with planning needs and political 

considerations to develop annual roadway repair/preservation programs. Data collection 

focuses primarily on pavement condition and ranges from simple “windshield surveys” (often 

with subjective results) to the use of elaborate testing vehicles that measure smoothness, skid 

resistance, faulting, and cracking in the road surface. Agencies may link to other data in a PMS, 

such as traffic volumes and physical descriptions of the roadway (width, sub-surface, 

shoulder/edge, etc.).

The analysis component of a PMS attempts to predict how long a pavement segment will last 

with a certain kind of repair under the given traffic loads, climate, and other factors. This 

analysis is based primarily on the collective experience of roadway experts and on the historical 

costs incurred for repairs or reconstruction. More sophisticated analysis packages also predict 

annual repair costs, overall system performance, and expected pavement conditions on related 

routes within planning corridors. The intent of the analysis is to identify the most cost-effective 

ways to maintain a roadway system in satisfactory condition. Temporality is important in a PMS 

since it’s critical in analyzing pavement data similar to its importance in analyzing safety data.

ROADWAY ASSETS

Data about a roadway’s characteristics or attribute may include elements such as:

• Hardware Type (e.g., signal, guardrail, sign, crash barrier, light pole, etc.).

• Maintenance (e.g., State-maintained or local-maintained).

• Condition (e.g., appraisal of condition, conformity, distance from right-of-way).

• Electronic (e.g., cameras, message signs, meters, advisory radio, monitor).

Not all of these data are available in every jurisdiction or for every section of roadway. 

However, a national guideline, MIRE, to provide a model of what roadway and traffic data 

elements a jurisdiction should collect to support data-driven decisions for safety. In many 

States asset management systems are being used to coordinate the management of various 

inventory databases. 
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Various specialized inventories may be available, particularly for a local or regional 

transportation agency. These inventories define numerous features and activities, for example, 

billboards, adopt a highway, permits, work zones, roadway type, speed limits, safety devices 

(guard cable), signals, lighting, flashers, construction projects, and other types of roadwayrelated data. 

The Tennessee DOT has been able to successfully create asset inventories. The DOT extracts 

the data collected from both Light Detection and Ranging (LiDAR) and Photolog. Figure 6 is an 

example using LiDAR and related software. 

Figure 6. Roadway view using LiDAR by Tennessee DOT

Figure 7 shows an example of how assets can be mapped to illustrate their location on the 

roadway. For a more detailed look at asset management and inventories, see 

http://www.fhwa.dot.gov/asset/. 
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Figure 7. Example of asset management reporting.

HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS)

The Highway Performance Monitoring System (HPMS) is a national level highway information 

system program that provides data on the extent, condition, performance, use, and operating 

characteristics of U.S. highways. The FHWA Office of Highway Policy Information manages the 

HPMS. Through the HPMS, FHWA requires States collect roadway characteristic data and 

report those data to FHWA. Currently, the HPMS database contains over 123,000 highway 

segments. 

The number of data elements and extent of roadway that they have to be collected on vary 

based on the type of roadway. The data required for the annual submittal of HPMS includes: (1) 

limited data on all public roads (Full Extent), (2) more detailed data for designated sections of 

the arterial and collector functional systems (Sample Panel), and (3) area-wide summary 

information for urbanized, small urban and rural areas (Summary). In addition, with 2010+ 

Reassessment, States are also required to submit a linear referencing system for the Full Extent 

and Sample Panel data on selected highway functional systems, see 

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/.

The major purpose of the HPMS is to support a data driven decision process at the national, 

State, and local level for meeting the nation’s transportation needs. FHWA uses the HPMS data 
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for highway system performance assessment, condition and performance (C&P) reporting, 

apportionment of Federal-aid highway funds, reporting of highway statistics, and other 

transportation related analysis.

HPMS information is also available for highway and transportation planning and other purposes 

through the report of annual Highway Statistics or through other media, see 

http://www.fhwa.dot.gov/policyinformation/hpms.cfm.

HIGHWAY SAFETY INFORMATION SYSTEM (HSIS)

The Highway Safety Information System (HSIS) is a roadway-based system operated and 

maintained by FHWA that provides quality data on a large number of crash, roadway, and traffic

variables. This multi-State system contains information already being collected by agencies for 

managing the highway system and studying highway safety. In some cases, special inventories 

such as intersections, interchanges, and roadside hardware are available. The data are acquired 

annually from a select group of States and one municipality, processed into a common 

computer format, documented, and prepared for analysis. Data users (practitioners, 

researchers, etc.) can request specific data from FHWA for approved transportation research. 

See http://www.hsisinfo.org/ for more information. 

DATA-DRIVEN DECISIONS

Many other systems could be included in the discussion in this Chapter. The important point is 

not just that the data exist, but that they exist to be used. Data-driven decision-making is an 

approach that has permeated all levels of government. Practitioners recognize the fact that we 

make better quality decisions when we base those decisions on valid facts describing the real 

world. It is an approach that reduces the reliance on individual intuition and focuses more 

attention on quantifiably proven strategies—the things we know work.

This is no easy endeavor. In fact, decision-making is still as much an art as it is a science. There 

are often not complete and accurate data; many of the data sources are out-of-date; and it is 

often difficult to convince decision-makers that the investment in data and advanced data 

analysis is worthwhile. It also is true that having data does not necessarily simplify the decisionmaking process. In highway traffic safety, in fact, the more we develop our quantitative 

understanding of the situation, the more factors we realize should be incorporated into our 

analyses. The Haddon Matrix is not a description of discrete influences sorted neatly into rows 

and columns, but rather a framework reminding us that the crashes and crash outcomes we see 

are the result of multiple interacting influences. The growth of multi-disciplinary approaches to 

traffic safety is the natural consequence of our increased understanding of the situation. The 

state-of-the-practice is to work jointly with engineering, enforcement, emergency care 

providers, educators, and others to solve the problems. Each specialty has a contribution to 

make to solutions that are most effective when coordinated.
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CHAPTER 3—TRANSPORTATION PLANNING AND COORDINATION

States conduct continuing, comprehensive, and collaborative intermodal statewide 

transportation planning that facilitates the efficient and economic movement of people and 

goods in all areas of the State, including metropolitan areas. Planning a transportation system is 

a continuous process influenced by changes in traffic patterns, congestion, air quality, safety, 

technology, politics, events, culture, legislation, and rule making. Transportation planning 

occurs at the State, region, and local levels of government. The FHWA has broadly defined the 

planning process as a series of steps: 

• Monitoring existing conditions.

• Forecasting future population and employment growth.

• Assessing projected land uses in the region/identifying major growth corridors.

• Identifying problems and needs, and analyzing through detailed planning studies, various 

transportation improvements.

• Developing alternative capital and operating strategies for the movement of people and 

goods.

• Estimating the impact of the transportation system on air quality within the region.

• Developing a financial plan that covers operating costs, maintenance of the system, 

system preservation costs, and new capital investments.

Transportation agencies need to make decisions in an environmentally sensitive way, using a 

comprehensive planning process that includes the public and considers land use, development, 

safety, and security. The Volpe National Transportation Systems Center, Analysis of State Long 

Range Transportation Plans, identified the following options to take into account when making 

decisions:

• Factors required by legislation.

• Type of plan (such as needs based, vision-based, policy, project, corridor, or fiscally 

realistic).

• Multi-modal planning (includes water, aviation, transit, rail, bike, & pedestrian).

See http://www.fhwa.dot.gov/planning/processes/statewide/practices/anaswplans.cfm for more 

information. 

The current transportation legislation, MAP-21, identifies the following performance factors 

that need to be considered in the statewide transportation planning process:

• Safety 

• Infrastructure conditions
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• Congestion reduction

• System reliability

• Freight movement and economic vitality

• Environmental sustainability

• Reduced project delivery delays

As is evident from this list, data play a central role in the decision making in all aspects of a 

project plan, and the information needs include both roadway and non-roadway data. Since 

planning will vary by State and local needs, the data used vary by jurisdiction. Current roadway 

data collected for the planning process may include ADT, crash history and severity, and 

specific roadway characteristics such as number of lanes, pavement type, intersection type, and 

rail crossing signal type. The information available for planning purposes should also include 

historical data to assist planners in monitoring trends and forecasting future mobility needs. 

Examples of non-roadway data necessary for the planning process include air quality, 

demographics, household income, environmental detail, land use, population growth, and 

political policy.

CONTEXT SENSITIVE SOLUTIONS (CSS) 

FHWA defines “context sensitive solutions” (CSS) as “a collaborative, interdisciplinary 

approach that involves all stakeholders in developing a transportation facility that complements 

its physical setting and preserves scenic, aesthetic, historic, and environmental resources while 

maintaining safety and mobility. When transportation planning reflects community input and 

takes into consideration the impacts on both natural and human environments, it also promotes 

partnerships that lead to ‘balanced’ decision making.” 

Note that, in a typical road project (see Figure 8), the planning phase is followed by the “project 

development” phase, which includes these basic steps:

• Refinement of purpose and need.

• Development of a range of alternatives (including the option not to build).

• Evaluation of alternatives and their impact on the natural and built environments.

• Development of appropriate mitigation.

The project development phase includes a context sensitive design (CSD) component, which is 

related closely to the earlier CSS activity. CSD reflects an emerging national trend that strives 

to preserve and enhance a sense of place when building or expanding road, transit, bicycle, and 

pedestrian projects. The intent is to include broad community participation and comment in a 

project’s selection and design. Figure 8 shows an example of the context-sensitive process. 

Additional information and the partnerships involved, along with detailed technical information 

about CSS is available at http://contextsensitivesolutions.org/. 
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Figure 8. An example of a context-sensitive process.

The planning process in most jurisdictions also determines the functional classification of the 

roadway. The functional classification is used in transportation planning, in roadway design and 

for allocation of federal roadway improvement funds. The functional class hierarchy is:

• Principal Arterial - Interstate.

• Principal arterial other freeways and expressways.

• Principal arterial other.

• Minor arterial.

• Major collector.

• Minor collector.

• Local.

The concepts and criteria for classification were formulated based on the Federal-aid Highway 

Act of 1973. “Functional classification is the process by which streets and highways are 

grouped into classes, or systems, according to the character of service they are intended to 

provide. Most travel involves movement through a network of roads. It becomes necessary 

then to determine how this travel can be channelized within the network in a logical and 

efficient manner.” 

ROADWAY DESIGN

Beyond the planning and the project development phases is design. At this stage, agencies 

select a preferred design alternative, determine the functional class, and evaluate other design 

considerations for inclusion into the final design.

Federal law governs roadway design; Title 23 United States Code (USC) 109 requires that the 

Secretary of Transportation, in cooperation with the State highway departments, approve 

design standards for projects on the National Highway System (NHS) 
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(http://www.fhwa.dot.gov/legsregs/directives/cfr23toc.htm). On the federal level, this authority has 

been delegated to the Federal Highway Administrator.

State highway departments, working through AASHTO, develop design standards through a 

series of committees and task forces. FHWA contributes to the development of the design 

standards through membership on these working units, sponsoring and participating in research 

efforts, and many other initiatives. Following development of the design standards, FHWA uses 

a formal rulemaking process to adopt those it considers suitable for application on the NHS.

The design standards currently adopted by the FHWA can be found at 23 CFR 625 (Code of 

Federal Regulations under Title 23 USC). Additional design guides and references can be found 

in the Federal-aid Policy Guide, Non-regulatory Supplement (NS) 23 CFR 625, paragraph 16. 

The CFR and Non-regulatory Supplement point to a list of publications that contain the 

standards. Probably the most frequently used document in 23 CFR 625 is A Policy on 

Geometric Design of Highways and Streets (often called the AASHTO Green Book).

Title 23 USC 109 also requires that projects (other than highway projects on the NHS) shall be 

designed, constructed, operated, and maintained in accordance with State laws, regulations, 

directives, safety standards, design standards, and construction standards. Design standards for 

each State are available at http://www.fhwa.dot.gov/programadmin/statemanuals.cfm. At this point 

in the project with the final design complete, land acquisition is fulfilled, and the actual 

construction phase is bid and awarded. This ultimately leads to physical construction and 

maintenance of the roadway.

STATE TRANSPORTATION IMPROVEMENT PROGRAM (STIP) 

As defined by FHWA, the State Transportation Improvement Program (STIP) is a “short-term 

transportation planning document covering at least a three-year period and updated at least 

every two years. The STIP includes a priority list of projects to be carried out in each of the 

three years. Projects included in the STIP must be consistent with a long-term transportation 

plan, must conform to regional air quality implementation plans, and must be financially 

constrained (achievable within existing or reasonably anticipated funding sources).” 

The STIP itself is not a plan, per se, but a budget document that is used to schedule and fund 

projects. Projects listed in the STIP typically come from local or State approved plans. All 

projects that will receive federal funding must be included in the STIP and may include 

programs such as:

• Pavement management.

• Bridge management.

• Safety.

• Transit.

• Congestion management, etc.
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Many States and local jurisdictions publish their STIP to the internet and solicit public comment 

on the proposed next version of the STIP. The CSS concept usually has already involved all 

stakeholders in the planning process before the project is eligible for inclusion in the STIP. The 

STIP process is determined by a jurisdiction, using guidelines set by federal law, and includes 

input from all the relevant stakeholders, including the public.

HIGHWAY SAFETY IMPROVEMENT PROGRAM (HSIP)

The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses 

on results. Through HSIP, FHWA provides federal funding to States for the purpose of 

reducing traffic fatalities and serious injuries on all public roads through the implementation of 

infrastructure-related highway safety improvements. Since roadway data are crucial in 

evaluating infrastructure programs, these funds can be used for data collection. MAP-21 

Guidance developed by the FHWA Office of Safety identifies eligible uses of for HSIP funds to 

support data collection, analysis and interoperability. More information on funding eligibility can 

be found at http://www.fhwa.dot.gov/map21/guidance/guidesafetydata.cfm. Each State should 

contact their FHWA Division Office for additional information on available funding sources. 

Contact information for the FHWA Divisions Offices can be found at 

http://www.fhwa.dot.gov/about/field.cfm. 

Additional information on the HSIP is available on the FHWA Office of Safety website at 

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

STRATEGIC HIGHWAY SAFETY PLAN (SHSP)

The purpose of an SHSP is to identify the State's key safety needs and guide investment 

decisions to achieve significant reductions in highway fatalities and serious injuries on all public 

roads. The SHSP allows all highway safety programs in the State to work together in an effort 

to align and leverage its resources and positions the State and its safety partners to address the 

State's safety challenges on all public roads.

An SHSP is a statewide-coordinated safety plan that provides a comprehensive framework, and 

specific goals and objectives, for reducing highway fatalities and serious injuries on all public 

roads. This statewide document is developed by the State DOT. The SHSP is a data-driven, 

four to five year comprehensive plan that integrates the “4 Es” (i.e., engineering, education, 

enforcement, and emergency medical services). The SHSP establishes statewide goals, 

objectives, and key emphasis areas and is developed in consultation with Federal, State, local, 

and private sector safety stakeholders” ((http://safety.fhwa.dot.gov/hsip/shsp/). 

NHTSA TRAFFIC RECORDS PLANNING

The purpose of a Traffic Records Strategic Plan is to guide the State’s TRCC and its member 

organizations in fulfilling a shared vision of what the State’s traffic records system should be: its 
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organization, management, contents, and functions. The major components of a comprehensive 

traffic records system include: 

• Crash information.

• Roadway information.

• Exposure (traffic volumes).

• Driver information.

• Vehicle information.

• Citation/adjudication information.

• Injury surveillance information.

Traffic Records Strategic Plan

MAP-21 identifies complimentary data programs between FHWA, NHTSA and FMCSA as a 

requirement. One means to help achieve complementary data programs at the state level is the 

development of the state Traffic Records Strategic Plan. This is a document that provides a 

roadmap from the current traffic records system to a desired future system that best meets the 

identified needs of all stakeholders in the 

State. Strategic planning must translate the 

desired system into reality, including an 

organizational structure, operating budget, 

and resource decisions.

NHTSA’s requirements for a Strategic Plan 

relate to eligibility for funding under current 

traffic records grant programs (Section 

405c), but may be interpreted more broadly 

as a requirement to link the State’s safety 

plans to the traffic records strategic plan. 

The support for making this linkage is that all 

of these planning efforts require data and 

require some statement about the quality 

and reliability of the data used in the plan. 

Where deficiencies are noted, a State is 

expected to address those in the plan with a 

series of activities that will result in 

improvement. More information is available 

on NHTSA’s website at

http://www.nhtsa.gov/Data/Traffic+Records. 

NHTSA Requirements for a Traffic 

Records Strategic Plan

• Be comprehensive and multi-year.

• Have the State's TRCC approval.

• Be reviewed annually to ensure that project 

goals are defined and are being met.

• Address existing deficiencies, how they were 

identified, and priorities for corrective action.

• Identify performance-based measures and 

matrices for measuring progress, including 

benchmarks.

• Indicate what funds will be used and how 

they will be used to address the goals of the 

plan.

• Establish timelines and accountability for 

components of the plan.

• Integrate the State data needs and goals 

with the State’s SHSP.
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The plan is not the only place that some of this information should be found. Benchmarking 

and performance measures describing the current status of the traffic records data (its 

timeliness, accuracy, completeness, etc.) are not items created solely for insertion into the 

Strategic Plan, but are intended to be part of an ongoing quality control measurement effort –

the data are to be referenced in the plan, not created solely for the plan.

Traffic Records Coordinating Committee (TRCC)

Under the NHTSA guidelines, the State TRCC is responsible for reviewing, updating, and 

approving the Traffic Records Strategic Plan. Updates are called for annually. As part of this 

role, the TRCC is expected to have the authority to review agencies’ plans for changes to any 

component of the traffic records system. That review authority is expected to be recognized 

through formal Memoranda of Understanding (MOU).

The responsibility for developing and maintaining traffic records information that supports a 

State’s traffic safety mission is shared among many organizations and literally hundreds of 

individuals. Without a coordinated process of strategic planning, these organizations and 

individuals may take actions that may inhibit the State's ability to manage its traffic records 

systems in an efficient and effective manner. Simple decisions to add or delete specific 

information items require careful consideration and planning if the traffic records system is to 

remain viable. Strategic planning offers a means by which these decisions may be taken in a 

coordinated way.

Coordinating the management and improvement of a traffic records system is the role of the 

TRCC. This takes on many different forms among the States, but one of the most common 

assignments for the TRCC is to oversee the development and maintenance of the Traffic 

Records Strategic Plan. To accomplish this, the TRCC will have to take time to define and 

communicate its vision, relate its activities to this vision, measure its progress in achieving this 

vision, and identify changes in its behavior that must be accomplished to maintain this vision. 

This activity will prepare the organization for the future and permit it to act in advance of 

events that may affect the system rather than be driven by those events in a direction that the 

TRCC and its members do not wish to proceed.

By virtue of this coordination, the strategic planning effort focuses the TRCC on promoting a 

system-wide approach to traffic records and its role in helping its member agencies move 

forward. The TRCC cannot and should not act alone. To be truly effective, the TRCC must 

work with its member agencies to achieve mutually agreed upon goals. Defining the active and 

facilitative roles of the TRCC is one of the primary uses of a good Strategic Plan.

Traffic Records Assessments

Traffic records may be viewed as a product that is supplied by the agencies. It is important to 

look at the quality of that product, list the customers, their needs, and their systems’ strengths 
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and weaknesses. It is also important to be aware of opportunities for improvement and threats 

to the product’s quality or the system’s ability to supply that product.

A traffic records assessment takes stock of the status of all of the related traffic records system 

components, the environment in which the system operates, and considers the current service 

provided. An agency must conduct an assessment to identify system strengths, weakness, and 

areas of potential improvements. Once an assessment is completed, the detailed review 

provides awareness to all of the stakeholders that have a vested interest in the system and 

helps bring a practical focus on identifying the various goals to be pursued by projects identified 

in the Strategic Plan.

A vision has evolved from these and other efforts of a TRS that is not just a hypothetical 

construct—easily described but never actually existing—to become a reality of integrated data, 

easily accessible, and capable of supporting analyses that foster the coordinated approaches to 

traffic safety we know work best. Realization of this vision takes place in the context of 

multiple custodians over multiple systems, most of which have a primary purpose other than 

traffic safety. Bringing together data from systems designed to produce revenue, track assets 

and expenses, monitor insurers and hospitals or care utilization, and a host of other systems 

and purposes requires both the will to share data and the technology to make data sharing 

affordable. There are State and federal regulations securing data privacy that must be 

addressed. Moreover, it is common to find systems of vastly different vintages and capabilities 

that must be modified or worked around in order to develop the desired data access and 

sharing processes. This vision of shared data relies on the data management practices that are 

part of each TRS component system, and imposes yet another layer of data management 

practices required for the integrated datasets that are created because of the data sharing 

process itself. 
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CHAPTER 4—DATA MANAGEMENT

Data managers and users must make the most of the resources available to them, and where 

data needs are identified, the first task should be to look for already existing electronic sources 

of the necessary information so that it may be obtained as economically and efficiently as 

possible. 

DATA INTEGRATION

The needs of users and the constraints of tight 

budgets are best satisfied by integrating data from 

available sources and making the resulting 

combined data available to as many users as 

possible. As noted in the white paper developed 

from the Traffic Safety Information Systems 

International Scan, see 

http://www.tfhrc.gov/safety/pubs/06099/06099.pdf:

“Excellent crash, roadway inventory, and traffic 

data are critical to making decisions 

concerning roadway planning, roadway design 

and improvement, vehicle design, and driver 

programs—all of which affect the safety of the 

driving public. Safety data will become even 

more critical ... to more fact-based safety 

decisions and to performance-based 

programs.”

The Data Integration Primer from Federal 

Highway Administration’s Office of Asset 

Management is an excellent introduction from a 

roadway data management perspective, see 

http://isddc.dot.gov/OLPFiles/FHWA/010393.pdf. The 

benefits of integrating data from disparate agency 

systems may not seem obvious. Some familiar 

examples of combined datasets used in roadway 

analyses may help make the point that these 

efforts are worthwhile. Figure 9 shows an 

example of the data integration process. 

Results of analysis from integrated systems can 

provide answers to questions such as what is the 

impact on the crash rate by upgrading the intersection design to a roundabout? Which rumble strip 

Figure 9. Data integration process.
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design is the most beneficial when compared to similar roadways with equal amounts of average daily 

traffic? Questions such as these require data from multiple shared data sources. In these 

examples are the familiar combination of roadway inventory, crash, and traffic volume databases 

used to support analyses that could not be possible without integrated data. 

DISTRIBUTED DATA SYSTEMS

Traditionally, States still often concentrate roadway data collection and maintenance efforts 

based on whether roadways are state-maintained or non-state-maintained “local” roads, often 

giving the highest priority to State-maintained roads. The caveat being for HPMS data, which 

creates a three-tiered system of roadways within a State on which data are collected: Federalaid roads, non-Federal Aid State-maintained roads, and local roads. However, some States often 

maintain primary and roadway traffic data inventories independent of their HPMS data 

submittal. Managing these various datasets can present a challenge. 

A potential form of distributed data management of roadway information is a single statewide 

system covering only those roadways that are managed by the State DOT and a separate 

system, unconnected to the first, designed to store data on local roadways. In many States, this 

latter (local) system has been developed in an ad hoc fashion and it is built from multiple local 

roadway data sources. The so-called “local file” is usually a much less complete dataset 

incorporating a smaller number of data elements, and often missing data even in the data 

elements that are included in the system. However, this is not to say that such a two-tiered 

approach cannot work. It is certainly possible and perhaps even desirable from a cost 

effectiveness standpoint to develop a distributed system that shares the data collection and 

management burden among multiple agencies; State, local, Metropolitan Planning Organization 

(MPO)/Regional Planning Commission (RPC), etc. 

From an IT viewpoint, a modern distributed model would consist of source data that are 

collected and managed locally by those who have the greatest stake in its overall quality. These 

varying sources of data are linked to a master statewide system that can poll all of the local 

systems and obtain updated information on an as-needed/as-available basis. For such a 

distributed system to work well, it is imperative that all participating agencies adhere to a set of 

agreed-upon standards for data quality, especially consistency across jurisdictions.

CENTRALIZED SYSTEMS

In an attempt to create a complete database of all public roadway locations, many States have 

opted to develop centralized systems that store records on State and local roads in a single 

resource. These systems have the obvious advantage over distributed systems of having data 

quality management reside in a single agency. The realization of such centralized systems has, in 

many States, fallen short of the ideal because, ultimately, data quality relies heavily on a variety 

of data providers who (just as in the distributed data systems) may not all adhere to established 

data quality standards. Only when a State’s DOT controls all the data collection and data 
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management can the processes for roadway data collection and management be said to be truly 

centralized. Such systems do exist, but their costs are born entirely by the DOT and still there 

are often important differences between the quality of information obtained for statemaintained and locally-maintained roads.

The FHWA Office of Highway Policy Information and Office of Planning, Environment, and 

Realty issued the Memorandum on Geospatial Network for All Public Roads on August 7, 2012. 

This Memorandum identified a Highway Performance Monitoring System (HPMS) requirement 

for States to update their Linear Referencing System to include all public roadways within the

State by June 15, 2014, in accordance with the HPMS information collection approval from the 

Office of Management and Budget (2125-0028). This Linear Referencing System will provide a 

means to geolocate all safety data on a common highway basemap that includes all public roads.

ENTERPRISE-WIDE DATA SYSTEMS

Some State DOTs have developed a single, centralized data application that incorporates all (or 

almost all) of the traditional data sources found in a transportation agency. Agencies design 

such “enterprise” databases to provide a common platform, structure, and tools for all of the 

department’s data resources. This type of system can reduce costs and certainly improves the 

integration of data among the various sources that make up the components of the enterprisewide system. Such systems are expensive, but they may represent a cheaper solution than one 

in which the same department must separately maintain applications software and databases for 

each of the functions separately. They also help to reduce duplication in data storage and often 

in data collection. Agencies realize the most benefits when they design a system to meet the 

needs of multiple users and build the system using strict data governance processes. 

Figure 10 shows the information flow for an enterprise-wide roadway data system in a State 

DOT. This example illustrates an enterprise view of the Illinois DOT based on their roadway 

data systems.
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Figure 10. Illinois DOT information flow for asset management.

Even with an enterprise-wide roadway system, many states have created the opportunity for 

data owners to enter and maintain their own data. The challenge is to keep all of the data in 

sync over time so temporal analyses can occur and the associated location remains anchored 

on the correct highway and reference point. For example, in the North Carolina DOT, the 

Planning Division enters basic inventory data from the original plans, plan changes, and as-built 

plans and the maintenance operations division updates records as any data is added or updated. 

In other cases, a DOT’s Structures Division may provide all of the data entered into the system 

about bridges and culverts. 

Data Governance

Data governance is a term used with increasing frequency in the government IT area, and 

especially within State DOTs. The term can mean multiple things; however, the common 

theme of data governance efforts is to impose common standards for all data elements and data 

collection efforts throughout a broad community (such as an entire DOT or the DOT plus all 

local partners). The goals of data governance efforts are to improve coordination among 

multiple systems by ensuring that data definitions and data collection methodologies are 

standardized, and that multiple systems using the same (or similar) data are sharing the data to 

the greatest extent possible. Data governance efforts seek to reduce redundant data collection 

and simplify the processes of data integration while, at the same time, helping to ensure 

accuracy and completeness.
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A formal data governance process typically includes a set of data governance policies and a data 

governance “board” made up of executive-level and practitioner-level staff from all stakeholder 

offices and agencies. An example for a DOT would be a data governance board including the 

Director of IT, the stewards/custodians of each of the agency’s major systems, and the IT 

managers or staff assigned to support those systems. At a statewide level, data governance 

boards might include the heads of each State agency, the State Chief Information Officer, and a 

set of representative data stewards and agency IT staff responsible for the major systems. The 

functions of the board include setting standards, reviewing system designs, assessing data 

definitions and requested changes to those definitions, and determining the sequencing and 

resource allocations for data-related projects.

Because of the historic methods used by most State agencies (DOTs included) for developing 

data resources, data governance practices and policies often are seen as a barrier to system 

improvement rather than the aid that they should be. This is unfortunate since a well-run data 

governance process can save the agencies and the State a great deal of time and money and 

make much better use of scarce IT resources. Submitting agency plans to a data governance 

board; however, may involve losing a measure of internal control over project scheduling, 

scope, and funding. 

The decision of whether or not to establish a data governance process/policy and whether or 

not to submit all of an agency’s planned system development efforts to the data governance 

process is one that is made at the State and individual agency level. System designs and changes 

that are developed and implemented in a coordinated manner have a better chance of meeting 

the widest variety of users’ needs at the least possible cost. It is possible for States to develop 

this kind of coordination in a less formalized manner, perhaps through the work of the TRCC 

and/or through the establishment and maintenance of various strategic plans (e.g., the Strategic 

Plan for Traffic Records Improvement; the Strategic Highway Safety Plan data quality emphasis area; 

etc.). The formal data governance practices discussed here have advantages over the other 

methods mentioned in that they are specifically designed to include the IT perspective and they 

are capable of going beyond the traditional set of systems included in the definition of a traffic 

records system.

The remainder of this RDIP document will focus more exclusively on the Roadway Data 

Component of a TRS. For the purposes of this report, the term “roadway data” incorporates a 

broad array of data sources and information systems. As with the overall TRS, the roadway 

data component systems each exist to fulfill a specific purpose. Often that primary purpose has 

only a tangential relationship to traffic safety. Fortunately, as with the TRS overall, advances in 

database architecture and analytic software are making the job of data extraction and 

integration much easier. Modern databases are more flexible than the legacy systems they are 

rapidly replacing. Throughout this discussion, it is important to remember that most of the 

analytic needs for roadway data require spatial information. It is not enough to know the 

spatial coordinates of a crash or of a particular set of roadway features; the information must 
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also be aggregated into relationships that define particular roadways, routes, and networks of 

roadways.
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CHAPTER 5—ESTABLISHING THE ROADWAY NETWORK 

At the most basic level, knowing where the roads are in given a jurisdiction is fundamental to 

the daily operations of many DOT functions. Location reference methods provide “a way to 

identify a specific location with respect to a known point” and includes three elements:

1. Identification of a known point.

2. Measurement from the known point.

3. Direction of measurement.

The two basic location reference methods described in those studies are still in use today:

• Sign-oriented methods (milepost, reference post).

• Document-oriented methods (calculated milepoint, route log, and straight-line 

diagrams).

Linear Referencing System

The location referencing methods (LRM) described above are used by State and local 

municipalities to develop a Linear Referencing System (LRS) that consists of the LRMs and the 

procedures to store, manage, and retrieve information about the roadway location data. 

Historically, State and local linear reference methods were designed and maintained to meet 

specific, individual business needs. In many cases, several LRMs are used in a single agency, and 

variations of a LRS may exist within the same agency. For effective safety analyses on all public 

roads, roadway data inventories must be able to able to be logically linked with other traffic 

records systems across all State and local agencies; with this linkage most likely occurring based 

on location. 

Resolution, scale, and precision used in various LRMs may introduce accuracy problems; for 

example, roadway location data in one LRM may use milepoint data in tenths, while another 

uses milepoint data in hundredths, and a third uses State plane coordinates, and a fourth uses 

latitude and longitude data from GPS. Assuming that the data in each of the LRMs are correct 

to the desired degree, transforming each of these methods into a single LRS, or multi-level LRS 

introduces a wide variance in the resulting accuracy of the location information. If errors exist 

in the individual LRMs, these are compounded in the results. However, with advancements in 

technology, this is less of an issue. Techniques have been developed to process and minimize 

the differences in the data, but confidence with the resulting data and visual displays still vary 

from LRS to LRS.

When agencies integrate the LRS with the GIS, additional accuracy variance is introduced, 

depending on the source and precision of the GIS base mapping. Agencies may digitize GIS 

mapping from various sources or collected using commercially available maps. Ideally, agencies 

construct precision GIS base mapping using enhanced GPS coordinate data and roadway 
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characteristic data that are collected using GPS tools in the field with a comparable degree of 

accuracy.

These basic methods have several variations as noted below:

• Route-Milepost.

• Route-Reference.

• Link-Node.

• Route-Street Reference.

These four methods are described in more detail in the flowing sections. This list is not allinclusive, as variations of these methods are used at times in each agency and jurisdiction.

ROUTE-MILEPOST

The route-milepost is the most commonly used method. A measurement generally begins at the 

beginning of a route or a specific known point such as a jurisdictional boundary (e.g., county 

border) and continues to the location of interest (e.g., end of guardrail, crash site, sign location, 

etc.). This is a location method used in the field with mileposts or other physical reference 

markers positioned accordingly. On major routes, the reference markers or posts are placed 

where they can be seen for use by maintenance crews, law enforcement, etc. On other routes,

the reference marker may be placed in less conspicuous locations, such as on the backs of 

traffic signs. 

These reference markers may get out of sync because the road is changed and the marker 

placement is not corrected. If they are correctly placed, jurisdictions should be sure to correct 

their inventories regarding the repositioned markers. This practice may result in degradation of 

accuracy when encoding or using data that describes the location. When relying on mileposts 

or other reference markers along the roadway, measurements are generally collected 

anywhere from tenth of a mile to thousandths of a mile increments, depending on the type of 

data that are being collected. Figure 11 illustrates a guardrail measurement taken from the 

beginning of a route. The guardrail begins at 4/10th of a mile past milepost one 1 and terminates 

at 3/10th of a mile past milepost 3.

Figure 11. Example of a roadway feature location measured by route and milepost.
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ROUTE-REFERENCE

The route-reference or control/section-milepoint method is a document-oriented location method 

with calculated distances from a known location. It relies on known physical landmarks (e.g., 

markers, bridge, an intersection, water tower, fire hydrant, or a marker such as a numbered 

pole embedded in the ground). Initial measurements of roadway features (i.e., reference or 

milepoint) come from the as-built or the more recent construction of design plans available and 

generally are expressed in hundredths of a mile or thousandths of a mile. Events such as 

crashes are located using a distance from these markers that is converted to the documented 

calculations of locations to determine the degree of accuracy. Figure 12 shows a guardrail that 

begins at 400 thousandths (400/1000) of a mile from the documented marker and terminates at 

2.320 thousandths (2 and 320/1000) of a mile from that known point.

Figure 12. Example of a roadway feature location measured by route-reference.

LINK-NODE

Some State and local municipalities use the link-node method. This is the most generic 

document-oriented method of location coding and can be used to mimic other documentoriented methods rather easily. For example, using the link-node method, Figure 13 shows a 

crash occurring on link 102103, 104 feet from node 102. Known locations, typically an 

intersection, are designated as a unique node with an identifier, and the connector to the next 

node is a unique link. Links may be of unequal lengths. Roadway features are located by 

measuring the distance from the nearest node or nodes. 
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Figure 13. Example of link-node method of identifying locations.

The schema for designating node and link names can be complicated when changes are made to 

the existing system. This is particularly true for a legacy system that does not utilize a relational 

database. These legacy systems may not be flexible enough to add additional nodes and 

segment descriptions without losing historical data and essentially entering all of the data again 

that has accumulated over the years for that roadway segment. 

ROUTE-STREET REFERENCE

A similar method to link–node is route–street, typically used in urban settings. Rather than 

assigning node and link designations, the street names or unique street codes are used and 

distances then are measured from intersections or other fixed landmarks. Examples of 

complications when using this method include:

• Street names change over time or there are alias names for the street that are used 

interchangeably by data collectors.

• Streets intersect more than once with another street.

• Technicians or law enforcement officers fail to differentiate streets of similar names 

(e.g., Stanford Drive and Stanford Court, or West Prospect Road and Prospect Road).

• Block numbers can be used to segment a street and indicate where an event occurred, 

rather than measured distances from a known point. 
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GEOGRAPHIC COORDINATES

A different method used by both State and local municipalities is Cartesian coordinates along an 

x and y plane, often by means of the State plane coordinate system (SPCS, established in the 

1930s for each State). Typically, a cartographic map is produced with the State plane 

coordinates fixed on the map, showing roads and other known fixed locations and geographic 

features. Locations of roadway characteristics are digitized manually to collect the coordinates 

of each feature.

Global Positioning System (GPS)

More recently, some agencies use GPS to automate the collection of road segment coordinates 

or individual point coordinates for roadway characteristics and events. These coordinates 

represent a ‘point in space’ or in this context, a spatial location relative to a specific point on 

the Earth’s surface. A number of jurisdictions have noted that collection of LRM information in 

addition to the coordinates has helped to maintain the quality of location data.

Some agencies may have had difficulty integrating the legacy LRM data with new GPS 

coordinates due to variations in required scale for individual data systems and errors in original 

data. In some cases, though rarely documented, maintenance personnel have physically 

relocated mileposts without notifying other key stakeholders, resulting in a degradation of 

location data quality. Several States have developed complex algorithms or other data matching 

and integration tools (called conflation techniques) to merge the various LRMs while attempting 

to reduce the extent of error that may result in specific datasets.

Additional methods for denoting locations that usually are not used in a LRM include the 

following:

Roadway Stationing

Locations on a roadway may include the station information from the construction plans. For 

most State and local agencies, the station is a survey location denoting a distance and direction 

from a known point. Usually agencies convert this information to calculate milepost locations 

expressed in thousandths of a mile. Even with this degree of accuracy, location error can 

potentially be expected when comparing this information with GPS coordinate data for the 

same physical location.

Roadway Geo-coding / Address Interpolation

Agencies obtain geo-coding by capturing a street or road address from the field, entering that 

data into interpolation software or a web-based site that returns latitude and longitude 

coordinates. Accuracy may vary considerably using this method. However, Ohio was able to 

develop a highly accurate unified map data. Ohio’s Location Based Response System (LBRS) is a 

model for other States considering a system. A White Paper on the LBRS is available at: 

http://gis3.oit.ohio.gov/%5Clbrs/_downloads/docs/White%20Paper-LBRS_2011.pdf. 
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Roadway Realignment

Location coding methods indicate how a spatial location is identified for purposes of associating 

attributes to a specific location. When a section of roadway is no longer located in the original 

space, adjustments must be made to the location scheme to indicate the new location of the 

roadway and, in some cases, to historical data that references that location.

Where agencies realign a roadway to shorten the road (e.g., when a curved section is 

straightened), the new roadway will have a gap in the mileposts (sometimes referred to as a gap 

equation). Figure 14 shows the resulting mileposts ahead (AH, which usually denotes the 

original mileposts) and back (BK, referring to new mileposts):

Figure 14. Example of a realignment that shortened a roadway.

With the new shortened alignment, the mileposts from 11.42 to 12.00 no longer exist. The 

crucial item is the temporal or time element that must be preserved to denote when the change 

to the roadway occurred and which segment is relevant to the features involved. This also 

applies to the scenario illustrated in Figure 15 that shows a circumstance where the road 

realignment is longer than the original, resulting in duplicate mileposts on the new roadway. 

The designation for ahead or back is critical, and must be added for the entire length where 

duplicate milepoints exist.

Figure 15. Example of a realignment that lengthened a roadway.
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With the variety of LRMs, the next logical step is creating a linear referencing system that 

manages the various LRMs into a cohesive product that can be used in conjunction with GIS 

software.

Many transportation network specialists use a LRS approach where attributes are related to 

roadway segments in terms of linear measurements from defined locations (e.g., intersection, 

etc.). This technique reduces the need to re-segment the network geometry when new 

attributes are added or changes are made. Figure 16 graphically depicts a segmented road 

model. Nodes define the limits of each segment; i.e., where characteristics of the roadway 

change. Note in Figure 16 that the beginning and endpoints of the bridge are delineated with 

nodes.

Figure 16. Example of a segmented road model.

Figure 17 depicts a roadway segment using LRS. In this example, the bridge is represented as a 

linear event that occurs along the roadway, rather than needing new nodes and a roadway 

segment to be defined. A more detailed explanation of this topic and graphical examples of 

road segmentation models are available in the American National Standards Institute (ANSI)

Standard for IT. For information that is more detailed see Geographical Information Framework 

Data Content Standard, Part 7: Transportation Base, (http://www.fgdc.gov/standards/projects/incits-l1-

standards-projects/framework/documents-1/Part_7%20-%20Transportation-Base%20-%2020060112-

1846.pdf).

Figure 17. Example of a linear referencing system (LRS).
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GEOGRAPHIC INFORMATION SYSTEM (GIS)

The roadway data component of a traffic records system must provide a framework that 

defines the roadway network and provides a method for describing locations and 

characteristics of points and segments of the roadway system. With the widespread use of GIS 

software, the ability to define a roadway network and provide a means to link to locations on 

that network has become more comprehensive and accurate than ever before. Using a GIS 

graphical representation to cross-reference various location-coding methods and define 

roadway attributes makes even legacy data easier to report, analyze, and understand. Figure 18

shows an example of geographical analytical tools. 

For over 30 years, State highway departments have worked with GIS software to define their 

roadway networks in sufficient detail to use for engineering activities. A GIS joins a computer 

software mapping application with a database of geographically enabled information that links 

spatial information (i.e., where things are) with demographic and environmental information 

(i.e., what things are like). Basic data elements and terminology for defining the roadway 

network include the following:

Shapefile

A shapefile is one format of a digital geo-referenced file that supports point, line, and area 

features to enable a “...user to apply various geographical analytical tools, such as 

Figure 18. Example of geographical analytical tools.
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interpolation, distance measurements, or data visualization.” At a minimum, a shapefile will 

include a coordinate pair, an index number, and an attribute.

Coordinates

Coordinates are used to determine the relative position of points within a survey area or, in 

many cases, with respect to a much larger area. In order to make the coordinate system 

usable for engineering projects, the horizontal relationships should be defined as two 

dimensional on one (mapping) plane. The north-south axis is designated as the Y-axis and 

the east-west axis as the X-axis. The horizontal distance from the Y-axis is the "easting" 

coordinate and the vertical distance from the X-axis is the "northing" coordinate. A 

statement of the exact position of a point within the system can be expressed as X-Y, or 

easting-northing value of the point. Stated either way, the point location is being described 

by rectangular coordinates and the location is a grid location. For spatial analysis (using GIS 

tools), a Z-coordinate can be used to add a vertical third dimension to the analysis to 

express elevation. 

Cartesian Coordinates System

Cartesian coordinates represent a two-dimensional planar coordinate system in which x 

measures horizontal distance and y measures vertical distance. 

Coordinate Pair

A coordinate pair is a set of Cartesian coordinates (north-south and east-west distance, or 

latitude and longitude) that describe the two-dimensional location in terms of the earth’s 

surface, from a known reference point.

Latitude

Latitude measures angles in a north-south direction and defines the y-coordinate of a 

Cartesian coordinate pair.

Longitude

Longitude measures angles in the east-west direction and defines the x-coordinate of a 

Cartesian coordinate pair.

Map Scale

A statement of a measure on a map and the equivalent measure on the earth's surface, 

often expressed as a representative fraction of distance; for example, 1:24,000 (one unit of 

distance on the map represents 24,000 of the same units of distance on the Earth). Map 

scale also can be expressed using different units: for example, 1 inch = 1 mile or 1 inch = 

2,000 feet.
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Attributes

Attributes are the characteristics of a geographic feature described by numbers, characters, 

images and drawings, typically stored in tabular format and linked to the feature by a userassigned identifier or index.

There are numerous methods used to reference a roadway segment or section. An example 

provided by Illinois DOT includes:

• Annual program number.

• Multi-year program number.

• Design project number.

• Construction contract number.

• Federal job number.

• Local agency reference.

• Structure number.

• Rail crossing inventory number.

To integrate the various methods of locating data for safety program development, Illinois DOT 

uses the following methods for their enterprise spatial data integration:

• Crash data (safety marked route).

• High accident locations (paper maps).

• Program (program number).

• Ongoing construction (contract number).

• Roadway inventory (key route).

• Rehabilitation history (marked route).

• Effectiveness of safety countermeasures.

• Relationship to total program.

Locating data about a roadway segment may vary from state to state and among jurisdictions, 

but one or more method for identifying a roadway location is needed to develop safety 

programs and conduct safety analyses. 

Figure 19 shows a GIS map example of Iowa’s use of multiple referencing systems. Iowa DOT 

has the ability to cross-reference data easily to locate and analyze the effects of safety-related 

improvements. Iowa’s mapping includes the capability to be used by cities for utilities and 

engineering, by counties for the tax assessor and engineering, by other state agencies such as 

Emergency Management, and by the Federal government for FHWA and non-transportation 

agencies such as the Census Bureau. 
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Figure 19. Iowa DOT example of using multiple location methods.

With the structure of modern databases designed to define roadway networks comes the 

ability to manage increasingly large amounts of data. Databases to support detailed analysis of 

roadway features and attributes are easily designed. Implementing and maintaining such 

systems is the more difficult task because it requires data. The next chapter presents common 

and emerging data collection tools and methods useful in populating the databases we use to 

construct the roadway network and support safety analysis.

Several NCHRP Synthesis reports on highway location reference methods have been published 

by AASHTO over the years. An example includes NCHRP Synthesis Report 460, Guidelines for 

the Implementation of Multimodal Transportation Location Referencing Systems

(http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_460.pdf). This report presents and describes 

the transportation multimodal, multidimensional location referencing system data model 

developed through NCHRP Project 20-27. A more recent report, that builds on this earlier 

work, is the NCHRP 20-07 Task 302 Report, Multi-level Linear Referencing System (MLLRS) 

Cost/Benefit Value Analysis Study (http://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP20-

07(302)_FR.pdf). 
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CHAPTER 6—DATA COLLECTION TOOLS AND METHODS

Application of technology has been suggested as a way to overcome many of the limitations 

surrounding current safety data. As noted in the NCHRP 367 report Technologies for Improving 

Safety Data (http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_367.pdf) incorporating 

technology will assist DOTs in meeting the current and future needs of managing safety on our 

road systems, with the following points identified:

• Technology alone cannot solve all of the problems associated with safety data systems, 

especially those related to inadequate institutional cooperation. Organizational issues 

should be addressed before considering technological advancements.

• Technology implementation and maintenance can be capital-intensive, requiring 

significant funding and programmatic support; therefore, the benefits and costs clearly 

must be evident across all affected agencies.

• Technologies are constantly evolving; therefore, agencies should seek to employ 

technologies that allow for flexibility. Additionally, a practical plan for maintaining and 

upgrading the technologies, as well as assessing their continued effectiveness, should be 

developed before initial investment and implementation of the technology.

• No single technology will allow for the collection and maintenance of all the varied data 

required for safety analysis — hence, an array of technologies should be considered.

This overview of technology is an introduction to the possibilities of collecting accurate, 

comprehensive, timely, and complete roadway inventory data that can be integrated into 

current GIS and is needed for the use of safety analysis tools such as IHSDM, Safety Analyst, and 

the HSM.

ROADWAY PHOTOLOG

The typical method of collecting roadway data until the 1970s was through ‘windshield surveys’ 

and manual data collection. These efforts are very time intensive and the resulting information 

is generally poorly distributed, incomplete, and lacking continuity. In the late 1970s, some 

DOTs adapted the concept of ‘photo logging’ that usually consisted of a 35 mm photo frame, 

taken every 52.8 feet (1/100th of a mile or 100 frames per mile), from a vehicle traveling on 

State roadways. As each photo was taken, usually a date stamp, highway identifier, and location 

by milepost were included in the frame. Photolog offers a photo inventory of the highways that 

can be referenced without an additional field trip to locate or verify an inventory item at a 

specific location.

Agencies used the resulting film images to determine general surface conditions and locations of 

signs, signals, driveways, and other roadside items or hazards. The storage and indexing of the 

film reels was cumbersome and time consuming, and usually limited to one agency office. The 
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difficulties in storing and accessing 35 mm film images led to the use of video home system 

(VHS) technology and other analog storage such as laser optical platters providing for improved 

storage and reproduction. Now digital photography has replaced VHS, with the advantages of 

digital storage for faster and easier retrieval by users, typically over an agency’s intranet. 

Several States make their imagery available to the public via the internet. States are now 

collecting digital images in high definition (HD) video, higher resolution imagery for improved 

clarity.

The digital photolog or Videolog systems can include additional cameras (as many as 18) and 

images that allow users a much wider view of the roadway and images of the road surface for 

pavement evaluation, etc. Agencies update the roadway photolog images typically every two to 

three years, with archival images retained for two to three cycles since storage is less expensive 

and easier to access in digital format. While some States obtain Videologs using internal staff, 

they also use commercial Videolog services. 

AUTOMATING THE ROADWAY INVENTORY

In the early 1980s, some of the early photolog systems included compass gyros, adapted from 

maritime and aviation use, along with other sensors to collect roadway data elements. As 

technology evolved, this equipment has become vastly more sophisticated and includes such 

devices as differential GPS, accelerometers, laser measurement, inertia detection, and other 

sensors to collect data such as:

• Sign type, location, and distance to the edge of the road.

• Sign nighttime reflectivity.

• Roadside barriers and other roadside hardware.

• Pavement roughness, rutting, texture, faulting or cracking, reflectivity.

• Road geometry (heading, grade, cross-slope).

• Lane width, shoulder width.

• Location via GPS coordinates.

The consistency and timeliness of information collected on roadway and pavement condition in 

conjunction with accurate GPS data are key to an effective pavement management system. 

Agencies can also use data collected to determine horizontal (roadway curvature) and vertical 

alignment (hills and terrain) information to be used to generate roadway profiles for safety 

analysis. To date, automated collection of curve and elevation data has not achieved acceptable 

levels of accuracy or consistency.

Some DOTs purchase roadway Videolog vehicles with the roadway inventory equipment, while 

others contract for these services from commercial vendors. Although the fully equipped 
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vehicle with a two-person crew is expensive to operate, the cost of obtaining and maintaining 

the resulting data is far less expensive than comparable manual pen and paper data collection.

Figure 20 illustrates a typical vehicle equipped with video logging and additional sensors for 

automated roadway inventory data collection. The van is fully equipped with cameras, 

computer equipment, and lasers to capture pavement conditions and road geometry.

Figure 20. A typical van used to collect the video images. 

(http://www.fhwa.dot.gov/publications/research/safety/05073/chapt2.cfm) 

LIGHT DETECTION AND RANGING (LIDAR)

LiDAR-equipped vans are capable of obtaining highly detailed digital images of roadway features 

and attributes. Agencies use the data for asset management, safety analysis and other purposes. 

Depending on how they are configured, such data collection systems can automatically collect 

x,y,z spatial coordinates along with images of the roadway surface, pavement width, shoulder 

width and type, median width and type, signs, pole locations and types, roadside hardware 

locations and types, and other information. The high-resolution images support automated and 

manual measurement of features. Surveys can be ground-based by adding LiDAR 

instrumentation to vans as that shown in Figure 20, as in Figure 21 at low altitude. Figure 21 

shows downward-looking LiDAR lasers that have been mounted temporarily to the underside 

of a helicopter. 
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Figure 21. Aerial low-altitude LiDAR.

(http://www.fhwa.dot.gov/publications/publicroads/01septoct/lidar.cfm).

GLOBAL POSITIONING SYSTEM (GPS)

A GPS is a satellite-based radio-beacon navigation 

system developed, owned, and operated by the 

U.S. Government. GPS uses a constellation of 24 

satellites that transmit time signals continuously. 

Users equipped with the appropriate receivers 

can receive signals from the satellites to calculate 

their position (latitude, longitude, and elevation), 

time, and velocity. Originally, GPS accuracy for 

civilian use registered only within approximately 

100 meters. This intentional degradation of the 

accuracy was a security measure instituted by the 

U.S. government for any non-military use of the 

system. This degradation was removed as of May 

1, 2000, bringing accuracy of the basic GPS data to approximately 15 meters. Though GPS use 

is continuing to flourish, the accuracy of the basic civilian-accessible implementation of GPS 

often is insufficient for applications needed for transportation requirements.

To achieve sufficient accuracy, agencies can use the augmentation technique commonly known 

as the Differential Global Positioning System (DGPS). The DGPS technique is based on a highly 

accurate geodetically surveyed location of a GPS reference station. The reference station 

observes GPS signals in real time and compares their ranging information to the ranges 

expected to be observed at its fixed location. The differences between observed ranges and 

predicted ranges are used to compute differential corrections, which then are provided to GPS 

users. DGPS accuracy is approximately 1 m with some newer technology approaching 10 cm 

accuracy.

Example of a PDA/Phone with GPS. 
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Each transportation department’s use of DGPS is based on its own particular needs; however, 

there is a common thread among transportation applications. This technology is used to 

improve public and personnel safety as well as efficiency. 

There are wide ranges of transportation applications using DGPS, including:

• Geographic databases for use in emergency 911 systems.

• Highway inventory (i.e., signs, milepost markers, right of way, guardrail, and bridges).

• Emergency response services (e.g., police, fire, and rescue).

• Automatic vehicle location for public transit and other fleets.

• Snowplow guidance for low-visibility situations.

• Inventory of highway-railroad crossings and road centerline.

• Tracking hazardous materials from origin to destination.

• Mapping pavement condition, safety, accident, and traffic data.

The integration of DGPS with GIS is one of the most widely used applications. GIS allows the 

association of statistics of any kind with a specific geographic location and the displaying of the 

data on an interactive map. The GIS provides a “base map” on which to display geocoded 

information and the DGPS provides an accurate physical location for each data point. For a list 

of States that are using DGPS, see the FHWA summary report An Investigation of the Use of 

Differential Global Positioning System (GPS) Technology and its Augmentations within State and Local 

Transportation Departments, see 

http://www.fhwa.dot.gov/publications/research/operations/its/00093.pdf. Agencies within many States 

are considering or are already planning to incorporate GPS and DGPS technologies into a 

variety of applications.

AERIAL PHOTOGRAPHY AND SATELLITE IMAGERY

The term “remote sensing” was first used in 1960, but aerial photography was first used 100 

years earlier with cameras hung from balloons, kites, and pigeons, for a variety of tasks relating 

to planning and land use. Remote sensing is:

“...the measurement or acquisition of information of an object by a recording device that is 

not in physical or intimate contact with the object. In practice, remote sensing is the use at 

a distance (as from aircraft, spacecraft, satellite, or ship) of any device for gathering 

information about the environment. Satellite and aerial photography images are the two 

most commonly used forms of remote sensing in transportation.”
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The value of remote sensing from aerial 

photography and satellite is evident 

from the availability and usage of 

internet mapping websites that allow 

map and photographic views of the 

Earth. These maps allow for simple 

identification of specific addresses,

businesses, type of business, and 

directions (including mileage and travel 

time) from one specific location to 

another. Some of the commercial 

mapping sites illustrate the significance 

of mapping and imagery combined in the 

‘hybrid’ views, and the coordination of 

aerial views with ground images via 

DGPS correlated Videologs.

Within GIS, agencies can analyze digital satellite images generated through remote sensing to 

produce a map-like layer of digital information about a transportation network and the 

surrounding land use. The quality of remote sensing data depends on its spatial 

resolution. Spatial resolution refers to the size of a pixel that is recorded in a raster image (e.g. 

photograph image). Typically, pixels may correspond to square areas. Most digital ortho photos 

are based on 3 inch, 6 inch, or 12 inch pixels. This precision has been improved by decreasing 

data collection cost due to the widespread use of digital cameras. Remote-sensed data can be 

used in a similar manner to the Videolog to identify elements of the transportation 

network. With advancements in technology, Videolog and satellite imagery are increasingly 

being geo-referenced; therefore, elements are inherently being tied to spatial coordinates and 

are readily placed on a map or integrated into a GIS. 

TRAFFIC COUNTERS

Collecting traffic information is vital to the daily operations of many groups within a DOT, and 

safety is no exception. Traffic volumes can be used for many purposes in safety analysis. For 

example, traffic volumes allow agencies to determine expected crash rates for a road segment 

or intersection based on similar operational characteristics. Agencies can use this information 

to rank roadways and intersections by priority for receiving safety funding. 

Traffic counters are classified into two basic groups: (1) intrusive and (2) nonintrusive. Intrusive detectors are the most common and are embedded into the road surface 

or lay exposed on the surface. These include the loop detector, the pneumatic road tubes, 

bending plates, and piezoelectric sensors. These devices usually last three or more years, 

require detouring traffic to install the device, and involve compromising the road surface for 

Aerial photograph showing a four-way intersection 

(http://www.ops.fhwa.dot.gov) 
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installation (except for temporary pneumatic tube counters). These devices also are not always 

accurate, as they usually do not accurately count motorcycles and bicycles. Current methods 

for automatically collecting pedestrian volumes can be expensive and inaccurate.

Non-intrusive detectors usually do not include road surface installation. Examples include: 

Passive Infrared

Passive infrared devices detect the presence of vehicles by comparing the infrared energy 

naturally emanating from the road surface with the change in energy caused by the presence 

of a vehicle. Figure 22 provides an example of passive infrared devices. Since the roadway 

may generate either more or less radiation than a vehicle depending on the season, the 

contrast in heat energy is what is detected.

Active Infrared

Active infrared devices detect the presence of vehicles by emitting a low-energy laser 

beam(s) at the road surface and measuring the time for the reflected signal to return to the 

device. The presence of a vehicle is measured by the corresponding reduction in time for 

the signal return.

Figure 22. Examples of passive infrared devices.
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Magnetic -- Passive and Active

Passive magnetic devices measure the change in the earth’s magnetic flux created when a 

vehicle passes through a detection zone. Active magnetic devices, such as inductive loops, 

apply a small electric current to a coil of wires and detect the change in inductance caused 

by the passage of a vehicle. Figure 23 illustrates how one passive magnetic device works.

Microwave -- Doppler, Radar, and Passive Millimeter

Doppler microwave devices transmit low-energy microwave radiation at a target area on 

the pavement and then analyze the signal reflected back to the detector. According to the 

Doppler principle, the motion of a vehicle in the detection zone causes a shift in the 

frequency of the reflected signal. This can be used to detect moving vehicles and to 

determine their speed. Radar devices use a pulsed, frequency-modulated, or phasemodulated signal to determine the time delay of the return signal, thereby calculating the 

distance to the detected vehicle. Radar devices have the additional ability to sense the 

presence of stationary vehicles and to sense multiple zones through their range finding 

ability. A third type of microwave detector, passive millimeter, operates at a shorter 

wavelength than other microwave devices. It detects the electromagnetic energy in the 

millimeter radiation frequencies from all objects in the target area.

Passive Acoustic

Passive acoustic devices consist of an array of microphones aimed at the traffic stream. The 

devices are passive in that they are listening for the sound energy of passing vehicles.

Figure 23. Example of passive magnetic device.
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Ultrasonic -- Pulse and Doppler

Pulse devices emit pulses of ultrasonic sound energy and measure the time for the signal to 

return to the device. Doppler devices emit a continuous ultrasonic signal and utilize the 

Doppler principle to measure the shift in the reflected signal.

Video

Video devices use a microprocessor to analyze the video image input from a video camera. 

Two basic analysis techniques are used: tripline and tracking. Tripline techniques monitor 

specific zones on the video image to detect the presence of a vehicle. Video tracking 

techniques employ algorithms to identify and track vehicles as they pass through the field of 

view. The video devices use one or both of these techniques.

In field tests, some of these detectors did not function well at intersections and accuracy may 

be compromised by weather and road conditions as they depend on detected heat, sound, or 

radio waves. The video image camera detectors may suffer from accuracy problems and 

require installment expertise and setup time, but offer the capability of collecting additional 

traffic data beyond standard counts. For detail about these capabilities, see the FHWA report: 

Field Test of Monitoring of Urban Vehicle Operations Using Non-Intrusive Technologies

(http://ntl.bts.gov/lib/jpodocs/repts_te/6665.pdf). 

ADDITIONAL DATA COLLECTION SYSTEMS FOR ROADWAY INFORMATION

Agencies can collect some of the data for roadway databases using automated systems. For 

example, this report discusses various types of roadway data collection sensors that may be 

installed on a data collection van. Agencies collect traffic volume data with a combination of 

automated, semi-automated, and manual systems. The most advanced systems use permanent 

detectors (typically embedded in the roadway, but some systems rely on external sensors) tied 

to software that aggregates data for determining hourly and daily traffic counts as well as speeds 

and vehicle classifications. Agencies still widely use older technology (such as temporary 

installations of road tubes with digital or even pneumatic counters), especially at locations 

where the counts are collected for a short duration on an annual or even longer-term schedule. 

Agencies still collect some roadway data manually. Agencies may perform these efforts using 

paper-and-pencil forms or using a variety of field data collection devices including software 

implemented on a personal digital assistant (PDA), tablet, handheld data collection device, or a 

field data collection computer. The software products used in capturing these data, either 

remotely or by transcribing manual data into a system, share some common features. These 

features include: 

• Databases designed specifically to store a particular type of data - roadway inventory, 

traffic volume, pedestrian/bicycle counts, sign inventories, etc. 

• Tools for managing the data - record entry/acceptance, validation checks, sorting, 

editing, duplication detection, record deletion, etc. 
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• Output generators to provide data extracts, as well data aggregation or analytic reports. 

The most advanced of these tools and software are typically capable of collecting GPS 

coordinates for each record entered. The coordinates may be tied directly to a statewide or 

departmental GIS in order to support automatic interface and linkage with other relevant 

sources of roadway data through standard location coding and mapping methods.

With roadway data collection technology comes the opportunity, in comparison to older 

methods, to collect more data at substantially lower costs, usually in a more timely manner and 

often with improved precision and accuracy. It is true, however, that while these methods 

offer savings over options that would collect the same data manually, the choice facing most 

departments is not between two alternative methods of obtaining the data but rather whether 

or not to collect the data at all. It is usually much easier to document the cost of data than it is 

to prove its benefit. The FHWA Office of Safety conducted a study to develop guidance on 

methodologies that State and local DOTs could implement to make the case for investing in 

data collection, data systems and processes. A Guidebook was developed to assist States in 

justifying the decision to invest in additional data collection efforts related to safety. If a State is 

uncertain of the value of data collection, or if a State is having difficulty justifying the allocation 

of resources to data collection projects, the Guidebook provides instruction in how States may 

assess the potential impact of investment in safety data improvement. The Guidebook may be 

accessed at http://safety.fhwa.dot.gov/rsdp/downloads/guidebook.pdf. 

It is important to recognize the central role that data quality plays in determining both the cost 

and benefit of data for decision-making. Not all data collection methods are equal in terms of 

guaranteeing that the resulting dataset is reliable. In addition, when a State wants to justify the 

move to more advanced (and perhaps initially expensive) methods of data collection, it is 

important to be able to quantify the improvements that will result. Data quality improvements 

are important to quantify for decision-makers because they demonstrate the reliability of the 

data. The benefits of more reliable data are not lost on decision-makers even if those benefits 

are sometimes difficult to quantify as precisely as one would like.
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CHAPTER 7—ROADWAY DATA QUALITY MEASUREMENT

The management of the roadway systems development process to ensure data quality can be 

organized into three parts:

• Quality planning: selecting standards and how to meet them.

• Quality assurance: evaluations during project development.

• Quality control: monitoring project results and improving performance as needed.

QUALITY PLANNING

The data quality decisions may vary from State to State depending on their priorities. Quality 

planning involves deciding what level of quality to expect in the resulting system. Examples of 

these data standards are provided in the final section of this document (Roadway Safety Data 

Quality Measures). These standards for the system should be planned into the design of the 

system from the earliest possible point in the design cycle. 

As part of the quality planning effort, the design team would also conduct a benefit/cost analysis 

for achieving the desired level of quality. Assessing the downside of increased quality can be a 

difficult task for designers unfamiliar with roadway data reporting and analysis in general, or for 

those system users who are unfamiliar with system development. It is important, therefore, 

that the system design team include personnel from both the information technology and the 

user management functions so that decisions of how high to set the quality bar can be made in 

an informed manner and the resulting system is sustainable over time.

FHWA Office of Safety developed methodologies to estimate the costs and benefits of investing 

in data and data systems for safety. These methodologies are documented in Benefit-Cost 

Analysis of Investing in Data Systems and Processes for Data Driven Safety Programs Decision-Making 

Guidebook (http://safety.fhwa.dot.gov/rsdp/downloads/guidebook.pdf). 

QUALITY ASSURANCE

The quality assurance process includes those techniques used by the system development team 

to ensure that the system meets expectations. At this point in development, the design is 

complete and the system is being developed and tested before it is implemented. Quality 

assurance continues after implementation as well. The measurements that apply to the 

system’s performance may not seem familiar to those used to managing data quality, but they 

are very important for making sure that the system itself is not causing data problems. 

For example, does the roadway system design allow access by all users who have a 

responsibility of analyzing safety data and setting project priorities? A measure of what 

percentage of the critical users has flexible access for analysis is a quality assurance metric. It 

tells system managers if there is a problem with the system accessibility. Such “down time” 
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may not have a measurable impact on more traditional data quality measures (e.g., timeliness, 

accuracy, completeness, etc.) but can have a major impact on how well the system is perceived 

by users and thus how willing they are to continue using it. Following system implementation, 

the system developers and managers are the primary users of the quality assurance measures. 

These measures allow tracking of how well the hardware and software are performing and lets 

managers know if there is a problem that needs to be addressed. They also can serve as an 

early warning of problems that could affect data quality – the bottom line for most of the users 

of a system.

QUALITY CONTROL

For data managers and users, quality control is the most familiar part of the project planning 

process. Quality control is the set of measurements and procedures put in place to ensure that 

the data quality is meeting expectations. The measures of data quality can cover a wide variety 

of issues at a wide range of “levels” – from global indicators of overall quality to micro-level 

indicators of the validity of data in one particular field of the database. Quality control 

processes are the responses of the system (the software and the people working with it) to 

quality problems that arise. For example, data quality metrics would show if location data were 

not meeting expectations (e.g., location coding is not precise enough or features cannot be 

matched to a location on the roadway network). The quality control processes are the 

response to these questions – what do the data managers and collectors do about the 

problems.

Additional detailed information on quality measures are provided in Chapter 8, Roadway Safety 

Data Quality. 

BENEFIT/COST

The two most desired types of system-level performance measures for the roadway systems 

are those that would relate the availability of the data to lives saved; and those that would 

accurately track the costs maintaining the system. In other words, measurements of the 

benefits and costs associated with maintaining a complete and accurate record of the roadwayrelated data used for safety analyses. Operational departments (e.g. operations, infrastructure 

etc.) can also realize benefits in reduced costs and less duplication of data. 

On the surface, it would seem that the cost of collecting and maintaining roadway-related data 

would be an easy metric to define and obtain. However, the recent trend has been to 

distribute the responsibility of various roadway-related data to those users most closely 

involved in the collection and use of specific data. For example, the planning division may 

establish the base network from design plans, change orders, and as-built plans, while inventory 

updates may be made in the maintenance or other divisions. These distributed costs make it 

more difficult to obtain a reliable cost estimate. 
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Much of the roadway-related safety data is initially collected for other uses, which makes the 

definition of costs and benefits more difficult to distinguish from the State DOT’s routine 

operational roadway databases. A standard method for explicitly tracking the system cost 

might include the following components:

• Time spent collecting initial data (if not already collected for existing databases).

• Transmission of imports of data from existing systems.

• Initial software purchase and implementation or in-house development.

• Annual maintenance including any licensing and support.

• Separate line items for life-cycle costs of the hardware and software.

• Total number of roadway segments and features obtained electronically and the 

percentage that represents of total data collection for the entire system.

FHWA’s Benefit-Cost Analysis of Investing in Data Systems and Processes for Data Driven Safety 

Programs: Decision-Making Guidebook (http://safety.fhwa.dot.gov/rsdp/downloads/guidebook.pdf)

provides a methodology for such analyses.

CUSTOMER SERVICE

States may wish to measure the impact of their delivery of service to the users of the roadway 

and other safety-related data. Customer service, in particular, may be affected by the use of a 

decision support system that consolidates safety-related data and a State may wish to measure 

how much better able it is to meet customers’ needs (both internal to the agency and external 

customers) once data are available electronically. For example, the proportion of data requests 

met via a web portal (thus requiring little or no direct staff time) could be measured and the 

time it takes to deliver data following a customer request could be measured. States wishing to 

show the full impact and utility of their system will measure customer service as well as costs.

Michigan provides online maps that are integrated with the Web and available to local 

jurisdictions. Figure 24 provides examples of data and maps. 
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Figure 24. Michigan DOT sharing maps on the web.
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CHAPTER 8—ROADWAY SAFETY DATA QUALITY

The key to roadway-related safety analysis is location-based linkage of three basic data sources: 

• Roadway inventory (to provide detailed descriptions of locations and features).

• Traffic volume (to provide level-of-use statistics and measures of exposure).

• Crash data (for estimates of safety performance of locations and attributes). 

Until recently, the standard for safety data analysis was a simple rank ordering of locations 

based on their crash frequency, crash rate (frequency of crashes divided by AADT), or a 

weighted ranking using both measures. This analysis still is useful to provide engineers and 

other safety professionals with a good sense of where to look in the roadway network. With 

access to more detailed data describing locations and an increased ability to merge data from 

multiple data sources, more effective safety analyses can be conducted. 

States can create separate rank orderings of roadways that belong to a particular type. For 

example, rather than identify the “top 10” roadways overall, engineers might look at 

intersections, interchanges, highways, and county routes separately and compare among similar 

roadway types. In this way, they could examine a series of “top 10” lists for each of the 

roadway location types of interest. With the development of CMFs, engineers have started to 

focus on the attributes of roadway locations including:

• Type of intersection/interchange.

• Number of lanes and lane width.

• Presence or absence of traffic control, turn lanes, and other features. 

The availability of detailed data describing locations means that engineers can look at roadway 

location types on several dimensions and determine the most likely safety concerns and the 

predicted crash frequency for each. Modern network screening techniques, as described in the 

HSM, take advantage of richer data sources and increased ability to merge inventory, traffic, and 

crash data.

To assess a State’s ability to conduct safety analyses, data quality performance measures are 

needed for each of the key components: roadway inventory, traffic volume, and crash. For the 

crash data elements, there are several examples of data quality measurements including the 

FHWA Crash Data Improvement Program Guide

(http://safety.fhwa.dot.gov/cdip/finalrpt04122010/). There are, unfortunately, few sources of 

comprehensive data quality measurements for roadway inventory and traffic volume. In the 

discussion that follows, measures are presented that have been derived from the available 

resources including: 

• FHWA’s Performance Measures for Roadway Inventory Data.
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• NHTSA’s Program Advisory on Traffic Records Systems.

• NHTSA’s 2011 Model Performance Measures for State Traffic Records Systems.

• NHTSA’s Traffic Records 101 web-based training.

• Expert panel suggestions.

DATA QUALITY CHARACTERISTICS

The NHTSA Program Advisory for Traffic Records suggests six data quality characteristics that 

should be measured. These are defined below. 

Timeliness 

Information should be available within a specific timeframe to allow for meaningful analysis of 

the status of the issue under investigation (e.g., as soon as a roadway is open to the public, data 

about that roadway is available on the roadway inventory database; traffic data are updated 

prior to compilation of annual statistics in support of problem identification).

Accuracy 

Information within the database should be correct and reliable in describing the data element it 

purports to describe. Accuracy typically is enhanced through the practice of conducting 

consistency checks and validations on the data being entered into the database. For roadway 

data, this may involve field audits, validations against other data sources (including an agency 

GIS, as-built plans, etc.), as well as consistency checks to identify unexpected changes in crash 

frequency or severity distributions for specific locations.

Completeness

Information within the database should be complete for all roadways that are open to the 

public, whether designated State routes, county roads, or city streets. For any selected 

roadway location, each relevant data element should be available within the database and all 

required data elements within the record should be completed with appropriate responses.

Consistency/Uniformity 

Information collected should be consistent among all reporting jurisdictions with all reporting 

jurisdictions using valid data collection methods (e.g., traffic counts, lane measurements, rightof-way definitions). Ideally, information will be reported using nationally accepted and published 

guidelines and standards (e.g., MIRE Version 1.0) to define each data element.

Integration 

Information in one database should be capable of being interfaced or joined with information 

from other databases through use of common data elements called linking variables. An 
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example of integration is the linkage of information from a statewide crash database with data 

in a roadway inventory file to support problem identification, countermeasure selection/design, 

and program evaluation. 

Accessibility 

Information within the database should be readily available to all eligible users. States differ in 

how much and what types of roadway information they make available and to whom. A wellfunctioning system will support the State DOT as well as its local engineering partners with the 

data needed to perform their engineering-related duties. Access for other partners depends on 

a variety of factors including cost, as well as concerns over tort liability, data complexity, and 

analytic competence of the users. 

DATA QUALITY METRICS

States are recommended to develop performance measures to assess the quality of the data 

being entered and maintained in their roadway, traffic, and crash databases. Developing 

performance measures to assess the timeliness, accuracy, completeness, and consistency of the 

data within the databases can help to identify deficiencies and allow states to develop and 

implement actions to improve the data quality. Developing additional performance measures 

for the roadway, traffic and crash databases can permit a state to assess how well and 

completely the information in these databases integrates with other databases and how 

accessible the data are to eligible users.

The following tables provide examples of potential data quality metrics for each of the three 

key databases for safety analyses: roadway inventory (Table 2), traffic volume (Table 3), and 

crash data (Table 4). The source of each metric is indicated in the columns, along with a brief 

definition of the measurement. The metrics are organized according to the data quality 

attributes defined above. Metrics for integration may apply to all of the systems. These metrics 

expand upon those provided in the FHWA Performance Metrics for Roadway Inventory Data 

(http://safety.fhwa.dot.gov/rsdp/downloads/performancemeasures.pdf) that incorporates 

NHTSA performance metrics, to give practitioners with additional examples of potential 

metrics. Additional information on crash data metrics can be found in the FHWA CDIP Guide 

(http://safety.fhwa.dot.gov/cdip/finalrpt04122010/). It is up to each agency to decide what metrics 

are most appropriate for them.
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Table 2. Roadway inventory data quality metrics.

Metric Description/Examples

Timeliness Metrics

Update Cycle Measures the timing of updates to the file (continuous, annual, and other). 

⋅ Percent of records updated in a year.

⋅ Percent of records reviewed in a year.

Update Delays Delay from change in “ground truth” to posting of the change in the inventory file.

⋅ Number of days from roadway change is complete to posting update.

⋅ Number of days from completion of a periodic review to posting update.

⋅ Percentage of records updated electronically.

Accuracy Metrics

Features 

Locations

Measures variance between ground truth and the coded location of each feature.

⋅ Centerline location is within 10 feet of actual.

⋅ Cross-section data elements are accurate w/in +/- 1 ft.

Source of Data Measures the proportion of the database derived from accurate sources.

⋅ Percent of records based on “as built” plans.

⋅ Percent of records based on field measurements.

Error Rate Measures accuracy of data in individual records and data elements within a record.

⋅ Percentage of records with zero errors in predefined “critical” data elements.

Completeness Metrics

Roadways 

Included

Measures the percentage of defined locations that are included in the database.

⋅ Percentage of State-maintained roadway locations included in the inventory.

⋅ Percentage of local roadway locations included in the inventory.

⋅ Percentage of intersections/interchanges included in the intersection inventory.

⋅ Percentage of ramps included in the inventory.

Data Fields 

Collected

Measures the percentage of location records with data present in key fields.

⋅ Percentage of State roadway records with zero missing data in key fields.

⋅ Percentage of local roadway records with zero missing data in key fields.

⋅ Percentage of key fields coded “unknown.”

⋅ Percentage of roadway locations with defined geographic coordinates.
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Table 2. Roadway inventory data quality metrics (cont’d).

Metric Description/Examples

Consistency/Uniformity Metrics

Standards 

Compliance

Measures the number of data elements collected.

⋅ Percentage of data elements collected for segments.

⋅ Percentage of data elements collected for intersections.

⋅ Percentage of data elements collected for ramps.

⋅ Percentage of data elements collected for State & HPMS segments.

⋅ Percentage of data elements collected for local road segments.

Inter-jurisdiction 

Consistency

Measures the compliance with State standards for location inventory reporting 

across all reporting jurisdictions (DOT divisions, locals, MPOs, etc.).

⋅ Percentage of jurisdictions reporting.

⋅ Percentage of jurisdictions reporting complete data for key data elements.

⋅ Percentage of jurisdictions reporting within accuracy tolerances.

Year-to-Year 

Comparisons

Measures the percentage of records with unexplained/anomalous changes in the 

record from one year to the next. This could also be considered an accuracy 

measure.

Integration Metrics

Macro-Level 

Integration

Measures the number of databases that are linked. This is an overall indication of 

the number of files that can be merged. Each linkage of two files counts as one 

integration. For example, linking crash & roadway inventory = 1 linkage.

Micro-Level 

Integration

Measures the strength of linkage between two or more files. This is a measure of 

the proportion of records that should be linkable that, in fact, are linked in the 

resulting file.

⋅ Percent of crash records with a corresponding roadway inventory record.

⋅ Percent of traffic count records with a corresponding roadway inventory record.

⋅ Percent of crash records actually linked to roadway inventory records.

⋅ Percent of traffic count records actually linked to roadway inventory records.
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Table 2. Roadway inventory data quality metrics (cont’d).

Metric Description/Examples

Automated 

versus Manual 

Linkage

Measures the ability to created merged datasets linking two or more data 

sources using completed automated and automated plus manual processes.

Percent of crash and roadway inventory records linked without manual 

intervention.

Percent of crash and roadway inventory records linked after all processing 

is completed.

Percent of traffic volume and roadway inventory records linked without 

manual intervention.

Percent of traffic volume and roadway inventory records linked after all 

processing is completed.

Accessibility Metrics

External 

Agency Access

Measures the number of non-DOT users (and agencies) with access to the 

location-based safety data.

Internal DOT 

Users

Measures the number of internal users within DOT who have access to the 

location-based safety data.

Data Access 

and Analytic 

Requests

Measures the requests for data and analysis. This measure is a count of the 

number of requests that the data managers and analysts receive and 

complete in a period.

⋅ Number of requests for data extracts; and number fulfilled.

⋅ Number of requests for analytic results and number fulfilled.

⋅ Average time from request to completion.

User 

Satisfaction

Measures (via a survey) the number of users “satisfied” with the data and/or 

analyses available to them.
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Table 3. Traffic volume data quality metrics.

Metric Description/Examples

Timeliness Metrics

Update Cycle Measures the timing of updates to the file (continuous, annual, and other). 

⋅ Percent of records updated in a year.

⋅ Percent of records reviewed in a year.

Update Delays Delay from data collection to availability of volume data for analysis (i.e., 

the time it takes to post the raw data and any required calculated values 

such as AADT).

Accuracy Metrics

Features 

locations Percentage of records with “estimated” volume counts.

Source of Data Measures the proportion of the database derived from various sources.

⋅ Percent of records based on permanent counters.

⋅ Percent of records based on temporary (3-day? Other?) counts.

⋅ Percent of records based on locally-provided data.

Completeness Metrics

Roadways 

Included

Measures the percentage of defined locations that have traffic count data 

available.

⋅ Percentage of State-maintained roadway locations with actual counts.

⋅ Percentage of local roadway locations with actual counts.

⋅ Percentage of ramps with actual counts.

Consistency/Uniformity Metrics

Interjurisdiction 

Consistency

Measures the compliance with State standards for traffic volume reporting 

across all reporting jurisdictions (DOT divisions, locals, MPOs, etc.).

⋅ Percentage of jurisdictions reporting.

⋅ Percentage of jurisdictions reporting complete data.

⋅ Percentage of jurisdictions in compliance with data collection standards.
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Table 3. Traffic volume data quality metrics (cont’d). 

Metric Description/Examples

Integration Metrics

Macro-level 

Integration See the metrics discussed in Table 2: Roadway Inventory.

Accessibility Metrics

External Agency 

Access

Measures the number of non-DOT users (and agencies) with access to the 

location-based traffic volume data.

Internal DOT

users

Measures the number of internal users within DOT who have access to the 

location-based traffic volume data.

Data access and 

analytic 

requests

Measures the requests for data and analysis. This measure is a count of the 

requests that data managers and analysts receive and complete in a period.

⋅ Number of requests for data extracts; and number fulfilled.

⋅ Number of requests for analytic results and number fulfilled.

⋅ Average time from request to completion.

User 

Satisfaction

Measures (via a survey) the number of users “satisfied” with the data 

and/or analyses available to them.
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Table 4. Crash data quality metrics.

Metric Description/Examples

Timeliness Metrics

Overall 

Timeliness

Measures the time from the crash event to posting of the data in a 

database ready for analysis. This is the sum of submission timeliness and 

processing timeliness components defined below.

Submission 

Timeliness

Measures the time from crash event to receipt of the report by the 

custodial agency. This is the “delay” contributed by law enforcement 

agency processes including data collection, report review, and report 

submission. It should be reported for each law enforcement agency, along 

with the statewide average.

Processing 

Timeliness

Delay from receipt by the custodial agency to posting the data in a 

database ready for analysis. This may be measured in individual 

components as well as overall.

⋅ Number of days for creation of an image archive record.

⋅ Number of days for data entry.

⋅ Number of days for location coding.

⋅ Number of days for post-processing quality control review.

Accuracy Metrics

Edit Checks Measures the initial and final accuracy of crash reports against the 

standards set for edit checks and quality validation.

⋅ Percent of crashes with 1 or more fatal errors at time of submission.

⋅ Percent of crashes with 1 or more “warning” errors at time of submission.

⋅ Percentage of crashes with 1 or more fatal errors when accepted as final.

⋅ Percentage of crashes with 1 or more “warning” errors accepted as final.

Location 

Accuracy

Measures the proportion of crashes that can be “landed” using the 

statewide location coding system.

⋅ Percent of crashes “landed” using automated processes only.

⋅ Percent of crashes “landed” when accepted as final.
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Table 4. Crash data quality metrics (cont’d). 

Metric Description/Examples

Validation 

Against Driver 

and Vehicle 

Files

Measures the accuracy of data fields that can be validated against the driver 

and vehicle records.

Percentage of in-State driver’s license numbers with a corresponding driver 

history record.

Percentage of in-State vehicle identification numbers (VINs) with a 

corresponding vehicle registration record.

Percentage of VINs that decode accurately using standard VIN decoding 

software.

Percentage of crashes where the decoded VIN information disagrees with 

the vehicle make/model/type coded on the crash report form.

Validation 

Against Injury 

Surveillance 

Data

Measures the accuracy of (in particular) injury codes collected on the crash 

report form.

⋅ Percentage of crash-involved persons with a mismatch between the injury 

severity score on the crash report and the injury severity score recorded in the 

trauma registry and/or Emergency Department or hospital discharge databases.

⋅ Percentage of crash reports coded with an incorrect overall crash severity based 

on a comparison of to the injury surveillance system injury codes.

Completeness Metrics

Overall 

Completeness 

of Key Data 

Fields

Measures the percentage of reports with no missing data in key data fields. 

This usually is measured as an average statewide and separately for each 

individual law enforcement agency that submits crash reports.

Overall Missing 

Data

Measures the percentage of crash reports with no missing data.

⋅ Percent of crash reports with no (key) fields left blank.

⋅ Percent of crash reports with no unexplained or inappropriate use of 

“unknown” or “n/a” values.

Narrative and 

Diagram 

Completeness

Measures the percentage of reports with acceptably complete narratives 

and diagrams.
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Table 4. Crash data quality metrics (cont’d).

Metric Description/Examples

Consistency/Uniformity Metrics

MMUCC 

Compliance

Measures the percentage of MMUCC data elements and attributes 

collected

⋅ Percentage of MMUCC data elements collected/defined on the crash report 

form or through linkage.

⋅ Percentage of MMUCC data attributes (values within data elements) 

collected/defined on the crash report form or through linkage.

Interjurisdiction 

Consistency

Measures the compliance with State standards for crash data collection.

⋅ Percentage of jurisdictions reporting all reportable crashes – this is based on a 

comparison of the fatal+injury/total crashes reported

⋅ Percentage of jurisdictions with no unexplained/unexpected drops in the number 

of crashes reported in comparison to prior years.

Integration Metrics

Macro-Level 

Integration

Measures the number of databases that are linked. This is an overall 

indication of the number of files that can be merged. Each linkage of two 

files counts as one integration. For example, linking crash & roadway 

inventory = 1 linkage. Crash data may also be integrated with injury 

surveillance, driver, vehicle, and other databases.

Micro-Level 

Integration

Measures the strength of linkage between two or more files. This is a 

measure of the proportion of records that should be linkable that, in fact, 

are linked in the resulting file.

⋅ Percent of crash records with a corresponding roadway inventory record

⋅ Percent of crash-involved drivers with a corresponding driver history record.

⋅ Percent of crash-involved injured persons with a corresponding injury 

surveillance record.

⋅ Strength of linkage as measured using the Crash Outcome. Data Evaluation 

System (CODES) or other probabilistic linking software system.
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Table 4. Crash data quality metrics (cont’d).

Metric Description/Examples

Accessibility Metrics

Overall Access Overall Access

Data Access and 

Analytic 

Requests

Measures requests for data and analysis. This measure is a count of the 

number of requests that the data managers and analysts receive and 

complete in a period.

Number of requests for data extracts; and number fulfilled. Number of 

requests for analytic results and number fulfilled. Average time from 

request to completion.

User 

Satisfaction

Measures (via a survey) the number of users “satisfied” with the data 

and/or analyses available to them.
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CHAPTER 9—USE OF ROADWAY DATA IN SAFETY ANALYSIS

The FHWA provides and supports a wide range of data and safety analysis tools for State and 

local practitioners. These tools have been designed to assist practitioners in understanding 

safety problems on their roadways, link crashes to their roadway environments, and select and 

apply appropriate countermeasures. The tools' capabilities range from simple to complex. 

Some tools provide general information, while others allow more complex analysis of crashes 

under specific conditions and/or with specific roadway features. 

DATA COLLECTION AND MANAGEMENT TOOLS

Model Inventory of Roadway Elements (MIRE)

Safety data are key elements to sound decisions on the design and operation of roadways. 

Critical safety data include not only crash information but also roadway inventory and traffic

data. MIRE is a data dictionary that includes a listing of roadway inventory and traffic elements 

deemed essential to safety management and proposes standardized coding for each. Additional 

information on MIRE is available at http://safety.fhwa.dot.gov/tools/data_tools/mirereport/.

Highway Safety Information System (HSIS)

HSIS is a multi-State database that contains crash, roadway inventory, and traffic volume data 

for a select group of States. The participating States were selected based on the quality of their 

roadway, traffic and crash data available and their ability to merge data from these various files. 

The HSIS is used to analyze a large number of safety problems, ranging from the more basic 

"problem identification" issues to modeling efforts that attempt to predict future accidents from 

roadway characteristics and traffic factors. The HSIS is used in support of the FHWA safety 

research program and as input to program and policy decisions. The HSIS is also available to 

analysts conducting research under the NCHRP, university researchers, and others involved in 

the study of highway safety. Additional information on HSIS is available at http://www.hsisinfo.org/. 

SAFETY DATA ANALYSIS TOOLS

Highway Safety Manual (HSM)

The HSM provides information and tools to assist transportation professionals in making 

decisions that have a positive impact on highway safety. HSM data elements are provided in

Table 5. Focusing on objective measures of safety with a primary emphasis on crash frequency 

and severity, the HSM includes analytical tools to quantify and predict the safety performance of 

a variety of elements considered in road planning, design, maintenance, construction, and 

operation. The HSM also includes a synthesis of validated highway research and procedures 

that are adapted and integrated into practice. Additional information on the HSM is available at 

http://www.highwaysafetymanual.org/. 
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HSM

Table 5. HSM data elements.

Crash Data

• Date (year) 

• Location 

• Type 

• Severity level 

• Relationship to intersection (at-intersection, intersection related, not 

intersection related)

• distance from the intersection 12

Roadway Characteristics and Traffic Volume Data

Roadway Segments

• Area type (rural/suburban/urban) 

• Annual average daily traffic volume 

• Length of roadway segment 

• Number of through lanes 

• Lane width 

• Shoulder width 

• Shoulder type 

• Presence of median 

(divided/undivided) 

• Median width 

• Presence of concrete median barrier 

• Presence of passing lane 

• Presence of short four-lane section 

• Presence of two-way left-turn lane 

• Driveway density 

• Number of major commercial 

driveways 

• Number of minor commercial 

driveways 

• Number of major residential 

driveways 

• Number of minor residential 

driveways 

• Number of major 

industrial/institutional driveways 

• Number of minor 

industrial/institutional driveways 

• Number of other driveways 

• Horizontal curve length 

• Horizontal curve radius 

• Horizontal curve superelevation 

• Presence of spiral transition 

• Grade 

• Roadside hazard rating 

• Roadside slope 

• Roadside fixed-object density 

• Roadside fixed-object offset 

• Percent of length with on-street 

parking 

• Type of on-street parking 

• Presence of lighting 

Intersections

• Area type (rural/suburban/urban) 

• Major-road average daily traffic 

volume 

• Minor-road average daily traffic 

volume 

• Number of intersection legs 

• Type of intersection traffic control 

• Left-turn signal phasing (if signalized) 

• Presence of right turn on red (if 

signalized) 

• Presence of red-light cameras 

• Presence of median on major road 

• Presence of major-road left-turn 

lane(s) 

• Presence of major-road right-turn 

lane(s) 

• Presence of minor-road left-turn 

lane(s) 

• Presence of minor-road right-turn 

lane(s) 

• Intersection skew angle 

• Intersection sight distance 

• Terrain (flat vs. level or rolling)

• Presence of lighting
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IHSDM

Interactive Highway Safety Design Model (IHSDM)

IHSDM is a suite of software analysis tools for evaluating the safety and operational effects of 

geometric design decisions. Table 6 shows the IHSDM data requirements. The current version 

checks existing or proposed two-lane rural highway designs against relevant design policy values 

and provides estimates of a design's expected safety and operational performance. Future 

expansion of IHSDM crash prediction capabilities to include rural multilane highways and 

urban/suburban arterials is planned to match the 1st Edition Highway Safety Manual. Intended 

users include highway project managers, designers, and traffic and safety reviewers in State and 

local highway agencies and engineering consulting firms. IHSDM currently includes five 

evaluation modules – crash prediction, design consistency, intersection review, policy review, 

and traffic analysis. Additional information on the IHSDM is available at http://www.ihsdm.org/. 

Fundamental Data Elements (FDE) 

In 2012, the FHWA Office of Safety issued MAP-21 Guidance on State Safety Data Systems 

(http://www.fhwa.dot.gov/map21/guidance/guidesafetydata.cfm). The guidance identifies a subset of 

MIRE, referred to as the fundamental data elements (FDE), that, “when integrated with crash 

data, enables States to conduct a sufficient safety analysis to identify safety problems and make 

more effective investment decisions.” The guidance recommends that, “States should collect 

the FDEs on all public roads as soon as practicable in order to benefit from improved analyses 

as soon as possible.”

Safety Analyst

Safety Analyst is an AASHTOWare product that can assist State and local highway agencies to 

program site-specific highway safety improvements for implementation. Table 7 shows the 

SafetyAnalyst data requirements. Additional information on SafetyAnalyst is available at 

http://www.safetyanalyst.org/. 

Table 6. IHSDM data requirements. 

Highway segment geometry 

(Horizontal alignment, vertical alignment, crosssection)

Roadside geometry

Intersection geometry

Traffic volume data

Crash data
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Table 7. Safety Analyst data elements.

Roadway Segment Characteristics 

Data

• Segment number 

• Segment location (in a form that is 

linkable to crash locations) 

• Segment length (mi) 

• Area type (rural/urban) 

• Number of through traffic lanes (by 

direction of travel) 

• Median type (divided/undivided) 

• Access control 

(freeway/nonfreeway) 

• Two-way vs. one-way operation 

• Traffic volume (AADT) 

Crash Data

• Crash location 

• Date 

• Collision type 

• Severity 

• Relationship to junction 

• Maneuvers by involved vehicles 

(straight ahead/left turn/right 

turn/etc.) 

Ramp Characteristics Data 

• Ramp number 

• Ramp location (in a form that 

is linkable to crash locations) 

• Area type (rural/urban) 

• Ramp length (mi) 

• Ramp type (on-ramp/offramp/freeway-to-freeway 

ramp) 

• Ramp configuration 

(diamond/loop/directional/etc.) 

• Ramp traffic volume (AADT) 

Intersection Characteristics 

Data 

• Intersection number 

• Intersection location (in a form 

that is linkable to crash 

locations) 

• Area type (rural/urban) 

• Number of intersection legs 

• Type on intersection traffic 

control 

• Major-road traffic volume 

(AADT)

• Minor-road traffic volume 

(AADT)

Safety Analyst
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PEDESTRIAN AND BICYCLE SAFETY TOOLS 

Pedestrian and Bicycle Crash Analysis Tool (PBCAT)

The Pedestrian and Bicycle Crash Analysis Tool (PBCAT) is a software application designed to 

assist State and local pedestrian and bicycle coordinators, planners, and engineers address 

pedestrian and bicyclist crash problems. PBCAT helps users create a database of details 

associated with crashes between motor vehicles and pedestrians or bicyclists, analyze the data, 

produce reports, and select countermeasures to address problems identified. Additional 

information on PBCAT is available at http://www.bicyclinginfo.org/facts/pbcat/index.cfm.

Pedestrian Safety Guide and Countermeasure Selection System (PEDSAFE)

The Pedestrian Safety Guide and Countermeasure Selection System (PEDSAFE) provides

practitioners with the latest information available for improving the safety and mobility of those 

who walk. This online tool gives users a list of possible engineering, education, and/or 

enforcement treatments to improve pedestrian safety and/or mobility based on user input 

about a specific location. Additional information on PEDSAFE is available at 

http://www.fhwa.dot.gov/research/deployment/pedsafe.cfm.

Bicycle Countermeasure Selection System (BIKESAFE)

The Bicycle Countermeasure Selection System (BIKESAFE) provides practitioners with the 

latest information available for improving the safety and mobility of those who bicycle. 

BIKESAFE's resources include an overview of bicycling in today's transportation system and 

information about bicycle crash factors and analysis and selecting and implementing bicycling 

improvements. BIKESAFE's tools allow users to select appropriate countermeasures or 

treatments to address specific bicycling objectives or crash problems. Additional information on 

BIKESAFE is available at http://www.bicyclinginfo.org/bikesafe/.

INTERSECTION/INTERCHANGE SAFETY ANALYSIS TOOLS

Interchange Safety Analysis Tool (ISAT) 

The Interchange Safety Analysis Tool (ISAT) provides design and safety engineers with an 

automated tool for assessing the safety effects of basic geometric design at typical existing 

interchanges and adjacent roadway network. ISAT also can be used to predict the safety 

performance of design alternatives for new interchanges and prior to reconstruction of existing 

interchanges. The primary outputs from an analysis include the number of predicted crashes 

for the entire interchange area, the number of predicted crashes by interchange element type, 

the number of predicted crashes by year, and the number of predicted crashes by collision type. 

The ISAT tool is available in Excel spreadsheet format. Additional information on ISAT is 

available at http://www.fhwa.dot.gov/publications/research/safety/07045/07045.pdf.
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Surrogate Safety Assessment Module (SSAM)

The safety of intersections, interchanges, and other traffic facilities most often is assessed by 

tracking and analyzing police-reported motor vehicle crashes over time. Given the infrequent 

and random nature of crashes, this process is slow to reveal the need for remediation of either 

the roadway design or the flow-control strategy. This process is also not applicable to assess 

the safety of roadway designs that have yet to be built or flow-control strategies that have yet 

to be applied in the field. 

The Surrogate Safety Assessment Module (SSAM) is a technique combining microsimulation and 

automated conflict analysis, which analyzes the frequency and character of narrowly averted 

vehicle-to-vehicle collisions in traffic, to assess the safety of traffic facilities without waiting for a 

statistically above-normal number of crashes and injuries actually to occur. Additional 

information on SSAM is available at 

http://www.fhwa.dot.gov/research/tfhrc/projects/projectsite/safety/ssam/index.cfm.
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CHAPTER 10—SUMMARY 

This Roadway Data Improvement Program: Supplemental Information Resource is intended to serve 

as resource document for practitioners in a State DOT and the allied agencies that provide or 

use roadway data. The primary focus of the RDIP is on the data systems used for safety 

decision making, and in particular, those that would support engineering efforts such as 

network screening (problem identification), diagnosis, countermeasure selection, and program 

evaluation. The RDIP is focused on the quality of data in these systems with particular 

attention to the six key attributes of timeliness, accuracy, completeness, consistency, 

integration, and accessibility of roadway inventory and traffic volume data. This Resource

includes information on data quality as well as various programmatic uses of the data for safety 

analysis.

This RDIP Supplemental Information Resource synthesizes information from a broad range of 

topic areas on highway transportation. Roadway data systems are described along with their 

associated data collection tools and methods (including automated data collection). Uses of the 

data for planning, design, and safety improvement are discussed along with the various options 

for data management and overall system design. Examples of data quality metrics and safety 

analyses are provided as well.

Users of this Resource are encouraged to approach it as a convenient synopsis of the 

engineering highway safety field. Its focus on current practices provides assurance that every 

tool, technique, or technology presented here is in use and has produced results for at least 

one State. Moreover, the presentation of state-of-the-practice techniques for safety data 

analysis gives a State a comparison point for an internal review of its own systems. 

RDIP technical assistance team (TAT) site visits are available to help the State. FHWA selects 

subject matter experts who will travel to a state, conduct workshops, and develop 

recommendations tailored to the specific needs of that State. The TAT leaves a matrix of 

recommendations with the State at the end of the site visit, and then follows up with a full 

report containing descriptions of the status of a State’s systems and recommendations for 

improving the quality of the data in those systems.
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APPENDIX A—EXPLANATION OF DATA FLOW DIAGRAMS

A DFD is a graphical means of describing the flow of data through a system that produces, 

changes, or stores data. The DFD is a visual depiction of an automated or manual system from

the perspective of the data. The DFD view uses the four graphic shapes defined below to 

describe the system. A DFD differs from a flowchart in its perspective: a flowchart describes a 

system of programs or manual processes that manipulate the data, while a DFD describes a 

system from the perspective of the flow of information. A DFD is drawn from most general to 

most specific in a series of levels of detail. Each page, or diagram, describes a part of the 

system with the first page DFD showing the most general picture of the data and its flow 

through the entire system. Any further pages (or "lower levels") of the DFD provide 

progressively more detail about the processes being described.

State

Department of

Transportation

ENTITY - This box represents an entity that is 

involved in the flow of data. Entities are ordinarily a 

person, group of persons, an agency, etc.

30 Local Road Database

DATA STORE - The rectangular box represents a 

data storage file(s) incorporating the data that are 

flowing into it. These file(s), or data stores, can be 

computerized or manual. The number is unique for 

each data store throughout all of the DFDs to 

ensure that each data store, or file, is specifically 

identified.

Assign Roadway 

Location

PROCESS - This oval represents a process or 

activity that is performed on the data flowing into it. 

The process is used when the data is actually 

changed or enhanced in such a way that it is 

important to show in the DFD. The change in the 

data can be effected by either an automated or a 

manual procedure. The asterisk signifies that more 

specific detail about this process may be found on a 

lower level page of the DFD.
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Traffic Counts

DATA FLOW - This arrow represents a flow of 

data into, or out of, an entity, data store, or 

process. The designer labels the data flow arrow 

when necessary to describe clearly the specific type 

of data in the flow.
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APPENDIX B—EXAMPLE SYSTEM BUSINESS FUNCTIONS

To assist States in developing and integrating the MIRE into a management information system 

structure that will provide greater utility in collecting, maintaining, and using MIRE data, FHWA 

has undertaken the MIRE Management Information System (MIRE MIS) project. It is important 

to be able to link statewide crash data to the MIRE data elements for use in safety improvement 

identification and analysis. A MIRE MIS will enable users to merge roadway and traffic data with 

crash data to enhance data analysis capabilities. The following table shows the high-level 

business requirements that a developer must satisfy when developing the MIRE MIS.

MIRE MIS Functions

1.0 General

1.1 Address key State-approved performance indicators.

The suite of performance indicators will vary among States, but there are three main types 

of performance measures that can be supported: 

a. Software/system service levels: these are indicators of the function of the software itself, 

useful in benchmarking and management of users. Examples include tracking the number 

of records processed, proportion of records rejected, number of users, and system 

downtime. These measures would also be useful in tracking the cost of the system, 

including the costs associated with obtaining and storing the data.

b. System performance: these are comprised of data gathered to support agencies’ indicators 

of performance in key areas (such as safety, mobility, asset management, etc.). Indicators 

that could be useful in linking the availability of MIRE-compliant data to improved safety 

(lives saved, injury levels reduced, and crashes avoided) would be included in this set of 

measurements.

c. Data quality: these are measures of the timeliness, accuracy, consistency, and 

completeness of the data in the MIRE MIS

1.2 Work within context and framework of State-specific approach.

The MIRE MIS must fit within and support a State’s existing business practices. For 

example, States define and combine data across segments in a variety of ways. The MIRE 

MIS will require flexibility to support multiple segmentation methods including those 

defined by changes in roadway features as well as those defined by distance, or a 
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combination of both. Varying methods of aggregating data across segments must be 

accommodated including aggregation based on similar features, by route, by contiguous 

segments, rolling averages, and others. In essence, the segmentation in the core roadway 

inventory file will be mirrored by MIRE MIS.

2.0 Database Design Technical Diagram

2.1 Using a modeling tool such as an Entity Relationship Diagram or other modeling tools, 

document State-specific information required for creation of the database.

Modeling tools are useful from the IT perspective in that they provide a succinct view of 

the data, data sources, and the interactions among those sources. State-specific entity 

relationships are incorporated into the data model to guide the database design and insure 

that all necessary inputs, outputs, and linkages are supported.

2.2 Develop a technical diagram that identifies the groupings of data and elements used by 

name and description.

The technical diagram complements the system’s data dictionary and the data model 

described in the Entity Relationship Diagram by showing the flow of data through the 

system and the major groupings of data by sources and outputs.

2.3 Develop a technical diagram that defines the relationship between the data groups and 

correctly structures (normalizes) the data.

Data relationships are described as one-to-one; one-to-many; many-to-one; and many-tomany depending on how records from one source “map” to records in another source. 

To take a trivial example, crashes have a many-to-one relationship with any particular 

segment in the database. In a fully normalized database, each data element is present 

once, but can (by virtue of its specific relationships) be accessed through multiple paths.

2.4 Document the location and structure of system integration points both for import and 

export data relevant to the MIRE MIS.

As part of identifying the sources and outputs for the MIRE MIS, all of the points for

importing into and exporting data out of the system are defined in terms of the interface 

requirements, the point in the system’s process the import or export occurs, how the 

operations run (on demand, automated batch processing, as a step in the records 

management processes at the source, etc.), and how often.
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3.0 Local Data Validation and Entry

Digital Imaging and Workflow

3.1 Provide ability to accept both original data entry and import from other data sources.

In addition to data import points, the system must accommodate manual data entry both 

from a central location and via remote access (e.g., web-based users with permission to 

add records). The processes for both importing data electronically and manual data entry 

will be documented.

3.2 Provide ability to validate both electronic transmission and direct data entry based on 

defined business rules.

Systems evolve over time. It is anticipated that all implementations of MIRE MIS will begin 

with a set of data validations arising from the MIRE data dictionary as well as any Statespecific data definitions that must be accommodated. Over time, as States identify errors 

that could be trapped through automated means, it is anticipated the number and 

complexity of validation checks will increase. The MIRE MIS will be designed with a basic 

set of error checks, but it will also accommodate the addition of new edit checks as 

defined by the users.

3.3 Provide electronic notification for critical and non-critical data errors.

Individual records will be flagged whenever any validation rule is violated. These rules are 

generally grouped by severity into critical (often called “fatal errors”) and non-critical 

(“warnings”). Most commonly, fatal errors cause a record to be rejected—such records 

are held in a pending database awaiting correction and resubmittal. Errors generating a 

warning message may be allowed to remain uncorrected—the record is passed into the 

production database and updated if a correction is received. All errors are logged so that 

they may be analyzed and used for aggregate reporting of data quality measurements of 

accuracy, completeness, and consistency.

3.4 Establish communication protocol or use existing secure protocols based on industry 

standard mechanisms.

The MIRE MIS must be capable of one- and two-way communication with various system 

users, including data collectors, as well as interface with a variety of other systems. These 

communications typically require a secure connection between the user/source and the 
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centralized system both to insure data integrity and to protect sensitive information from 

release. MIRE MIS will be designed to accommodate existing secure communications 

protocols in States where these exist, but it will also require some new communication 

protocols to be established in some States.

Internet Browser Screens

3.5 Provide standard templates for data entry and validation through browser interface.

The MIRE MIS data entry function will be web-based. Screens that open in a browser on a 

secure website will be built around a standardized template for all MIRE MIS 

implementations (customizable to meet a State’s specific needs). Validation (including edit 

checks and interfaces with other systems) will take place during data entry and 

immediately upon submission of the data through the web. Data quality feedback (edit 

check results) will be provided to the user via the same web interface. 

3.6 Provide notifications to log and track data input, updates, and deletions.

System management reports available to the custodian of the MIRE MIS records and IT 

support staff will track user’s inputs, updates, and deletions. Error logs will also be stored 

to support management reporting of data quality.

3.7 Provide data collection tools via browser, such as GIS, mobile mapping.

The user interface for data collectors will be web-based. This interface will include 

standard screens for data entry as well as screens for users to access assistive 

technologies such as the GIS or map-based location validation and input.

3.8 Provide electronic submission for added digital or scanned attachments.

Images may be appended to any record during data entry or updating. Users will have the 

option of directly uploading scanned or digital images into an image archive in the MIRE 

MIS. Because the data and images are associated, data managers and users will have the 

ability to search images in the database using a variety of search terms limited only by their 

access to the data elements in the database.

Electronic File Transmission 

3.9 Provide the ability to accept data from other local or State databases
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MIRE MIS will accept data from local or other State roadway inventory files for which an 

interface has been defined or provided. This serves to reduce the amount of data that 

must be entered manually into the system and reduces the cost of data collection to the 

State by taking advantage of already existing sources—thus reducing redundancy.

3.10 Document and publish standards for data collection and transmission.

In order to ensure consistent and error-free data, all entries into the system must be 

validated prior to acceptance into the production database. A single unified set of 

validation rules and data definitions will be supplied to all data collectors. In addition, a 

package for those wishing to implement electronic data sharing (uploads and downloads) 

will also be provided with data transmission guidelines and require file structures. A 

testing protocol for acceptance of electronic data will also be developed and shared with 

these external sources of data.

4.0 Central Data Validation and Entry

Document imaging and Workflow

4.1 Provide an imaging system that contains a database that can provide access to all 

supporting documentation associated with any roadway segment.

The imaging system supports users by making visual evidence available for review. 

Because each image is tied to the database record for a particular segment, the MIRE MIS 

also serves as an indexing system for sorting and retrieving of the images as well. While 

not intended to replace or function as a Photolog system, the image system in MIRE MIS 

can fill the need for storing of both scanned documents as well as digital photos captured 

in the field. This can support multiple uses including in-depth engineering analysis (e.g., 

condition diagramming) as well as asset management.

4.2 Receive and validate quality of submitted digital images.

Digital images can be most simply validated by checking the final image record size against 

the original (as submitted). Other methods may also be used including checksums, and 

use of image “wrapper data” which can be examined for both a match to the original 

submission and already existing records in the MIRE MIS database. In rare cases (such as 

poor initial image quality), human intervention—a manual review of the images—may be 

required. These are more a matter of State agency policy and procedures than a function 

of MIRE MIS; however, the MIRE MIS will support a review of images by the staff.
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4.3 Scan and index paper documents received from local agencies into a direct-access storage 

medium, creating digital images of reports and all supporting documentation provided.

In addition to digital photographs, the MIRE MIS image archive will be able to collect and 

store scanned images (of documents, hard-copy photos, or other materials obtained from 

local agencies) through digital scanning. As this process will be managed centrally by the 

MIRE MIS data steward agency staff, it assumed that indexing information will be added 

(i.e., image “wrapper” information) at the time the materials are scanned. This action will 

allow the scanned images to be added to the image archive and treated in the same 

manner, from that point forward, as are the digital images received from external sources 

(i.e., electronically transmitted to MIRE MIS).

4.4 Provide a unique identifier for each record, including for records received from local 

agencies.

There are two types of unique identifiers that may be implemented in MIRE MIS: systemassigned and State assigned. MIRE MIS will assign a unique identifier to each record 

received as a function of adding the record to the production database. In addition, each 

State may have its own record identifiers that it can assign (through manual or automated 

processes) to the records as they arrive. MIRE MIS could track local agencies’ record 

numbers but these are not assumed to be “unique” in that multiple agencies could 

conceivably assign the same record numbers to data in their own stand-alone systems. 

These record numbers are retained so that the State may use them in future contacts with 

the local agencies (e.g., to discuss a particular record or roadway segment).

4.5 Establish a process for returning manual or electronic data to local agencies for correction 

and track receipt of corrected submissions.

The MIRE MIS error logging and tracking function is designed to support State data quality 

management processes fully. Each error in each record is recorded as part of the “as 

submitted” data. Records with a critical (fatal) error are automatically rejected (i.e., not 

added to the production database) and are held in a pending status until those serious 

errors are corrected or (at the State’s option in some cases) over-ridden. Non-critical 

errors (i.e., those that generate a warning only) may be handled in a number of ways (at 

the State’s option). Typically, non-critical errors are noted in the record but the record 

itself is not barred from addition to the production database. States may choose to hold 

these records in a pending status awaiting correction or not. In any case, the MIRE MIS 

supports notification of the submitting agency/staff of both critical and non-critical errors 

through case-level and aggregate reports. Case-level reports are specific to an individual 
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record and typically are used to identify a record that contains a critical error that must 

be corrected. Aggregate reports summarize the errors noted from each data submitter 

over some defined period (e.g., monthly, quarterly, annually). The latter are a useful 

source of feedback to the submitting agencies and may be helpful in designing training 

content or revising data collection instructions/manuals.

Finally, MIRE MIS retains a log of all errors and all reports that contained an error. A 

tracking system is established that uses this information to generate reminder notices for 

any errors that have not been corrected by a specified time (e.g., 2 weeks after 

notification). These notices are provided to the system’s administrators automatically and 

may be sent electronically to the submitting agencies responsible for correcting errors.

4.6 Establish automated workflow routings and work queues for data transmitted from other 

systems (e.g., PMS, bridge, crashes).

MIRE MIS supports agency workflows by notifying designated staff when a record matching 

an established routing definition is added to the system. These may be useful in a number 

of situations. One example is to establish work queues for internal staff (e.g., location 

coders, data quality managers, etc.). Another example may be to provide data from the 

roadway inventory files for external users such as the FARS analysts who may need 

accurate roadway segment descriptions to add to their records of fatal crashes.

MIRE MIS Application

4.7 Develop MIRE MIS application and relational database.

This is the main system development task and takes place after all the functions and Statespecific modifications have been described. 

4.8 Capture Graphical User Interface (GUI) input screens that enable key from image or 

paper processes if needed; including split screen.

This task results in the main data entry system and process control screens for centralized 

scanning. It supports the paper-management process at the agency serving as the steward 

of statewide roadway inventory data. The same (or similar) data entry screens may be 

made available to external users in a form suitable for web-based remote entry of records.

4.9 Establish an intuitive flow for data entry and include features such as highlighting, table 

driven drip down lists, pre-populated fields, capturing system dates, etc. 
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The MIRE MIS data entry process will support high quality data entry through user 

assistance and constraints. Pick lists, for example, serve to constrain users’ inputs in 

comparison to entries made into a free text field so that only allowable information (data 

that will pass basic system edits) may be entered. Highlighting of specific field on the data 

entry helps users to identify errors and mandatory data fields so that these may be 

addressed during initial data entry rather than waiting until the report is submitted.

4.10 Provide ability to flag records and data elements based on Federal and State rules, close 

out year rules, etc. (e.g., data used for HPMS, data used for SafetyAnalyst, data onsystem/off-system).

These flags maybe treated as a specific type of validation rule which results in mandatory 

corrections (if the data as entered do not meet the reporting requirements) and for 

work-flow routing (e.g., flagging relevant records for the staff responsible for HPMS, HSIS, 

etc.)

4.11 Provide ability to search/access any field or digitized documents.

In the search/access function, the entire coded portion (all database contents will the 

exception of images) of the MIRE MIS can (potentially) serve as the indexing system. This 

means that users with the appropriate access permissions could search on any field and 

obtain records matching a specified search criterion. The search function will support 

both simple and complex queries. Simple queries are those that make use of one or a 

limited number of filters. Complex queries are those that may require the use of a query 

builder tool or a query model in order to ensure that all the tables in the MIRE MIS are 

accessed correctly. Queries result in a standardized list report that lists all records 

matching the search criteria. These may be fed into a reporting system that can generate 

output that is more advanced.

The search of the digital image archive works in a similar way with the addition of the 

capability to specify a particular document type of interest (photo, scanned report, all 

images, etc.).

In addition to this powerful query function designed for internal users of the MIRE MIS, it 

is also possible to support external users (and less-technical internal users) through a 

constrained query tool made available online. This tool is designed to allow users to 

build a query by selecting from among allowed data fields, establishing sort and filtering 

options, date ranges, and other criteria. 
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4.12 Support the designated number of internal and external users.

MIRE MIS stewards may establish limits on access to the system. Each user must 

establish an account that is secured with a login identifier and password. The system’s 

administrators can establish user access levels by class (Administrator, database manager, 

general user, data entry, etc.) and for each individual (i.e., users may be granted selective 

access to some portions of a higher level class without being given the full permissions 

associated with that pre-defined class. Permissions may be established at the record-level 

and for each database field independently to allow individual users (and classes of users) 

permission to view, edit, and/or delete information as separate permissions established in 

the user profile.

Mapping Tools

4.13 Provide an on-line, point-and-click, mapping location tool for data entry.

As a proven way to increase location data accuracy, users responsible for entering 

records will have access to a “smart map” which gives them a point-and-click interface 

for entering location data. Users can call up the map, zoom, pan in any direction, and 

click on the precise location of interest. The map will then supply all required location 

information and auto-populate the relevant fields on the data entry form. This may 

include latitude/longitude coordinates, roadway name(s), route number(s), segment 

identification number, milepoint/milepost, and any other information that is available from 

within the department’s or State’s GIS. If a statewide GIS does not exist, it is possible to 

implement the smart map capability using generic mapping tools (e.g., Google Earth).

4.14 Support a multi-linear referencing system.

MIRE MIS can incorporate translation tables to support multiple location coding methods 

including latitude/longitude, milepoint, milepost, and others. Ideally, the information is 

available within a GIS and the act of clicking on the smart map generates the relevant 

location information for entry into MIRE MIS. Where this is not feasible, MIRE MIS will 

still have the capability to store multiple location codes and the State can use the 

information entered into MIRE MIS to develop the desired translation tables.

5.0 Central Data Review and Analysis
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Database

5.1 Provide for MIRE MIS retention and archiving as determined by State.

Each State may establish its own record retention and archival process for MIRE MIS. The 

system can generate an archive file organized by year, which the State can then store 

separately. Archived records may be accessed through the MIRE MIS by the reverse 

process of “reattaching” the archive to the system at which the archived records become 

another database source file that can be searched and used in analyses.

5.2 Provide a scalable and secure relational database with backup and recovery procedures.

The MIRE MIS is designed to work well regardless of the number of records. The 

database is secure with administrator control over user access and permissions. Backup 

and recovery are supported through automated and administrator-controlled processes.

Analysis Tools

5.3 Provide web enabled analysis tool for authorized user groups.

Authorized users may access a set of web-based analysis tools include search and report 

generating functions. The search tool will be designed to enable users quickly specify 

criteria such as date ranges and record selection/filtering criteria based on any field in the 

database to which they are allowed access. The search feature results in a list of matching 

records that is then available for use in analyses. From the user’s perspective, the 

selection criteria and report specification are just two steps in the process of generating a 

desired output from the system. Multiple user-generate output types are supported 

including a simple list report, one-way and multi-way cross tabulations, graphic displays, 

and data extracts. The State may specifically limit users as to which types of report they 

may obtain.

5.4 Support export of data for HPMS.

5.5 Support export of data for SafetyAnalyst and other tools as approved.

With any known standard for data submissions, MIRE MIS can be programmed to generate 

a compliant data extract. The system will be developed with the capability to provide data 

extracts matching the HPMS and SafetyAnalyst data input requirements. System 

Administrators will have access to a tool that will allow them to update these output 
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definitions and create new ones as the need arises.

5.6 Allow retrieval of roadway and crash data for proposed safety projects.

The MIRE MIS search and reporting functions support the intended use of the data in 

support of safety projects by providing authorized users with a way to select relevant 

records and generate output reports. The search feature’s output of a simple list of 

matching records also supports users’ access to individual records including inventory, 

crash, traffic, and any other data available through MIRE MIS. One possibility is that the 

data could be accessible through a GIS providing users with a map-based interface that 

would allow them to click on a location, call up crash reports for that location along with 

the related inventory and traffic data. This functionality is supported by MIRE MIS as it can 

provide data and records to an external GIS. MIRE MIS’s web-based spatial analysis tool 

supports this capability for authorized users.

5.7 Provide analysis tools to authorized users in the form of queries and ad hoc reports from 

MIRE MIS.

The analysis functions within MIRE MIS are not constrained—every field in the database 

can be used in a query or report. User permissions established by the Administrator 

control that fields are available to a particular user. The internal query and reporting 

capabilities are akin to those described for a general user audience via the web. In fact, 

the same tools are available to both users, but the one intended for general user access is 

constrained in that selected fields are not available for query or analysis. Internal users 

can access any field as long as they have the appropriate permissions as established by the 

System Administrator.

Spatial Analysis Tools

5.8 Provide a web enabled spatial analysis tool for authorized user groups.

Authorized users will have access to a spatial analysis tool that can generate cross 

tabulation reports and support map-based output of results.

5.9 Provide ad hoc reporting ability for spatial analysis tool.

The ad hoc reporting capabilities of the spatial analysis tool include one-way and multi-way 

cross tabulations, and reporting of frequency counts and proportions (percentages).
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5.10 Provide graphical depiction of analyses based on, for example, road sections, bridges, and 

intersections for specific timeframe and weather conditions.

Users of the web-based spatial analysis tool will have the option of producing graphical 

output in addition to the tabular reports. The graphical output may include support for 

condition diagrams, intersection/site diagrams of crashes, frequency histograms, pie charts, 

line graphs, and others.

6.0 Central Data Distribution

Ad Hoc User Reports

6.1 Provide reports, queries, and/or inquires defined by the State, such as proposed safety 

project information. 

The analytic tools designed for MIRE MIS support user-generate ad hoc query and analysis 

functions. Any query or analysis defined in the system may be saved for future use and 

shared with other users. 

Standard Reports

6.2 Allow authorized users to view and generate pre-defined reports, queries, and/or 

inquires via a browser.

State-defined standard reports are established in the system in the same way as ad hoc 

reports. They are simply saved and shared with the user community in general. The 

web-based analysis tool will allow users to access any pre-defined query or report 

accessible to them based on their permissions established by the System Administrator. 

Users will be blocked from generating output from a standard/pre-defined report for 

which they do not have the required user permissions as established by the System 

Administrator.

6.3 Provide an interface for submitting requests for additional standard reports via browser. 

Users may request database searches and reports (i.e., analytic assistance) by completing 

a form available on line. The MIRE MIS system will log and track all of these user 

requests. It will also support workflow routing of requests to the appropriate agency 

staff.
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Government Reports

6.4 Generate mandated and standard reports for federal agencies.

MIRE MIS will be programmed with a series of standard reports meeting the 

requirements established under federal programs. These report definitions will be 

accessible to the relevant agency staff so that they can be modified in the future as the 

reporting requirements change.

File Transmissions

6.5 Provide aggregate information to other agencies and organizations via various media or 

through direct file transmission.

MIRE MIS will support electronic sharing of reports through creation of .pdf output. The 

system will also support data transmission by allowing users to define data extracts, which 

can then be shared electronically. The System Administrator in the user profile will 

establish user permissions for the creation of data extracts.

7.0 User Assistance Modes

7.1 Embedded help file & help file index.

MIRE MIS documentation will be used to generate a help file that will be available to users 

from within the system interface (by clicking on the “Help” button. The embedded help 

will document all user-accessible system features and the functioning of each option in the 

user interface.

7.2 Reporting and standards instruction manual.

MIRE MIS data definitions and relevant data validation checks will be documented in an 

instruction manual for data collectors. The same manual can be shared with analysts using 

the system so that they can be made aware of the conditions for data acceptance into the 

production database.

7.3 Context-sensitive help on data fields.

For data fields on any data entry form, users may access help in the form of information 

derived from the data collector’s instruction manual. This will include the data definition 

for that field, instructions for completing data entry into the field, and relevant validation 
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rules for that field.

7.4 Step-by-step system function assistance (Show me how).

For user-accessible functions defined in the system, the help file will contain step-by-step 

instructions on how to access and complete a desired task using that function. This type 

of user assistance is akin to a “show-me-how” set of steps that take the user through 

every step in the process.

7.5 Function completion wizards (Do it for me).

For selected user-accessible functions, a wizard-based utility will provide users with 

expert-level automatic task completion. This is akin to a “do-it-for-me” command that 

will launch a default sequence of instructions and form completions by the system. Since it 

is impossible to anticipate every possible user need, this type of wizard-based approach 

effectively supports only the most standardized (routine) tasks; however, the wizards also 

serve an important training function within the system as they provide users with a 

complete and correct example upon which to base their own future uses of the system.

7.6 Interactive tutorials.

The MIRE MIS also includes a set of interactive tutorials. These are delivered as an 

animated, annotated slide show with multiple branching opportunities in which the user 

controls the forward progression based on choices made at each step in the process. This 

type of tutorial is a powerful user aid since it provides full step-by-step examples of task 

completion using the system. The ability to annotate key features of the presentation 

make this a good learning tool as well since users may be presented with options and 

explanations while they are working through the programmed examples.