Performance Measures for Roadway 

Inventory Data

FHWA Safety Program

http://safety.fhwa.dot.gov
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

i

FOREWORD 

The Federal Highway Administration’s (FHWA’s) Highway Safety Improvement Program (HSIP) 

is a data driven program that relies on crash, roadway, and traffic data for States to conduct 

effective analyses for problem identification and evaluation. The FHWA developed the Model 

Inventory of Roadway Elements (MIRE) to provide a recommended listing and data dictionary of 

roadway and traffic data elements critical to supporting highway safety management programs. 

MIRE is intended to help support the states’ HSIPs and other safety programs. 

The MIRE Management Information System (MIRE MIS) was a project to explore better means 

of collecting MIRE data elements, using and integrating the data and identifying optimal data file 

structures. The resulting products include reports on the findings from the MIRE MIS Lead 

Agency Program, a MIRE Guidebook on the collection of MIRE, a suggested MIRE data file 

structure report and a report on collection mechanisms and gap analysis that will assist the 

states in conducting a more effective safety program. The intent of the MIRE MIS project is the 

integration of MIRE into States’ safety management processes. 

The Performance Measures for Roadway Inventory Data report is one of the products of the MIRE 

MIS effort. This report builds upon NHTSA’s Model Performance Measures for State Traffic 

Records Systems report and identifies issues to be considered and measures to assess the quality 

of the roadway and traffic data. This report will provide data managers and collectors with 

refined techniques for assessing the quality of the roadway and traffic data inventory data they 

collect and maintain. 

Michael S. Griffith 

Director, Office of Safety Technologies

Monique R. Evans 

Director, Office of Safety Research and 

Development 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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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. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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TECHNICAL DOCUMENTATION PAGE 

1. Report No. 

FHWA-SA-12-036 

2. Government Accession 

No. 

3. Recipient's Catalog No. 

 

4. Title and Subtitle 

Performance Measures for Roadway Inventory Data 

5. Report Date 

February 2013 

6. Performing Organization Code 

7.Author(s) 

Forrest M. Council, Jonathan P. Soika, Nancy X. Lefler, Robert A. Pollack, 

and Carol H. Tan 

8. Performing Organization Report 

No. 

9. Performing Organization Name and Address 

Vanasse Hangen Brustlin, Inc. (VHB) 

8300 Boone Blvd., Suite 700 

Vienna, VA 22182-2626 

10. Work Unit No. 

11. Contract or Grant No. 

DTFH61-05-D-00024 (VHB) 

12. Sponsoring Agency Name and Address 

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE 

Washington, DC 20590 

13. Type of Report and Period 

Final Report, September 2007 – 

February 2013 

14. Sponsoring Agency Code 

FHWA 

15. Supplementary Notes 

The contract managers for this report were Robert Pollack (HSA) and Dr. Carol Tan (HRDS-20). 

16. Abstract 

Performance measures for data systems are tools for helping assess data quality and establishing goals for data 

improvement. This report concerns the development of performance measures for roadway inventory data. Prior 

work by the National Highway Traffic Safety Administration (NHTSA) defined performance measures for timeliness, 

accuracy, completeness, uniformity, integration, and accessibility for each of the six core traffic safety data systems – 

crash, vehicle, driver, roadway, citation/adjudication, and injury surveillance. This report builds on those measures, 

providing a detailed review of each of the measures proposed for roadway data and suggesting modifications of and 

possible additions to that original list. The measurement of data quality using performance measures is of little value 

without follow-up efforts to correct problems identified. This report also provides suggestions for data-related 

business practices that can lead to the successful use of performance measures and to improvements in roadway data. 

17. Key Words: 

performance measures, safety 

inventory data 

data, roadway 

18. Distribution Statement 

No restrictions. 

19. Security 

Unclassified 

Classif. (of this report) 20. Security 

page) 

Unclassified 

Classif. (of this 21. No. of Pages 

60 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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TABLE OF CONTENTS 

EXECUTIVE SUMMARY .................................................................................. VIII

CHAPTER 1: BACKGROUND AND INTRODUCTION ................................ 10

TERMINOLOGY ......................................................................................................... 12

PROJECT TASK OBJECTIVES .................................................................................. 13

PROJECT APPROACH .............................................................................................. 14

DISCUSSION OF MEASURES .................................................................................. 16

GENERAL COMMENTS ............................................................................................ 16

CHAPTER 2: PERFORMANCE MEASURES .................................................... 18

TIMELINESS ................................................................................................................ 21

ACCURACY ................................................................................................................ 28

COMPLETENESS ....................................................................................................... 33

UNIFORMITY .............................................................................................................. 38

INTEGRATION ........................................................................................................... 43

ACCESSIBILITY .......................................................................................................... 47

CHAPTER 3: MEASURE-RELATED BUSINESS PRACTICES ....................... 55

ESTABLISH/IDENTIFY A ROADWAY INVENTORY LEADER .......................... 55

ESTABLISH AN INVENTORY COORDINATING COMMITTEE ........................ 55

ESTABLISH ROADWAY INVENTORY MEASURES ............................................ 56

CALCULATE THE MEASURES REGULARLY ....................................................... 56

CONDUCT PERFORMACE MEASURE RELATED FOLLOW-UP ANALYSES . 56

ESTABLISH PERFORMANCE GOALS .................................................................... 56

DEVELOP A SYSTEM OF INTERNAL QUALITY-CONTROL CHECKS ........... 56

INCREASE INVENTORY DATA USE ...................................................................... 57

DEVELOP HIGH-LEVEL SUPPORT FOR “GETTING IT RIGHT” ...................... 58

SUMMARY ........................................................................................................... 59

REFERENCES....................................................................................................... 60 
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LIST OF TABLES 

Table 1. Summary of NHTSA-recommended performance measures and author-suggested 

additional considerations. ............................................................................................................................ 19

Table 2. Critical data elements suggested in the NHTSA Report. ..................................................... 25

Table 3. Fundamental Data Elements identified by FHWA. ................................................................. 26
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ACRONYMS 

AADT Average Annual Daily Traffic 

CDIP Crash Data Improvement Program 

DOT Department of Transportation 

EMS Emergency Medical Services 

FHWA Federal Highway Administration 

GHSA Governors Highway Safety Association 

GIS Geographic Information System 

HPMS Highway Performance Monitoring System 

HSIP Highway Safety Improvement Program 

HSIS Highway Safety Information System 

IHSDM Interactive Highway Safety Design Model 

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

MIRE Model Inventory of Roadway Elements 

MIS Management Information System 

MMUCC Model Minimum Uniform Crash Criteria 

MPO Metropolitan Planning Organization 

NHS National Highway System 

NHTSA National Highway Traffic Safety Administration 

PRG Preusser Research Group 

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

for Users 

TRCC Traffic Records Coordinating Committee 

VIN Vehicle Identification Number 

WSDOT Washington State Department of Transportation 
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EXECUTIVE SUMMARY 

The last decade has seen a marked increase in the emphasis placed on data-driven decisionmaking in the management of safety programs for State and local agencies. While crash data 

have long been considered an essential component of safety analyses, the value of other 

datasets—including roadway inventory and traffic volume data—has gained greater appreciation 

in recent years. A number of state-of-the-art analytical tools and resources have emerged that 

rely not only on crash data but also on roadway inventory data. For this reason, safety data of 

various types are becoming increasingly important in safety management efforts across the U.S. 

The Federal Highway Administration (FHWA) has recently focused 

more attention on improving roadway inventory data nationwide. In 

2010, FHWA released the Model Inventory of Roadway Elements 

(MIRE) Version 1.0. MIRE is a recommended listing of roadway and 

traffic data elements critical to safety management, and includes 

proposed standardized coding for each element. A critical step 

toward acceptance and implementation of MIRE is the conversion of 

MIRE, which is now a listing of variables, into a Management 

Information System (MIS). The purpose of this report is to develop 

performance metrics to assess and assure MIRE data quality and MIS 

performance. Although the focus of this report is on the MIRE data 

elements, the measures developed can be applied to all types of 

roadway-related data. 

Performance measures are tools that can help to measure data quality and be used by States to 

establish goals for data quality improvement. The Model Performance Measures for State Traffic 

Records Systems—referred to subsequently as “the NHTSA Report”—is a publication released 

by the National Highway Traffic Safety Administration 

(NHTSA) in February 2011. In it, NHTSA defined measures SIX CORE TRAFFIC 

SAFETY DATA SYSTEMS 

 Crash 

 Vehicle 

 Driver 

 Roadway/Traffic 

 Citation/Adjudication 

 EMS/Injury Surveillance 

for six performance attributes that could apply to each of the 

six core traffic safety data systems. 

This report builds upon the roadway data performance 

measures presented in the NHTSA Report, providing a 

detailed review of each and suggesting modifications of and 

possible additions to that original list. These suggestions 

include the following: 

 Reword the current language, where appropriate, to 

more clearly describe the measure by clarifying 

SIX 

PERFORMANCE 

ATTRIBUTES 

 Timeliness 

 Accuracy 

 Completeness 

 Uniformity 

 Integration 

 Accessibility 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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potentially confusing wording; e.g., the word “segment” is used to describe data 

attributes for both road segments and intersections. 

 Use the measures independently for State-system and local-system roads to provide 

more detailed feedback. 

 Consider the adoption of additional measures; e.g., a new measure could be added to 

assess the internal consistency of variables across all inventory files. 

The measurement of data quality is of little value unless corrective action will be taken to 

address identified deficiencies within the data systems. These needs may be met through the 

following suggested data-related business practices: 

 Establish or identify a roadway inventory leader. 

 Establish an Inventory Coordinating Committee. (This could be part of the State’s 

Traffic Records Coordinating Committee (TRCC) or a Data Improvement Committee 

within the State DOT). 

 Establish roadway inventory performance measures. 

 Calculate the measures regularly. 

 Conduct follow-up analyses on the performance measures. 

 Establish performance goals. 

 Develop a system of internal quality-control checks. 

 Increase the use of roadway inventory data. 

 Develop high-level support for “getting it right.” 

Performance measures are tools that can be used to help increase the quality of roadway 

inventory data by examining the data performance quantitatively. In order to maximize the 

impact of performance measures on data quality, a number of questions should be addressed: 

 Which measures are most likely to be useful to a given user agency? 

 How should each measure be specifically defined and applied? 

 What specific performance goals should correspond to each measure? 

 Who will be responsible for measuring each of the performance attributes, and how 

often will this be done (e.g., monthly, annually, sporadically, etc.)? 

 How will the implementation of and the staffing for a problem-correction procedure be 

funded? 
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CHAPTER 1: BACKGROUND AND INTRODUCTION 

The Model Inventory of Roadway Elements (MIRE) is a set 

of roadway inventory variables that the Federal Highway 

Administration (FHWA) has identified as being critical to 

an effective contemporary highway safety management 

program. FHWA developed MIRE through a series of 

tasks, ranging from the initial collection of potential 

elements and element coding from a number of sources 

(e.g., Highway Performance Monitoring System [HPMS], 

SafetyAnalyst, etc.) to workshops and webinar reviews of 

the proposed elements by a national panel of potential 

users. The result of those efforts— MIRE Version 1.0—has 

been documented in a final report and is now available to 

user agencies (1).

The National Highway Traffic Safety Administration 

(NHTSA) released the Model Minimum Uniform Crash Criteria (MMUCC) in 1998 to provide 

guidance on data collection practices at the scenes of motor vehicle crashes. Since then, 

MMUCC has undergone multiple revisions and has become the de-facto standard for crash data 

variables among various State and local jurisdictions. Many agencies across the United States 

have modified or even revamped their crash data systems based largely on the standardized 

data set outlined by MMUCC. MIRE is a companion to MMUCC, and it is envisioned that the 

impact of MIRE on roadway inventory datasets across the country may be similar to that of 

MMUCC on crash data. 

The potential benefits of MIRE on safety management programs nationwide are numerous, but 

its incorporation into the day-to-day safety practices of individual agencies will require 

significant effort. While virtually all State Departments of Transportation (DOTs) and many 

local agencies maintain roadway inventory databases, few of them are currently collecting the 

majority of the data variables within MIRE. Even if collected, agencies must often modify 

computer files, link new elements to other safety files, and incorporate the new elements into 

the computerized or manual systems and tools that are used in the safety decision-making 

process. 

A critical step toward acceptance and implementation of MIRE is the conversion of MIRE 

(which is now a listing of variables) into a management information system (MIS). To assist 

States in developing and integrating the MIRE into an MIS structure that will provide greater 

utility in collecting, maintaining, and using MIRE data, FHWA has undertaken the MIRE MIS 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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project. The proposed MIS design will include the exploration, development, and 

documentation of the following: 

 Mechanisms for data collection. 

 An efficient process for data handling and storage. 

 Details of data file structure. 

 Methods to assure (1) the integration of MIRE data with crash data and other data types, 

and (2) that access to these data can be accomplished through the MIRE MIS. 

 The identification of performance measures to assess and assure MIRE data quality and 

MIS performance. 

The purpose of this report is to address the last objective: to develop performance measures 

to assess and assure MIRE data quality and MIS performance. 

The development of performance measures for safety data became a high priority in 2005 with 

passage of the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for 

Users (SAFETEA-LU). This legislation authorized grants to States for traffic safety record 

system improvements under Sec. 408 of 23 United States Code (known as Section 408 grants). 

The SAFETEA-LU language required States to set goals for their traffic safety record systems, 

to identify “performance-based measures by which progress toward those goals will be 

determined,” and to subsequently demonstrate “measurable progress toward achieving the 

goals and objectives” outlined in the States’ plans. NHTSA 

has since defined six core traffic safety data systems (crash, 

vehicle, driver, roadway, citation/ adjudication, and 

emergency medical services [EMS]/injury surveillance) that 

form the base of traffic safety records and six performance 

attributes (timeliness, accuracy, completeness, uniformity, 

integration, and accessibility) that can be considered when 

assessing the overall quality of each system. The new 

authorizing legislation, Moving Ahead for Progress in the 

21st Century Act (MAP-21), continues traffic safety record 

safety system incentive improvement grants under Section 

405. 

In 2008, NHTSA worked with State and Federal experts 

to establish a model minimum set of performance 

measures for behavioral highway safety programs (2). Later that same year and with the 

assistance of the Governors Highway Safety Association (GHSA), NHTSA initiated a project to 

develop a more finalized minimum set of data quality performance measures for State traffic 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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records systems. That group was to include at least one quality performance measure for each 

attribute for each of the six core traffic safety data systems. The development effort was 

coordinated by the Preusser Research Group (PRG) in collaboration with GHSA, and involved 

input from 25 State and 11 Federal data experts with extensive experience in the collection, 

processing, and utilization of data from each of the core systems. The results were 

documented in a July 2009 report entitled, “Model Performance Measures for State Traffic 

Record Systems White Paper: Request for Comment on Proposed Measures and Guidance” 

(hereinafter referred to as “the White Paper”) (3). The White Paper was widely distributed, 

and public comments were accepted by GHSA until early September 2009. The proposed 

performance measures were modified based on the feedback received, and an interim draft 

report was submitted to NHTSA in December 2009 (4). NHTSA subsequently revised the 

minimum set of data quality performance measures to ensure its usefulness with regard to 

existing traffic records grant programs. Members of the original expert panel reconvened in 

2010 to finalize the measures, and the final report (hereinafter referred to as “the NHTSA 

Report”) was published in February 2011 (5). All documents produced during the evolution of 

the performance measures—the PRG draft, the comments received from potential users, a prepublication draft of the NHTSA report, and the NHTSA Report—were provided to this author 

and have been incorporated into later sections of this 

paper. 

In a separate but related effort, FHWA initiated the 

development of a Crash Data Improvement Program 

(CDIP) Guide in 2008 to provide States with a means to 

measure the quality of the information contained within 

their crash data systems. The final report was published in 

April 2010 (6). While the CDIP Guide suggests crash data 

performance measures for the same six attributes that 

were identified in the NHTSA Report, it provides a much 

greater level of detail on each of the quality attributes, as 

well as real-world examples of measures currently being 

used by various State and local agencies. Much of the 

background discussion of performance measures found 

within this document is based on the CDIP Guide. 

TERMINOLOGY 

It is critical to employ concise language when discussing the performance of traffic records 

systems so that the users are not confused and the focal points are not misrepresented. Listed 

below are a few terms that could be potentially confusing, as well as an explanation of how 

those terms are used in this document. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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 While the NHTSA Report and the CDIP Guide discuss the same areas and performance 

measures, each document uses different wording to do so. The CDIP Guide 

distinguishes performance metrics from performance measures: a metric is what is being 

measured (e.g., the number of days from when a crash occurred until when the crash 

database is updated is a measure of database timeliness); a performance measure is the 

change from the baseline or changes over preceding period (i.e., the improvement over 

a period of time). The NHTSA Report refers to the six areas of interest as performance 

attributes and refers to what is being measured as performance measures. It further notes 

that performance measures are different from performance goals, which refer to 

improvements in the measures over time and which are established by the user. To be 

consistent with the NHTSA Report, this document will use performance attributes

to refer to the six areas of interest (timeliness, accuracy, completeness, etc.). 

 The terms milepost and milepoint are used in linear-referencing methods to denote the 

specific location of a point along a roadway that is identified by a measured distance 

from a boundary—usually where the subject route crosses either a State or county line. 

This document will use milepost and milepoint interchangeably.

 A variety of technical resources are cited in this report, including the White Paper and 

the NHTSA Report, and, similarly, a number of authors are also mentioned. For clarity, 

when a statement is an opinion or comment from the author of this 

document, it will be accompanied by the phrase, “this author.”

PROJECT TASK OBJECTIVES 

The primary objective of this task is to develop a series of performance measures by which an 

agency can assess the quality of data in terms of timeliness, accuracy, uniformity, completeness, 

accessibility, and integration with the components of other traffic safety information 

management systems. 

A secondary objective is to define data governance to help resolve any problems identified 

through the use of the performance measures, as the collection of the data-related 

performance measures without corrective follow-up efforts is of little value. The development 

of a listing of related good governance practices may help to improve the correction process. 

For example, consider a potential data accuracy measure: “the percentage of all road segment 

records with no errors in critical data elements.” If an investigation concludes that the 

percentage of records having no errors is less than 100 percent, then governance practices with 

the potential to affect the data might include (1) the designation of a single data correction 

leader in the agency, and (2) the establishment of regularly scheduled intra-office team reviews 

of the noted issues. As errors are identified, the correction leader would need to determine 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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what data collection and storage practices led to the errors and devise potential solutions to 

address these problems (which may include procuring new data collection technology). 

PROJECT APPROACH 

The initial step for this project was to gather existing information on performance measures by 

investigating (1) if and how States and local agencies currently use roadway data performance 

measures, and (2) what details on performance measures have already been illustrated by 

others in various research undertakings and reports, including the White Paper and NHTSA 

Report. 

A substantial amount of information on how various agencies are presently employing data 

metrics was obtained from Appendix C of the White Paper, which provides a summary of the 

usage of metrics by 49 States and other jurisdictions in their data improvement plans and 

progress reports submitted for Section 408 data projects. This was based on a 2008 report 

from Traffic Safety Analysis Systems & Services, Inc. that summarized and catalogued the 

performance measures used by the States and other jurisdictions (7). The author of the White 

Paper commented on the nature, extent, and appropriateness of the measures, noting a lack of 

structure and standard terminology and that the same or similar measure might appear under 

different categories in other States. 

There were 107 total roadway inventory metrics: 19 for timeliness, 24 for accuracy, 32 for 

completeness, 8 for uniformity, 13 for integration, and 11 for accessibility in Appendix C. As 

mentioned by the White Paper author, many of these were in the wrong category. The 

number of States with at least one roadway data measure for a particular attribute differed 

from attribute to attribute. While 20 States had at least one measure for completeness of 

roadway data, only six States had at least one for uniformity. Accessibility seemed to be the most 

difficult performance measure to define, with some users trying to define it as “easily 

accessible” while others counted the number of users as a measure of accessibility. The 

roadway measure most frequently used across States involved completeness and was related to 

the percentage of road miles included in the database. 

Complete details can be found in Appendix C of the White Paper, but some important findings 

are highlighted below: 

 Timeliness – Thirteen States had at least one measure, but most were not true timeliness 

measures. Of the measures deemed appropriate, three dealt with the time lapse from 

the completion of construction to the updating of the data file, and three used a 

measure previously recommended by NHTSA concerning the amount of time from the 

end of the traffic count cycle to the latest annual average daily traffic (AADT) volumes 

being available in the data file. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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 Accuracy – Sixteen States used at least one measure of accuracy, but many were related 

to completeness or integration. The most common occurrence was a measure of the 

percentage of crashes that could be located (which, as discussed below, is not a true 

measure of roadway accuracy). Three attempted to measure the geographical accuracy 

of the roadway file, and only one had a specific accuracy issue—examining the 

correctness of roadway width data. 

 Completeness – Twenty States had at least one measure, dealing mostly with roadway file 

coverage either as a percentage of public roads, a percentage of State-system roads, or 

the number of county systems with all roads included. 

 Uniformity/Consistency – Only six States had at least one uniformity measure. These 

varied widely, and only one dealt with uniformity directly—the “percent of databases 

using standard nomenclature for roads.” 

 Integration – Twelve States had at least one integration measure. Most appeared to be 

appropriate, dealing with the integration of county and metropolitan planning 

organization (MPO) files with the State file or map and the linkability of the roadway 

data with other traffic records files, including the crash file. 

 Accessibility – Ten States had at least one accessibility measure, with approximately half 

related to the percentage of certain key customers (e.g., county engineers or MPOs) 

using the file or to some count of access by the public. 

The second component of the information-gathering effort involved the review of various 

documents and reports that explored the current usage of roadway performance measures. 

Details were collected during a series of interviews as part of another task in the MIRE MIS 

project. A background statement concerning the definition and examples of roadway measures 

and performance goals was prepared and given to the interviewers, who posed two primary 

questions: 

1. Has your State developed formal inventory-data performance measure? (If so, please provide a 

list.) 

2. Do you use any automated (i.e., computerized) quality-control checks of your inventory data? (If 

so, please provide examples.) 

Interviews were conducted with four State DOTs (Florida, Iowa, North Carolina, and 

Washington) and two local agencies (Winston-Salem, North Carolina and Orlando, Florida). 

Most of those interviewed employed some form of automated quality-control checks for a 

portion of their traffic records files. However, with the exception of the Washington State 

DOT (WSDOT), no positive responses were received concerning roadway performance 

measures. As part of a series of goals for all of its traffic records systems, WSDOT noted the 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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following performance goal related to the completeness of its roadway inventory: Increase the 

percent of statewide public road miles available for use in the Washington Transportation Framework 

database.

In summary, while there are roadway inventory performance measures currently being used (or 

at least used during the preparation of Section 408 grant applications and progress reports), the 

specifics of their application vary greatly across the States. This inconsistency clearly supports 

the need for guidance such as that provided by the recent NHTSA Report and this document. 

DISCUSSION OF MEASURES 

The presentation of performance measures in Chapter 2 is organized by the six performance 

attributes identified above and provides the following details for each: 

 The basic definition of the attribute, with material drawn primarily from the White 

Paper, the NHTSA report, and the CDIP Guide. 

 The measures presented in the final NHTSA Report. 

 A discussion of the performance measures proposed in this document. 

 Possible modifications to proposed methods and additional performance measures for 

consideration. Example uses of the data performance measures in developing 

performance goals. 

Chapter 3 presents a discussion of possible measure-related governance practices that may 

improve data quality. 

GENERAL COMMENTS 

There are a few points that merit discussion before delving into the individual data performance 

attributes: 

 Since roadway inventory files contain an abundance of data elements—the first edition 

of MIRE contains 202 elements (plus eight additional suggested variables)—the effort 

required to establish measures for six different performance attributes for each data 

element would far surpass what is reasonable and effective. The NHTSA Report has 

addressed this issue in two ways: 

o First, the NHTSA Report states that “Use is voluntary: States should use the 

measures for those data system performance attributes they wish to monitor or 

improve. If the suggested measures are not deemed appropriate, States are free 

to modify them or develop their own,”(5).
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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o Second, the NHTSA Report suggests that certain roadway (and other database) 

performance measures only be measured for critical data elements, with the 

individual user agency making the determination of which elements are truly 

critical. (Further discussion of critical data elements in the roadway inventory 

file is provided under the timeliness attribute in Chapter 2.) 

 While the roadway inventory file represents a single data system in the NHTSA Report, 

the roadway inventory dataset in most States actually comprises multiple files having 

multiple owners. In addition to a basic inventory file, there are also existing inventory 

files for highway-rail grade crossings and bridges in all States, as well as supplemental 

inventory files in many States related to focus areas such as asset management, 

pavement management, sign inventory, etc. These multiple sources of roadway 

inventory data within a single agency lead to increased complexity in both the choice of 

roadway performance measures and in the governance practices needed to achieve 

system-wide improvements. When choices of critical data elements and data 

improvement projects are made, consideration must also be given to each individual 

component of the roadway inventory system. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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CHAPTER 2: PERFORMANCE MEASURES 

In this report, the term performance measure is used to represent the specific feature or data 

quality characteristic that is being measured. The performance measures identified here are 

presented according to the six data performance attributes identified by NHTSA:

 Timeliness. 

 Accuracy. 

 Completeness. 

 Uniformity. 

 Integration. 

 Accessibility.

Table 1 summarizes the metrics recommended in the NHTSA Report and additional measures 

suggested by this author. Both are discussed in detail below. The NHTSA Report, the White 

Paper, and the CDIP Guide provide general definitions of the six performance attributes. This 

information is captured under “Definition” in each discussion of the roadway data performance 

attribute. The NHTSA attribute naming convention (e.g., R-T-1) includes reference to the core 

data system (i.e., R = roadway), the attribute (i.e., T = timeliness) and the metric number (i.e., 1 

= first metric), respectively. 

The presentation of performance measures in Chapter 2 is organized by the six performance 

attributes identified above and provides the following details for each: 

 The basic definition of the attribute, with material drawn primarily from the White 

Paper, the NHTSA report, and the CDIP Guide. 

 The measures presented in the final NHTSA Report. 

 The measures proposed in this document. 

 Possible modifications to proposed methods and additional performance measures for 

consideration. 

 Example uses of the data metrics in developing performance goals. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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Table 1. Summary of NHTSA-recommended performance measures and authorsuggested additional considerations. 

NHTSA 

Data 

Performance 

Attributes 

NHTSA Recommended 

Performance Measures 

Suggested Revisions/Actions/

Additions regarding NHTSA 

Performance Measures 

Timeliness R-T-1: The median or mean number of days from 

(a) the date a periodic collection of a critical 

roadway data element is complete (e.g., Average 

Annual Daily Traffic) to (b) the date the updated 

critical roadway data element is entered into the 

database. 

R-T-1: Evaluate the utility of this metric to the 

State. 

R-T-2: The median or mean number of days from 

(a) roadway project completion to (b) the date the 

updated critical data elements are entered into the 

roadway inventory file. 

R-T-2: No explicit suggested action. 

Accuracy R-A-1: The percentage of all road segment 

records with no errors in critical data elements. 

(The State selects one or more roadway data 

elements it considers critical and assesses the 

R-A-1: Define the term “segment” in a broad 

sense to include records related to intersections, 

interchanges, and related inventories. 

Additional Metric 

accuracy of that element or elements in all of the 

roadway records within a period defined by the 

State.) 

R-A-2: Percentage of critical roadway inventory 

elements whose attribute values are within 

reasonable ranges and/or are consistent with 

related variables.

Completeness R-C-1: The percentage of road segment records 

with no missing critical data elements. 

R-C-1: No explicit suggested action. 

R-C-2: The percentage of public road miles or 

jurisdictions identified on the State’s base-map or 

roadway inventory file. 

R-C-3: The percentage of unknowns or blanks in 

critical data elements for which unknown is not an 

acceptable value. 

R-C-4: The percentage of total roadway segments 

that include location coordinates, using 

measurement frames such as a GIS basemap.

R-C-2: No explicit suggested action. 

R-C-3: No explicit suggested action. 

R-C-4: Road segments should also be considered 

as including intersections and interchanges/ramps. 

Additional Metrics: 

R-C-5: The percentage of critical elements actually 

collected in the roadway file layout. 

R-C-6: The percentage of total public road 

mileage that is included in the State’s spatial data 

system.
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

20

Table 1. Summary of NHTSA-recommended performance measures and authorsuggested additional considerations (cont.). 

NHTSA NHTSA Recommended Suggested Revisions/Actions/

Data Performance Measures Additions regarding NHTSA 

Performance Performance Measures 

Attributes 

Uniformity 

R-U-1: The number of Model Inventory of 

Roadway Elements (MIRE)-compliant data 

elements entered into a database or obtained via 

linkage to other databases.

R-U-1: Consider changing “number” to 

“percentage.” 

Additional Metric 

R-U-2: Percentage of critical roadway elements 

all inventory files whose names, definitions, and 

coding are consistent across the target roadway 

system.

in 

Integration 

R-I-1: The percentage of appropriate records in a 

specific file in the roadway database that are linked 

to another system or file. 

(Example – bridge inventory records linkable to 

basic inventory file)

R-I-1: No explicit suggested action. 

Accessibility 

R-X-1: To measure accessibility of a specific file in 

the roadway database: Identify the principal users 

of the file. Query the principal users to assess (a) 

their ability to obtain the data or other services 

requested and (b) their satisfaction with the 

timeliness of the response to their request. 

Document the method of data collection and the 

principal users’ responses

R-X-1: No explicit action suggested action. 

Additional Metric 

R-X-2: Number of users receiving information 

from the inventory file. 

R-X-3: Percentage of requests received for 

information or data from the State roadway 

inventory file that were filled within the State’s 

defined timeline.
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

21

TIMELINESS 

Definition 

Timeliness usually measures the duration of time between the 

event generating the data (e.g., a crash, a vehicle registration, etc.) 

and when those data are placed on file and available for use. For 

data that should be provided to another system, timeliness can 

also measure the amount of time between the occurrences of 

events and when the data are submitted to the other system; e.g., consider citations sent to the 

court administrators, and the subsequent court actions that are sent to the driver history file. 

For roadway data systems, timeliness typically refers to the duration of time between the 

opening of a new or modified roadway section and the placement of the associated data in the 

roadway inventory file. (Such a modification could include the installation of safety-related 

items such as signs, pavement markings, crash cushions, etc.) Timeliness could also refer to the 

time period between the systematic collection or updating of data and their placement within 

the database. A key example of this is the development of AADT estimates for each section of 

highway on an annual basis. 

Differences between Roadway and Crash Data 

There are fundamental differences between roadway data and crash data that must be 

considered when establishing measures for timeliness and other performance attributes. For 

instance, consider the date on which an event occurred—a critical factor in the assessment of 

the timeliness of an agency’s data. If the event in question is a crash, then this information is 

typically entered into the crash database automatically since date is a specific field on the crash 

report. However, if the event of interest is a construction activity (e.g., the modification of an 

existing roadway), then a corresponding date may not be as readily available and, therefore, 

may not be regularly denoted within the roadway inventory database. Although an agency 

sometimes has a database that describes construction activities and maintenance reports that 

describe some safety-related treatments, capturing the actual date of a roadway improvement is 

generally much more arduous than capturing the date of a crash. 

A key first step for the user agency is to decide what reference date is appropriate with regard 

to the roadway modification or the construction of a new facility. Ideally, the reference dates 

would be defined by a construction start date and the end the date on which normal traffic flow 

resumed (though this is not always possible). Next, a procedure should be established to 

record or extract this date for each project or action that will ultimately affect the roadway 

inventory. Timeliness would be defined, then, as the duration of time between the reference 

start date and the date on which the new data are entered into the inventory database. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

22

This author notes several primary issues concerning the definition of timeliness as based on the 

amount of time between the event date and the date on which the database is updated to 

reflect that event. 

 The value and importance of timely updates to the roadway inventory database are often 

overlooked. While it may be widely acknowledged that recent and up-to-date crash data 

may be required for state-of-the-art safety analyses, the critical role of roadway 

inventory data may not be as apparent. Because contemporary safety analyses rely on 

crash records being linked to roadway inventory data (and possibly other data), the 

timely and periodic update of the inventory file is crucial to an effective analysis of the 

most recent crashes. For example, if an agency’s identification of high-crash locations 

incorporates the use of inventory data, then the appropriate data must be available by 

the time such analyses are conducted.

 The location coding in the roadway inventory file should be in sync with that in the crash file.

The location attributes in an agency’s data systems are often coded according to a 

linear-referencing system. This method typically defines the specific location of a point 

along a roadway by incorporating a measured distance from a known boundary or 

landmark (e.g., where the subject route crosses a county line or bridge). Because this 

method is tied directly to the centerline of the subject roadway, any physical alteration 

to that centerline has the potential to render the linear-referencing system inaccurate 

downstream of the alteration. Locations in the crash files are often determined by an 

investigator-measured distance from the crash scene to some known reference point. 

Since the milepost of the reference point is known, the crash milepost can be calculated. 

However, if a reconstruction project lengthens or shortens an existing roadway 

segment (e.g., a curve is lengthened downstream from an intersection that is used as a 

reference point), the after-treatment milepost for a given point along the roadway will 

differ from the before-treatment milepost for that same point. Thus, in trying to 

identify high-crash locations, crashes before and after the reconstruction having the 

same mileposts will not have occurred at the same roadway location. Such potential 

changes in mileposts can be addressed as they occur (e.g., by providing updated 

information on the new roadway to those responsible for determining crash mileposts) 

or at a specified later date (e.g., at the end of the year) if the date of the crash, the date 

of the improvement, and the amount of change in the after-modification mileposts is 

documented. It will be necessary for the user agency to establish a process that keeps 

the crash and inventory mileposts in sync. The critical date for documenting the 

inventory changes will likely be dictated by this process to insure compatibility between 

the location coding of each data system.

 A critical timeframe for the updating of the inventory database may be dictated by practices 

beyond the realm of safety. A roadway inventory dataset may be used for a variety of 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

23

purposes other than safety, and such applications may require that the system be 

updated by a particular date. As an example, the Ohio DOT uses the total route 

mileage information from its inventory to determine the amount of roadway taxes 

returned to localities, and this must be done by a certain date each year. Thus, the 

timeliness target associated with the inventory updates may be driven by uses other 

than safety.

NHTSA Report Measures 

Listed below are the two roadway timeliness measures that were proposed in the NHTSA 

Report. Note that the NHTSA naming convention for measures (e.g., R-T-1) includes 

reference to the core data system (R = roadway), attribute (T = timeliness), and assigned 

performance measure number (1 = first measure), respectively. 

 R-T-1: The median or mean number of days from (a) the date a periodic collection of a 

critical roadway data element is complete (e.g., AADT) to (b) the date the updated 

critical roadway data element is entered into the database. 

 R-T-2: The median or mean number of days from (a) roadway project completion to (b) 

the date the updated critical data elements are entered into the roadway inventory file. 

Each performance measure focuses on a separate type of event. The first (R-T-1) relates to 

data generated on a periodic basis through an ongoing data update procedure. The most 

obvious example of this is the estimation of traffic flow (i.e., AADT) for every section of 

roadway in a State. There could also be other full or partial system inventories conducted 

regularly by the roadway agency (e.g., asset management system updates requiring an inventory 

of roadside hardware, pavement markings, or sign conditions). The second measure (R-T-2) 

relates to modifications to the inventory file that arise from changes to the roadway attributes 

due to new roadway segments being opened or roadway improvements or modifications being 

completed along existing segments. Improvement projects cover a variety of activities, ranging 

from major reconstruction to something as straightforward as the installation of a warning sign. 

These changes occur sporadically throughout a given year. 

Performance measure R-T-2 appears rational and needed, as it considers the duration of time 

between the completion of a construction project and the corresponding update to the 

database. It is associated with system changes that are critical to safety analyses, happen often, 

and are difficult enough to record and computerize to justify a measure. On the other hand, 

the usefulness of R-T-1 is slightly more questionable because it appears to be related to data 

collections that are ongoing and may happen less than annually. Clearly, some nonimprovement-related data are critical to safety decision-making and should be incorporated into 

the inventory file as quickly as possible. Some of these data, such as AADT estimates, are 

required by the HPMS. It would appear that there are few cases where the data are not 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

24

already being collected on a regular basis and entered into the inventory file in a timely fashion. 

While there is a need to record all newly-collected data in the roadway system in a timely 

manner, the question remains of whether a “standing” measure that is not used very often is 

needed to assess the quality of the data. If a roadway agency regularly collects nonimprovement inventory data and there is no existing requirement (such as HPMS reporting) as 

to a timely incorporation of the data, then this measure would be useful. 

Under proposed performance measure R-T-2, a State or other user agency would first need to 

define the terms roadway project and completion. From the safety analysis perspective, this 

author suggests that completion is best defined as the date a roadway or roadway segment was 

opened to traffic. Roadway projects, meanwhile, should include both those explicitly designated 

as safety projects (e.g., treatments at high-crash locations) and those that change one or more 

variables related to safety. For example, non-safety projects that address capacity may add 

lanes, modify lane and shoulder widths, add or modify medians, change speed limits, change 

curvature, upgrade intersection traffic control, etc. These changes are known to be safetyrelated, and thus data from such projects should be monitored. In many State roadway files, 

this type of data change is already being monitored and entered for State-maintained roads. 

The biggest question for States will be how best to expand the roadway dataset to also include 

non-State-maintained roadways. Comments on the original White Paper included suggestions 

that it may be necessary to have separate performance measures for State-system roads and 

non-system roads. This would appear to be a helpful proposition, since the definition of both 

projects and critical elements might differ between the two systems (especially if the safety 

analyses corresponding to each system differ). 

Both of the proposed timeliness measures (as well as those related to accuracy and 

completeness that are discussed later) refer to critical inventory data elements. The NHTSA 

Report provides some guidance on the choice of these critical elements. It first notes that each 

State can define its own set of critical elements unless the measure includes a specific reference 

to elements in a national standard (e.g. HPMS). The discussion of timeliness, the NHTSA 

Report suggests that the short list of critical elements should be a subset of MIRE and could be 

some or all of the elements required for HPMS sites. (This would appear to mean the longer 

list of HPMS variables collected on sample sites [known as the Full Extent data] rather than the 

much shorter list of attributes collected on every segment or roadway.) MAP-21 specifies that 

a subset of MIRE be established and these elements be collected for all public roads. As noted 

above, the MIRE system currently defines 202 elements and eight additional files of suggested 

inventory elements. MIRE provides information on whether a variable is critical or value-added, 

but, even here, most of the variables are considered to be critical. While certainly possible and 

even desirable, analyzing this many variables each year would require a major effort; thus the 

importance of user-chosen critical elements. Interestingly, there is a crash-related uniformity 

measure in the NHTSA Report related to the number of MMUCC-compliant elements in the 

crash system. (It is noted that when all sub-elements in MMUCC are counted, the total 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

25

number is similar to the number of MIRE variables.) This difference in the use of the two 

models likely reflects the age of both, with MMUCC being older and more entrenched among 

practitioners, so much so that it has become a (non-mandatory) national guideline for crash 

datasets. 

The MIRE critical variables can be considered as a starting point for a user agency looking to 

define its own critical elements. MIRE also provides information concerning which variables are 

needed in the use of the Highway Safety Manual tools, the Interactive Highway Safety Design 

Model (IHSDM), and SafetyAnalyst. For States adopting the use of such tools, this information 

should also be helpful in the identification of critical variables. Table 2 lists the critical data 

elements that were suggested in the NHTSA Report. 

Table 2. Critical data elements suggested in the NHTSA Report. 

Critical Roadway Inventory Data Elements 

 Roadway Owner  Intersections 

 Route  Divided/Undivided 

 Surface/Pavement  Horizontal Alignment Restriction 

 Median  Average Annual Daily Traffic 

 Turning Lanes  National Highway System 

 Traffic Lanes  Vertical Alignment 

 Functional Classification  Pavement Serviceability Index 

 Shoulder  Linear Reference System 

 Access Control  Mileage 

 County  International Roughness Index 

On August 1, 2011, the FHWA Office of Safety issued a Guidance Memorandum that 

introduced a subset of MIRE elements referred to as the “Fundamental Data Elements” (FDEs). 

This list of 38 roadway and traffic data variables were deemed beneficial for use in the 

development and implementation of a State’s safety programs (8). As shown in Table 3, the 

suggested list of data elements was broken down into three primary categories: roadway 

segments, intersections, and interchanges/ramps. The memo provided the rationale for the list, 

along with guidance on collection priorities and possible sources of assistance. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

26

Table 3. Fundamental Data Elements identified by FHWA. 

Roadway Segment Intersection Ramp/Interchange 





































Segment ID* 

Route Name* 

Alternate Route Name* 

Route Type* 

Area Type* 

Date Opened to Traffic 

Start Location* 

End Location* 

Segment Length* 

Segment Direction 

Roadway Class* 

Median Type 

Access Control* 

Two-Way vs. One-Way 

Operation* 

Number of Through 

Lanes* 

Interchange Influence 

Area on Mainline 

Freeway 

AADT* 

AADT Year* 

























Intersection ID 

Location 

Intersection Type 

Date Opened to Traffic 

Traffic Control Type 

Major Road AADT 

Major Road AADT Year 

Minor Road AADT 

Minor Road AADT Year 

Intersection Leg ID 

Leg Type 

Leg Segment ID 

















Ramp ID* 

Date Opened to Traffic 

Start Location 

Ramp Type 

Ramp/Interchange 

Configuration 

Ramp Length 

Ramp AADT* 

Ramp AADT Year 

*HPMS Full Extent elements are required on all Federal-aid highways and ramps located within grade-separated 

interchanges, i.e., National Highway System (NHS) and all functional systems excluding rural minor collectors and 

locals. 

MAP-21 required FHWA to identify a subset of MIRE elements that are useful for the inventory 

of roadway safety. The FHWA revised the FDEs and issued guidance. Changes between the 

original guidance memo and the revised FDEs can be found at the following link: 

http://www.fhwa.dot.gov/map21/qandas/qassds.cfm. 

Suggested Modifications and Additions 

The NHTSA Report proposed two measures for timeliness, R-T-1 and R-T-2. While no 

revisions to R-T-2 are suggested here, it is recommended that the real value of R-T-1 be 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

27

evaluated. As discussed previously, R-T-1 pertains to data that are collected on an ongoing and 

regular basis, with AADT volumes being perhaps the most obvious example. Since the annual 

collection of AADT volumes and their timely delivery are required by HPMS, a “standing” 

measure in this instance may be duplicative and may not be useful to the State. 

Example Use in a Performance Goal 

Recall that R-T-2 has been defined as the median or mean number of days from (a) roadway project 

completion to (b) the date the updated critical data elements are entered into the roadway inventory 

file.

Assume that a State wishes to incorporate R-T-2 into a performance goal. Data are entered 

into the inventory database at the DOT Central Office when an as-built plan is received. 

Assume further that no date-of-entry is recorded in the inventory file for each variable. Based 

on this scenario, the following steps appear to be necessary: 

1. Determine which safety-related roadway projects are target projects. The State must 

decide to which projects it would like to apply the measure. These could include 

Highway Safety Improvement Program (HSIP) projects or any that are related to 

changes in safety elements, with the latter being preferred. The choice may be as simple 

as using all projects for which an as-built plan is received. 

2. Determine the critical data elements. The State identifies which elements are most 

critical in terms of the timeliness of the data. It is important that this list include all 

critical elements, even if they are not on the as-built plan and must come from another 

source (e.g., AADT). 

3. Develop a supplemental file of date of data entry for each target project. Assuming no 

date-of-entry is recorded in the State’s inventory file, a supplemental file must be 

established that identifies each project by a unique reference number that can be related 

to the as-built plan and in which the date of data entry is recorded. The date of data 

entry should be the date on which the last critical element was entered. For example, if 

AADT is from another source and is obtained a week after all other elements are 

entered, the date of entry for this project would be the date on which the AADT data 

were entered in the file. 

4. Determine the open-to-traffic date. For each project, the date on which the project 

was completed and opened to traffic must be determined. This information may have 

to be obtained from district staff or regional DOT offices. 

5. Calculate the measure. At some time chosen by the State (e.g., at the end of the year), 

the number of days between the open-to-traffic date and the data entry date must be 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

28

calculated for each project. The State will decide whether to use median or mean 

number of days. Median will be less influenced by a few outliers with very long periods, 

but since these may be the projects of greatest interest (due to the longest delay), the 

mean number of days may be more appropriate. 

6. Specify the performance goal. The initial calculation will provide a baseline for use in 

developing the improvement goal. Assume in this case that the mean number of days 

for year one is 45. The State can then decide whether the goal is to be percentagebased (e.g., reduce the mean days to entry by ten percent each year) or time-based (e.g., 

reduce the mean days to entry by five days each year). If more desirable, the State 

could establish a goal of percent of acceptable days-to-entry (e.g., a goal of 90 percent of 

days-to-entry being 30 days or less). The same data and procedure would be followed 

down to Step 5, the actual calculation of the measure. 

7. Recalculate the measure each year. The same procedure would be followed each year 

to allow the State to chart its progress in reaching its goal. As is discussed below under 

Governance Practices, it will be important to further analyze the data to determine the 

cause of significant delays. In this case, the State would scrutinize those projects 

characterized by very long delays and attempt to determine why the delays occurred 

(e.g., a holdup in the delivery of the as-built plan). 

ACCURACY 

Definition 

Accuracy is defined as whether or not the information that is entered 

into the roadway inventory database is, in fact, a valid representation of 

what is found in the actual roadway environment. Accuracy has 

aspects both internal and external to the data file. 

Internal accuracy considers (1) the validity of the data (i.e., the codes entered are legitimate for 

that particular data element), (2) internal checks of consistency with other data elements, and 

(3) whether there are duplicate records for the same event (e.g., duplicate roadway sections). 

For some safety data elements, internal accuracy related to the legitimacy of the code can be 

tested by matching it with external sources (e.g., match a Vehicle Identification Number (VIN) 

with a national VIN file, match location coding with a statewide location database, etc.). 

Continuing with these examples, an indication that a VIN or roadway location within the 

roadway system has been matched through an external source does not necessarily mean that 

the VIN is accurate for the specific vehicle in the crash or that the roadway location noted is 

the exact location at which the crash occurred. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

29

Internal accuracy checks can be computerized and thus automated. While checks for duplicate 

roadway segments may be simple, significant effort will be required to both identify other 

possible checks and to computerize them. These generally differ for each data element in the 

file. Such checks can include data type (e.g., character or numeric), data range (e.g., shoulder 

width between zero and 12 feet), and consistency with other variables (e.g., shoulder width with 

curb presence; median width with divided/undivided; or ending milepoint with beginning milepoint of 

the next roadway segment). 

External accuracy focuses on whether the data are coded properly as determined by external 

sources or audits. As noted in both the CDIP Guide and the White Paper, external validity will 

require a special study or audit to answer questions such as, “was the crash location correct?” and 

“is the reported shoulder width for this roadway segment the same as at the field location?” For the 

roadway data, this will require some systematic review of data in the file versus field 

measurements or accurate as-built plans. Since this would not be possible for each variable in 

the entire file each year, it must be done on a sampling basis. 

NHTSA Report Measures 

The following measure and explanatory note is proposed in the NHTSA Report for roadway 

data accuracy: 

 R-A-1: The percentage of all road segment records with no errors in critical data 

elements. 

o The State selects one or more roadway data elements it considers critical and 

assesses the accuracy of that element or elements in all of the roadway records 

within a period defined by the State. 

Discussion 

While the details are not specified, R-A-1 is an excellent measure of external accuracy—are the 

critical data correct as compared to “ground truth”? The topic of critical variables was discussed 

earlier under timeliness. For a roadway file, it would appear that three sources of ground truth 

information are available: 

 As-built plans – If the user agency retains as-built plans, and if there have been no known 

modifications to a roadway section, then the information on the plans could be 

compared to the computerized data for the critical variables. 

 Photographic imagery – It may also be possible to compare some critical variable data to 

information captured from videologs or aerial photographs. The data would have to be 

coded from these sources. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

30

 Field surveys – If no other sources are available, field reviews of critical elements on a 

sample of locations could be conducted. Here, actual measurements would be taken – 

not as precise as survey data, but precise enough for comparison. 

Because of the complexity and cost of such audits, it would be expected that ground truth data 

would be extracted or collected for a sample of roadway sections. This could be done based 

on roadway functional class (which would assure the inclusion of lower class roads, where data 

might be more questionable) within each highway district (which would include various 

terrains). Again, the user agency will need to design a plan for this validation effort. 

Suggested Modifications/Additions 

First, it is noted that R-A-1 refers to “road segment records.” This may be confusing since 

MIRE and other documents use the term “segments” to only include longitudinal sections of 

roadway. Thus, segment record here should be broadly defined and should include records 

related to intersections, interchanges/ramps, and related inventories of traffic signals, guardrail 

inventories, etc. 

The proposed measure is related to external accuracy—file data versus ground truth. It is also 

possible to develop one or more measures of internal accuracy. An example might be the 

percent of critical roadway inventory elements whose attribute values are within reason-able ranges 

and/or are consistent with related variables. This would require the agency to determine for each 

pertinent critical element the reasonable range (e.g., lane widths of eight to 13 feet, shoulder 

widths of zero to 12 feet, etc.) and whether there are related variables and, if so, what the 

logical relationship should be (e.g., if a curb is present, shoulder width would normally be zero; 

if median type is undivided, then median width should be zero or not applicable). This would 

lead to a set of computerized checks, which should be considered a desirable business practice. 

Example Use in a Performance Goal 

Recall that R-A-1 has been defined as the percentage of all road segment records with no errors in 

critical data elements. Assume a State wishes to incorporate R-A-1 into a performance goal. As 

noted above, this measure compares file entries to ground truth. A plan could be developed to 

assess the “ground truth” validity of all the agency’s roadways over some determined period of 

time (e.g. 20 years- 5% of the system per year). 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

31

While the measure says “all segment records,” the collection of ground truth data will usually 

be expensive. Thus, it is likely that only a sample of segments will be analyzed. The following 

steps appear to be necessary: 

1. Determine critical data elements. The State decides which elements are most critical. 

(See the discussion under timeliness for general guidance.) 

2. Determine a sampling plan. The objective here is to develop a measure that will 

characterize the accuracy of the full inventory file for the chosen elements. One idea 

would be to use a subset of those sample sections already chosen for HPMS, which 

would only be appropriate if the data on non-HPMS segments were felt to be equally 

accurate. The following should be considered in choosing the sample to be used: 

a. Since the quality of newly-entered data is likely to be high (e.g., the projects used 

in the timeliness example), it would be better to use roadway segments that are 

not new to the file. 

b. The State may be aware of variations in accuracy based on terrain, functional 

class, agency district, etc. If so, these different representative locations should be 

included in the sample. 

c. Roadway segments in inventory files are often homogeneous sections where the 

characteristics do not change, and these are usually short in length. Thus the 

collection of ground truth data in one-mile sections, for example, will include 

multiple homogeneous segments and thus multiple comparisons. 

d. In order to track this over multiple years, very similar segments should be 

chosen each year (e.g., select the adjacent mile or intersection as was selected 

the previous year). 

Perhaps the most important guidance is to choose a sample that can be repeated in 

future years, where costs are not prohibitive, and for which staff time will be available. 

The final sampling plan is likely to be based on what is learned from the first year’s 

effort, i.e., how much can be sampled reasonably. 

For the purposes of this discussion, assume that the State has decided to capture one 

mile of data in each of the seven rural functional classes in three of their eight 

districts—one having level terrain, one rolling terrain, and one mountainous terrain. 

The one-mile sections will be chosen by placing a pin at the center of each district and 

choosing the one mile of highway in each functional class that is nearest that pin. 

(Other procedures could be used, including choosing HPMS sample sections if the 

selection criteria above are met). This will produce a sample of 21 miles in the State. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

32

3. Determine sources of ground truth data. For each of the critical elements, the State 

will need to define the method of ground truth data collection. Since it will not be cost 

efficient to conduct surveys, alternatives would include scaling off measurements or 

extracting other data from aerial photographs (e.g., lane width, shoulder width, number 

of intersection legs, intersection skew, number of driveways by type, degree of curve, 

etc.) and from videologs (e.g., number of lanes, shoulder type, no passing zones, etc.). 

AADT data could be assembled from traffic count files and checked for logic with 

AADTs on adjacent segments. 

4. Collect ground truth data. Using the sources found, ground truth data for each sample 

section will be collected. Care must be taken in assuring that the roadway being 

sampled is the same as the one in the file. There are times when the address variables 

describing a file section (e.g., route/milepost) may be in error. If so, all the comparisons 

will indicate error. Distance to nearby boundaries, intersections, or physical mile 

markers can often be checked either in the field or on videologs. 

5. Calculate the measure. A supplemental file should be developed so that for each 

roadway segment in the inventory file, each critical variable has two measures: the 

inventory file measure and the ground truth measure. Differences between the two will 

then be calculated for each critical element. The State will decide on an appropriate 

acceptance level (e.g., lane width within 0.5 feet) and will code each element in each 

segment as either correct or incorrect. Using the measure as written, each segment 

will then be defined as correct if all elements are correct and incorrect otherwise, and 

the percent of the total number of segments tested that are correct will be calculated. 

(Note that an alternative measure would be the percent of all elements in the file that 

are incorrect). 

6. Specify performance goal. The initial calculation will provide a baseline for use in the 

goal. Assume in this case that the percentage of correct records for year one is 60 

percent. The State could then specify that the goal is to be percentage-based (e.g., 

increase the percent of correct records by five percent increments each year) up to a 

final goal (e.g., all critical variables are accurate in 90 percent of tested records). 

7. Recalculate measure each year. The same procedure would be followed each year to 

allow the State to chart its progress in reaching its goal. 

Note that while step-by-step guidance for the incorporation of the suggested internal accuracy 

measure (i.e., the percent of critical roadway inventory elements whose attribute values are 

within reasonable ranges or are consistent with related variables) is not presented here, 

examples of both ranges and pairs of related variables were presented earlier. Additional 

examples of inventory variables for which reasonable ranges could be established include route 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

33

number, county and city codes, number of through lanes, median width, percent combination 

trucks, percent motorcycles, and speed limit. Examples of related variables for which logical 

relationships could be defined would include median barrier presence and median type, inner 

(paved) shoulder width and median type, and AADT ranges for the number of through lanes 

and functional class combinations. 

COMPLETENESS 

Definition 

Completeness is defined as having all required data elements 

(ideally accurate data) within the roadway inventory file for all 

required roads. As noted in the CDIP Guide and the NHTSA 

Report, completeness also has internal and external aspects. 

Internal completeness considers whether all variables are recorded for each event or element 

(i.e., there are no missing or unknown data). External completeness looks at whether the data 

file contains all applicable events, sometimes tested by matching files or records between 

databases (e.g., matching injury crashes with EMS run reports).

For roadway inventory files, it appears that completeness has at least three major components: 

 Whether the existing inventory file includes records for all of the roadways targeted for analysis. 

If the decision is made that all public roads are to be covered by various analyses, the 

completeness measure would be the percentage of targeted roads for which the 

inventory data are available. (While it may be a challenge for many agencies, it would 

not be impossible to earn high marks under this measure. For instance, Maine DOT 

includes all public roads in its inventory files.) If only State-system roads are needed in 

the analyses, then the measure would be related to whether newly-constructed State 

highways are entered into an existing database. 

 An external measure of whether all critical roadway inventory elements are included in the file 

layout. As noted above, critical variables can be defined by the user with guidance from 

MIRE, HPMS, and other data sources. 

 An internal measure of how often coding for a specific data element field is missing. The 

“missing data” concern is related to the issue of how to deal with “null” codes in the 

database, which includes entries such as “not available,” “unknown,” or “not recorded.” 

While typically less of a problem for the roadway inventory database than for other 

databases (e.g., crash or injury), null codes remain an issue that must be addressed. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

34

A null code is a legitimate response when the collectors of the information are unable to 

determine the specific value for a particular data element. However, it does not provide 

information that is usable by researchers. Perhaps the best approach to the issue of null codes 

is to monitor their frequency of occurrence within the individual elements in the database. 

When there is a high frequency of null codes, the State should investigate the elements in 

question to determine why the collectors of the data were unable to measure and or report on 

the actual values for the data elements. Based on the findings, it may be desirable to 

restructure the data element or provide additional training to the collectors to better enable 

them to report useful data. 

NHTSA Report Measures 

Listed below are the four measures for roadway completeness that were proposed in the 

NHTSA Report. 

 R-C-1: The percentage of road segment records with no missing critical data elements. 

o The State selects one or more roadway elements it considers critical and 

assesses internal completeness by dividing the number of records that are not 

missing a critical element by the total number of roadway records in the 

database. 

 R-C-2: The percentage of public road miles or jurisdictions identified on the State’s 

basemap or roadway inventory file. 

o A jurisdiction may be defined by the limits of a State, county, parish, township, 

MPO, or municipality. 

 R-C-3: The percentage of unknowns or blanks in critical data elements for which 

“unknown” is not an acceptable value. 

o This measure should be used when States wish to track improvements on 

specific critical data elements and reduce the occurrence of illegitimate null 

values. 

 R-C-4: The percentage of total roadway segments that include location coordinates 

incorporating measurement frames, such as GIS basemap. 

o This is a measure of the overall completeness. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

35

Discussion 

Defined as the percentage of public road miles or jurisdictions identified on the State’s basemap or 

roadway inventory file, R-C-2 involves an external measure of whether all pertinent miles are 

included in the file. It is the opinion of this author that this is a necessary metric, and the use of 

public road miles would be both better and easier than the use of jurisdictions. Total public 

road mileage is usually readily available, whereas a count of total possible target jurisdictions 

may be difficult to develop. In addition, the jurisdiction-based measure would lend equal weight 

to every jurisdiction, meaning that a very small jurisdiction could have the same effect on the 

measure as a much larger jurisdiction (e.g., a large city or even the full State highway system). 

Additionally, as written, the measure includes reference to both a basemap and the roadway 

inventory file. It should be noted that the two could differ, as there could be a basemap 

containing certain roads (e.g., a spatial data map with some roads digitized) and an inventory file 

that may contain more, less, or the same attribute data as the basemap for all segments. 

Jurisdictions should be cognizant of the roads contained in the basemap and the critical 

elements for which information is being collected. R-C-3 is an internal measure of whether 

coding for the variables included is complete. Defined as the percentage of illegitimate null values 

(i.e., blank or coded with “unknown” or another null code), R-C-3 appears to be both logical and 

understandable. The NHTSA Report note concerning “...for which unknown is not an 

acceptable value...” refers to possible cases where the variable is not pertinent for some 

specific set of roadway locations. While no examples were provided, the assumption is that the 

committee providing input to the NHTSA Report identified some examples of this scenario. 

This measure would be calculated for each element. It is also noted that while this variable 

refers to only critical elements, the State might wish to include all inventory variables in this 

analysis since it is relatively easy to calculate the percent blank or null for any variable in a 

computerized file. 

R-C-4 appears to be a measure of a State’s 

progress in moving to a spatial data (GIS) platform. 

Since no additional clarification is provided, it is 

not clear how it differs from the part of R-C-2 

that refers to “the percentage of public road miles 

identified on the State’s basemap,” unless 

“basemap” there does not refer to a GIS map. RC-4 refers to “percent of total roadway 

segments.” Again, no definition of segment is 

provided, and it is not clear how a user would 

count the total number of possible segments (i.e., 

the required denominator) if they are not already 

mapped. A count of total public road mileage is 

FHWA’s Highway Safety Information 

System (HSIS) has developed a 

computerized check of the percent of 

null records (i.e., uncoded (blank), “not 

stated” or “unknown”) within each 

element of every file (i.e., crash, 

roadway inventory, intersection, etc.). 

This check is conducted each year when 

new files are received from the 

participating DOTs. The output 

provides the name of the file, the name 

of the variable/element and the percent 

blank for every element with five 

percent or more null records. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

36

more likely to be available (this information is reported annually in FHWA’s Highway Statistics 

Report). It is also noted that the measure refers only to the inclusion of coordinates and not 

to the inclusion of other attributes. Finally, since the measure does not refer specifically to a 

State’s spatial data system, it could be interpreted as requiring a count of all segments (or miles) 

that are in both the State’s spatial data system and all spatial data systems created by local 

agencies. Thus, without further clarification, it is difficult to know the intent of this measure or 

how it differs from R-C-2. 

Suggested Modifications/Additions 

In summary, revisions to the above have been recommended: 

 R-C-1 (original): The percentage of road segment records with no missing critical data 

elements. 

No revisions to R-C-1 have been proposed. 

 R-C-2 (original): The percentage of public road miles or jurisdictions identified on the 

State’s basemap or roadway inventory file. 

No revisions to R-C-2 have been proposed. 

 R-C-3 (original): The percentage of unknowns or blanks in critical data elements for which 

“unknown” is not an acceptable value. 

No revisions to R-C-3 have been proposed. 

 R-C-4 (original): The percentage of total roadway segments that include location 

coordinates incorporating measurement frames, such as a GIS basemap. 

R-C-4 (revised): Under R-C-4 (original) roadway segments should also be considered to 

include intersections and interchanges/ramps 

R-C-1 appears to be related to the external measure concerning whether all critical variables 

are included in the file layout for the existing file. R-C-1 is seemingly element-based and 

focuses on whether all critical elements are present in the file layout and, therefore, supposed 

to be coded. (Note that this measure apparently does not relate to what percentage of 

records has legitimate codes, since this is covered in R-C-3.) Because the measure is related to 

the number of segments, it concerns only those roadways that are currently in the inventory 

file; thus, if a database covers only State-maintained roadways, then local roadway systems will 

be disregarded by the measure, since a count of segments would not exist for those roadways. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

37

An additional measure which could address whether or not critical elements are included in the 

roadway file layout could be written as follows: 

 R-C-5: The percentage of critical elements included in the roadway file. 

o The State selects a set of roadway elements it considers essential and assesses 

completeness by dividing the number of critical elements in the file layout by the 

total number of critical elements the jurisdiction considers to be necessary. 

If the intent of R-C-2 is to measure how much of the total public road mileage is included in the 

State’s GIS mapping, an additional performance measure to consider might be written as 

follows: 

WSDOT has a metric-based  R-C-6: The percentage of total public performance goal related to the road mileage that is included in the completeness of its database: State’s geospatial data system.  Increase the percent of statewide 

o “Included” here refers to the public road miles available for 

presence of usable location use in the Washington 

coordinates and not to the Transportation Framework 

presence of other attributes database. 

for each mapped segment. 

The FHWA Offices of Highway Policy Information and Planning, Environment, and Realty jointly 

released a Guidance Memorandum in August 2012 announcing that the HPMS Field Manual had 

been revised to require that all public roads be covered by each State’s linear referencing 

system network. This was an expansion of the previous requirement that only roads eligible 

for Federal Aid had to be included in the LRS. It is expected that each State will be compliant 

with this latest requirement by the time it submits its 2013 HPMS data, which will be due in 

June 2014 (9). 

Example Use in a Performance Goal 

The calculations of performance measures for R-C-1through 6 are relatively straightforward, so 

individual steps will not be presented here. 

The two general performance measures for completeness deal with 1) the inclusion of critical 

data elements which are populated with correct values into the State’s roadway inventory 

database, and 2) the completeness of the basemap used to uniquely identify the geospatial 

locations of roadway segments, intersections and interchanges/ramps onto which the critical 

data elements are overlaid. Therefore, the two general forms that the performance goals for 

the measures identified in the Completeness Section may take are as follows: 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

38

1. A general targeted annual percentage increase in the number of critical elements 

included within the file layout; e.g., increase the number of captured critical elements by 

ten percent each year. 

2. A series of specific percentage targets over multiple years; e.g., if 50 percent of the 

critical elements is included in the dataset in the baseline year, then the goal could be 70 

percent in year two, 85 percent in year three, and 100 percent in year four. 

3. A general targeted annual increase in the number of roadway locations included in the 

basemap onto which the data elements will be overlaid; e.g. increase the number of 

locations identifiable in the basemap by ten percent each year. 

4. Similar to performance goals in item 2, a series of specific percentage increases in the 

inclusion of roadway locations into the basemap over multiple years, would constitute 

performance goals for the inclusion of roadway locations to the basemap. The ultimate 

performance goal would, of course, be the inclusion of all road segments, intersections 

and ramps in the State’s basemap. 

UNIFORMITY 

Definition 

Uniformity is defined as the degree to which the data elements that 

are collected conform to standard or common definitions and 

attributes, and to which the definitions and attributes are the same 

across all geographic areas and across files developed at different 

points in time. 

As noted in the CDIP Guide and the NHTSA Report, uniformity has both external and internal 

aspects. Externally, data collection procedures and data elements should agree with nationally 

accepted guidelines and standards (e.g., crash elements agreeing with elements in MMUCC). 

For roadway inventory files, this might imply that the data, or at least the critical elements, are 

consistent with MIRE definitions and attributes for those elements. Uniformity with MIRE could 

have two aspects: (1) the percentage of MIRE elements that are included in the State file (which 

might also be considered a completeness measure), and (2) for MIRE elements that are present 

in the State file, their level of consistency with comparable MIRE elements in terms of variable 

names, definitions, and coding. 

There would appear to be two internal uniformity aspects for roadway data, one related to 

consistency across space, and the other related to consistency across time. With respect to 

consistency across space, all jurisdictions within a State should collect and report the same data 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

39

using the same definitions and attributes. Most State-system roadway inventory files are 

uniform across the entire State. However, if local inventory files are incorporated into a State 

inventory system, uniformity of elements and element-coding could be a significant issue. 

There would also appear to be two aspects of uniformity for roadway inventory files with 

respect to time: (1) the consistency in coding of a given element across years (due to either 

official changes or unofficial changes introduced by different data collectors), and (2) the 

consistency in coding of a given location across years (i.e., whether the same point on the 

roadway has the same address [e.g., milepost] over a multiyear period). Both are important 

because many safety analyses look at data from multiple years. The concerns over tracking a 

particular location over multiple years arise in a linear-referencing system when a section is 

lengthened or shortened through reconstruction activities, which affects all mileposts 

downstream of the change to the roadway. 

NHTSA Report Measure 

The following measure is proposed in the NHTSA Report for roadway uniformity: 

 R-U-1: The number of MIRE-compliant data elements entered into a database or 

obtained via linkage to other databases. 

Discussion 

This lone measure relates to external uniformity. If the goal is to provide information 

concerning the degree to which MIRE elements are included, this author would suggest the use 

of “percent” rather than “number.” Previous NHTSA documents defined this measure in terms 

of number or percent of MIRE elements comprised in the State file and included a supplemental 

note indicating that States could also consider comparisons with MIRE code values and 

attributes. This final measure does not clarify the term “MIRE-compliant,” leaving it up to the 

user to decide whether uniformity only involves the presence of an element or also involves 

comparison with MIRE definitions and coding. 

Suggested Modifications/Additions 

It is desired that all States move toward uniformity with the MIRE elements, and R-U-1 is a 

sound measure of that shift. Alternative external measures related to MIRE might include 

individual measures for each of the three categories of descriptors used in MIRE (i.e., segment, 

alignment, and junction descriptors) or for selected subcategories within each major category 

(e.g., segment cross section, segment traffic flow data, etc.). This author would suggest that the 

two most important components of MIRE not currently collected by State DOTs are 

junction/intersection files and horizontal curvature files. Both types of variables are critical to the 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

40

analyses of two of the more important crash types—intersection-related crashes and roadway 

departure crashes. Measures could be established for the percentage of MIRE intersection or 

MIRE horizontal curvature elements collected and entered into the State roadway inventory 

file. 

Since R-U-1 does not address what has been described above as internal uniformity, it is 

suggested that the following measure be added: 

 R-U-2: Percent of critical roadway elements in all inventory files whose names, 

definitions, and coding are uniform across the target roadway system (care should be 

taken in reporting to assure uniformity between jurisdictions). 

This performance measure has two primary focus areas: (1) uniformity between State-system 

data elements that come from various offices of the State DOT (e.g., various District, Division, 

or Regional offices), and (2) uniformity between data elements from the State system and 

various local roadway systems. Although achieving consistency may require a significant level of 

effort for the recoding of data variables, maximizing the reach and effectiveness of safety 

analyses will require consistency in variable naming and coding for data from all contributing 

datasets, including the base inventory file, railroad grade crossing file, the bridge index, asset 

management files, etc. 

A rigorous before-after evaluation of the safety effectiveness of a given countermeasure 

typically requires several years of data spanning the time before and after the installation of the 

treatment of interest. To monitor the quality of the data required for these multiyear analyses, 

the uniformity measures must place an emphasis on the two main time-related concerns noted 

above: 

 Uniformity in inventory data coding across time. The first goal is to define methods that 

help to identify possible coding changes from one year to the next and then to recode 

the file such that multiyear analyses can be done; that is, one must ensure that codes 

across the entire time period of interest mean the same thing. 

 Uniformity in location coding across time. The second goal is to insure that analyses aimed 

at examining the same locations over multiple years are indeed doing so. Has the 

specific address (e.g., milepost) of a point along a roadway changed, perhaps due to 

realignment of the roadway? Has a referencing system been modified or replaced 

during the study period? If so, is there a method to trace the same roadway location 

across the multiple referencing systems and all years of interest? 

Rather than define measures, each of these goals is discussed further as a “business practice” in 

Chapter 3. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

41

Highway Safety Information System (HSIS) Data Consistency Check 

For each variable in each data file it receives each year, HSIS runs a computerized 

check for consistency between variable codes in the new data versus data from the 

previous year. The check compares the column percentages for each code in each 

variable and prints out those for which the difference exceeds certain limits. For 

example, as shown in Lines 21-25 below, there were large differences in each 

functional class category between 2006 and 2007 data. (See the final two columns for 

percentages.) These differences resulted from a large percentage increase in the 

number of uncoded records (i.e., blank codes, as shown in Line 21). Subsequent 

discussions with the State data office led to a correction. In the final two rows of the 

table related to whether the crash was a hit and run crash, the large shifts in 

percentages indicated that the yes/no codes had been erroneously reversed in 2007. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

42

Example Use in a Performance Goal 

Assume a State wishes to incorporate a version of R-U-1 into a performance goal. The State 

defines uniformity in terms of the presence of the same variable in both the State’s basic 

roadway inventory file and the MIRE listing, with the definition of the variable being the same in 

each, regardless of whether the variable names or coding are identical in both files. Further 

assume that the State has compared data elements in its file with the 61 MIRE elements in the 

segment location/linkage list, the segment classification list, and the segment cross section list 

and learned that, for the State system, only two of the 52 critical variables in MIRE are missing. 

The State considers this to be adequate coverage and has thus chosen to define a measure 

concerning uniformity with the MIRE elements related to at-grade intersections on Statesystem roads. The State wishes to ultimately include both the general-descriptor variables 

related to the entire intersection and those variables collected for each approach. The 

following steps appear to be necessary: 

1. Define the measure. The State defines its roadway uniformity measure as follows: 

 R-U-1: The percent of critical MIRE data elements related to at-grade intersections 

entered into the basic inventory file for State-system roads. 

2. Determine the target data elements in MIRE. There are 58 intersection-related 

elements in the MIRE listing, 43 of which MIRE deems “critical” for analytic tools such as 

SafetyAnalyst or IHSDM While some of the additional 15 value-added elements may be 

incorporated into the inventory file, the State has decided that the measure and 

performance goal will concern only the 43 critical elements. 

3. Determine the MIRE-compliant elements in the inventory file. As noted above, the 

State considers a variable to be MIRE-compliant if its definition matches the MIRE 

definition (i.e., the codes do not have to match). The State will examine the definitions 

of each of the 43 critical MIRE elements and determine which are present in the State 

inventory file. This examination may reveal some variables in other inventory files but 

not yet in the State intersection file (e.g., route/milepost data on crossing routes may be 

available in the spatial data file; information on intersection traffic control may be 

available in a separate traffic signal file). These elements would not be counted as 

present in the base year but could be added in the subsequent years. 

4. Calculate the measure. The measure will be calculated by dividing the total number 

found in the current State file by 43, the number of critical MIRE elements. 

5. Specify the performance goal. The initial calculation will provide a baseline for use in the 

goal. Assume in this case that the number of critical MIRE intersection variables for 

year one is 15, so the calculated baseline measure is 35 percent. The State could then 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

43

decide that it wishes to include all 43 elements within the next four years and then 

specify that the goal for each subsequent year is the addition of seven elements each 

year. Thus, the yearly proportion of critical elements is as follows: 

 Year two goal – 51 percent of all critical elements. 

 Year three goal – 67 percent of all critical elements. 

 Year four goal – 83 percent of all critical elements. 

 Year five goal – 100 percent of all critical elements. 

6. Recalculate measure each year. The same procedure would be followed each year to 

allow the State to chart progress in reaching its goal. 

INTEGRATION 

Definition 

Integration is the ability to link the data records in one database 

with common or related records in another database. Such 

linkage among databases provides a richer source of 

information on which to conduct analyses. Because 

integration is based on the relationship between multiple individual datasets, it is unique from 

the other performance attributes in that it always involves two or more traffic records systems. 

When devising measures for integration, the focus may range from macro—the number of 

databases that can be linked to a particular database—to micro—the number of individual 

records from one file that can be linked to the individual records of another. 

Once a State has demonstrated the ability to integrate databases, this author would 

recommend the use of the micro measures (i.e., those that examine the linkage of individual 

records). Files that utilize the same location referencing system (e.g., GIS or linear-referencing 

system) may be considered “linkable,” but a substantial number of individual records may not 

be directly relatable to one another if there are errors in the linkage variables of either file. 

This points to the fundamental question of when two files can be declared truly “linkable”—

when they have the same location address system (e.g., milepost or latitudinal/longitudinal 

coordinates), or when some percentage of the records are linkable? Both these issues seem to 

point to micro-based measures that are based on the percentage of records that can be linked. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

44

NHTSA Report Measures 

The following is proposed in the NHTSA Report: 

 R-I-1: The percentage of appropriate records in a specific file in the roadway database 

that are linked to another system or file. 

o For example, a State may wish to determine the percentage of records in the 

State’s bridge inventory that can be linked to the basemap file. 

Discussion 

Integration is needed both between different inventory files (e.g., the basic roadway inventory 

file and the bridge file) and between roadway inventory files and non-roadway inventory files in 

the other five core databases (e.g., the crash file). The proposed measure is general in nature, 

leaving it up to the user to determine which pairs of specific files should be analyzed. The same 

measure (i.e., percentage of records that is linkable) would be used for each of the pairs of files. 

From the perspective of safety analyses, the most important pair of databases includes the basic 

roadway inventory file and the crash file. Linkability between these datasets is critical to safety 

analyses incorporating both traditional measures and newer tools that are used to support the 

State’s HSIP. At the next level of importance would be linkages between the basic inventory 

file and other inventory files containing variables needed for safety analyses (e.g., bridge 

database, traffic control device database, etc.). The user would need to identify each inventory 

file that contains critical safety variables and analyze the degree of linkability between it and the 

basic inventory file. The following are example data elements required by the Highway Safety 

Manual, IHSDM, or SafetyAnalyst that are often not contained within the basic inventory files 

(but may be in other datasets): 

 Horizontal curve data. 

 Grade information. 

 Number of legs at an intersection, and the milepost on the minor road. 

 Minor road AADTs (perhaps in files from local jurisdictions). 

 Driveway counts within categories. 

 Centerline rumble strip presence. 

More information concerning what is required by these tools is in Appendix B of the MIRE 

report (1).
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

45

Suggested Modifications/Additions 

None other than those noted above. 

Example Use in a Performance Goal 

Assume a State wishes to develop performance measures and goals related to the integration of 

the crash file and its basic roadway inventory. It has been decided that two measures will be 

developed: 

1. The degree to which total reported crashes can be linked to the roadway inventory file. 

2. The extent of linkage of roadway inventory data to crashes occurring on roadways 

already included in the inventory file; for many State DOTs, this would be all Statesystem roads. 

The first performance measure will provide information on the ultimate goal of establishing a 

statewide inventory file that is linkable to all crashes. The State understands that this measure 

is controlled by two factors: (1) the completeness of its roadway inventory file (i.e., what 

portion of total State mileage is included in the inventory file), and (2) the accuracy of the 

linkage information in each file. Using automatic linkage procedures already in place, the State 

will attempt to link all crashes for the base year with the existing inventory file, and the 

measure will be the percentage that can be linked. Additional analyses could bring to light any 

possible impediments to the overall integration, which may include the following: 

 Crashes were not assigned a location address (e.g., route/milepost, 

latitudinal/longitudinal coordinates, etc.) due to a lack of location descriptors. 

 Each crash was assigned a location, but the location did not match any segment in the 

inventory file. 

The initial calculation will provide a baseline for use in the specification of a performance goal. 

Assume in this case that roadway inventory data may be linked to 55 percent of reported 

crashes. Assuming there is an ongoing effort to increase the roadways covered in terms of 

crashes mileposted and roadway inventory data available, the State could then adopt a goal that 

is percentage based. For example establish a performance goal to increase the proportion of 

linked crashes by increments of five percent each year. For this example it would yield 60 

percent linkage in year two, 65 percent in year three, and so on. 

The second measure concerns only crashes on State-system roads and is related primarily to 

the accuracy of the linkage information in the crash and roadway inventory files. This measure 

will involve more effort, as the crash records being tested should only include those that can 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

46

possibly be linked with the existing inventory file (i.e., only those crashes on State-system 

roads). The following steps appear necessary: 

1. Specify which roadways included in the inventory file are to be tested. This will perhaps 

be the most difficult step in that the crash data may or may not include variables that 

will allow one to determine whether the crash could possibly be linked. Note that the 

goal is to test all crashes that could potentially be linked, including crashes that occurred 

on pertinent roadways but did not have adequate location information. Assume that the 

State’s roadway inventory file includes all Interstate, U.S., and State-numbered highways 

and some secondary roads. Since the identification of potentially-linkable crashes on 

secondary roads would essentially require a full address (i.e., route and milepost), an 

option would be to select only those route types that are completely covered in the 

inventory file—in this case, Interstate, U.S., and State-numbered routes. Assuming that 

the “route type” part of the address is coded for all crashes, crashes not occurring on 

these routes could be screened out, leaving only the potentially-linkable ones. 

2. Calculate the measure. At a time chosen by the State—possibly the date at which the 

year-end crash file is finalized—attempt to link all potentially-linkable crashes with the 

basic inventory file. Calculate the measure by dividing the number of crashes linked by 

the total number of potentially-linkable crashes (i.e., the total number of crashes on 

Interstate, U.S. and State-numbered highways). 

3. Specify the performance goal. The initial calculation will provide a baseline for use in the 

goal moving forward. Assume in this case that 80 percent of potentially-linkable crashes 

were actually linked to the appropriate roadway inventory data. The State can then 

decide to increase that percentage by five percent each year, with a final target goal of 

95 percent linked (i.e., 85 percent at the end of year two, 90 percent at the end of year 

three and 95 percent at the end of year four). Note that this percentage will not 

increase without additional efforts to determine the cause of non-linkage (e.g., errors in 

the inventory files, errors in original data reported by law enforcement, errors in crash 

location even with sufficient police data, etc.) and efforts to correct the problems 

identified. 

4. Recalculate the measure each year. The same procedure would be followed each year 

to allow the State to chart the progress in reaching its goal. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

47

ACCESSIBILITY 

Definition 

Accessibility concerns the degree to which data or information from the data files are readily and 

easily available to legitimate users. As noted in the NHTSA Report, accessibility is a measure of 

customer satisfaction. Accessibility can apply to both individual records and to either periodic 

reports or report-generating tools. The following discussion will concern the accessibility of 

the data itself.

It would appear that the specification of accessibility measures 

is dependent on the data agency’s customer service goals. Two 

slightly different goals emerge:

 Goal 1: Make data accessible to legitimate users in an 

easy and timely fashion. 

 Goal 2: Make data accessible to legitimate users in an 

easy and timely fashion, and provide additional datarelated assistance and marketing to increase data usage. 

The first goal is related to providing existing data to rightful and appropriate users. The second 

goal builds upon the first by also encouraging and assisting in the use of the data in order to 

increase the number of users. Measures for the first goal would the ease of data acquisition 

from those who request and obtain the data. Measures for the second goal would include 

these but also add measures of increases in data request. This suggests that an inventory data 

program should not only emphasize making the data accessible, but it should also look to 

market the data to potential users and provide customer service resources (e.g., a helpdesk) to 

simplify and accommodate an efficient use of the data. Note that the CDIP Guide suggests that 

marketing crash data is part of accessibility, so the number of users would be a logical measure. 

Monitoring the use of inventory data by key customers may be a necessary part of a State’s 

overall inventory program if use by others is important. For example, the Kentucky 

Transportation Cabinet has a program in which updated intersection AADT and other 

inventory information for State-system and higher-order county roads are provided annually by 

local development districts under a contractual arrangement. Under this arrangement, a valid 

measure for Kentucky might be the percentage of such districts that access the data on a 

regular basis. 

Use of data by others is a key component of a sound program, not only because the data are 

developed with public funds and thus should be available to others, but also because use by 

others will provide important inputs to the owner agency concerning issues and problems that 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

48

should, and can, be corrected. This is discussed further in Chapter 3 under “Business 

Practices.” The important issue is not just data accessibility but whether a goal of the agency is 

to increase usage. If this is the case, then the increases in the numbers of users is an important 

performance 

measure.

Washington State DOT Video Log Data 

WSDOT has made their State-system roadway video log available to the public at 

their Transportation Data web page (see http://srview.wsdot.wa.gov/). The user can 

select a region, find a specific route, and either “drive” the route or find a specific 

location based on information provided on the screen. A forward facing, right side and 

360-degree view are available.
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

49

NHTSA Report Measures 

The following measure and explanatory note for roadway data accessibility is proposed in the 

NHTSA Report: 

 R-X-1: To measure accessibility of a specific file in the roadway database: Identify the 

principal users of the file. Query the principal users to assess (a) their ability to obtain 

the data or other services requested, and (b) their satisfaction with the timeliness of the 

response to their request. Document the method of data collection and the principal 

users’ responses. 

The NHTSA Report then refers to an earlier general discussion concerning accessibility and 

makes the following key points: 

1. R-X-1 is for each database (and thus might be used repeatedly for the multiple inventory 

databases). 

2. The data manager determines who the legitimate principal users are—who is the best 

judge of data accessibility? 

3. The principal users would then be queried to assess whether they received the 

necessary data and if the data were delivered in a timely fashion. 

4. The data manager would then document the system or method used in the query and 

the principal users’ responses. 

Two example survey methods are provided. In the first, the principal users are surveyed on an 

annual basis, and they provide information on applicability of the data they received, timeliness 

of the response, etc. The data manager then reviews these inputs and classifies them as 

positive, neutral, or negative, and the measure is the percentage positive (or negative) of all 

responses. In the second example method, the manager reaches out to each user within two 

days of data delivery and asks questions concerning whether the requested data were received 

and if the user was satisfied with the speed of the response; sample answers to both questions 

include “yes,” “for the most part,” “partially,” and “no.” 

Discussion 

The accessibility measure changed more through the NHTSA developmental process than did 

any other measure. Earlier versions had specific measures including number of users requesting 

and receiving data, number or percentage of authorized users requesting and receiving data, and 

the percentage of requests filled within the agency’s defined timeline. As indicated above, the 

earlier measure involving the numbers of users are more appropriate for a data agency who 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

50

desires to continually increase usage of its data. The current measure appears to be related to 

goal one above—providing good access to the data but not necessarily increasing usage. 

The NHTSA Report also indicates that the suggested measure would be best used to gauge the 

impact of an improvement to a data system (presumably an improvement that makes it easier 

to extract and distribute data). This would be done by surveying principal users before and 

after the rollout of a specific upgrade that would “provide the most meaningful measure of 

improved database accessibility.” However, improved (or acceptable) accessibility should be a 

continuing goal, not just one measured with system changes. 

Suggested Modifications/Additions 

The measures noted are sound, although it does appear to be more appropriate for a data 

agency whose principal goal is to provide existing data to primary users rather than to increase 

data use. For agencies that also wish to increase use, the following is a possible supplemental 

measure: 

 R-X-2: Number of users receiving information from the inventory file. 

This author would also suggest modifying the part of the NHTSA example related to whether 

the data were received in a timely fashion. The example measure suggests asking whether the 

user was satisfied with the speed of the response, with possible responses being “yes”, “for the 

most part”, “partially,” and “no.” Since this leaves “acceptable speed” as a subjective judgment 

that can differ from user to user, it might also be good to include a supplemental question 

concerning the amount of actual time between submission of the request and receipt of the 

data. Or, if the agency has a defined goal for responding to a request, an additional measure 

could be established as follows: 

 R-X-3: Percent of requests received for information or data from the State roadway 

inventory file that were filled within the State’s defined timeline. 

Note that this measure can be generated 

FHWA’s HSIS is oriented to providing without the user survey if the agency tracks 

data from a multistate database to the date the request is received and the 

researchers. It has a goal of a one-week date the data are sent out. 

turnaround for data requests. Tracking 

Finally, it is suggested that if a user survey is this measure has led to changes in 

used, it should include at the end a free- request forms and processing methods. 

form request for suggestions for improving 

accessibility. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

51

Example Use in a Performance Goal 

Assume a State wishes to incorporate the procedure specified in R-X-1 above in order to 

define the following two-part measure: 

 R-X-1: The percent of internal and external roadway inventory data requestors who 

judged (a) the data received to be what was requested, and (b) the timeliness of the 

response to be acceptable. 

The State wishes also to use the survey to obtain feedback from users on possible 

improvements to its data and data distribution systems. Knowing that surveys are often 

declined or ignored, the State has also decided to develop the following (non-survey) 

companion measure: 

 R-X-2: Percent of requests received for information or data from the State roadway 

inventory file that were filled within two weeks of receiving the request. 

The State has also decided that, at this point, only the accessibility of the basic inventory file 

data is to be tested and that, rather than limiting the measure to requests from only principal 

users, any request that can be answered (e.g., there may be requests for data that do not exist) 

will be included in the process. The following steps appear to be necessary: 

1. Establish a request documentation system. Requests for data may be received in 

different formats (e.g., phone, mail, email, webpage contact, memo from another agency 

or department, etc.) and by different people (e.g., data manager, data staff member, 

etc.). A system (perhaps spreadsheet-based) should be established that records for 

each request the following information at a minimum: 

a. Requestor name and contact information. 

b. Date request was received. 

c. Specifics of request (often in narrative form). 

d. Person to whom request was assigned (i.e., staff member responsible). 

e. Date data or information were sent out. 

f. Comments; e.g., notes on each follow-up contact necessary to complete the 

request. (Contacts are often needed to clarify the request.) 

2. Determine time of survey. As noted above, the survey can be done at the time of data 

distribution or at some established time (e.g., annually). The advantages of surveying at 

the time of distribution include better recall of both satisfaction and timeliness and the 

fact that the survey can be included with the data, which may represent a time when the 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

52

requestor may be more likely to respond. Periodic (e.g., annual) surveying would 

provide an overall average rating from principal users who submit multiple requests 

each year and may provide more useful inputs as to how the data and delivery systems 

can be improved. For our example, the State has decided to survey at the time the data 

are delivered. 

3. Develop survey method. The data manager will need to determine how the survey is to 

be distributed—by phone, mail, email, or some combination thereof. The State has 

decided that, since the majority of requests are now filled via email or through 

downloads from their website, email will be the primary survey delivery method. 

4. Develop survey form. The NHTSA Report provides details concerning what 

information might be requested and example questions that might be asked. The box 

below shows one example survey incorporating some of these suggestions. 

5. Follow up with non-respondents. Because low response rates to surveys are the norm, 

obtaining a higher response may require that the State resend the survey with a 

reminder note at some point after the data are distributed or after the annual survey is 

distributed. This will require that the State either send a reminder to each data 

recipient (e.g., two weeks after the date of data delivery, including a note saying, 

“Disregard this reminder if you have responded,”) or to only those who have not 

responded. While the latter is better customer service, it will require the development 

of a response tracking system. The date that the response is received could be 

captured in the request documentation system described in Step 1 above. 

6. Calculate the measure(s). At least annually, the data manager or person responsible for 

this effort will compile the survey responses and calculate the percentages within each 

category of each response and summarize the non-categorical responses (e.g., calculate 

the number of responses with over 21 days between request and data delivery; list and 

summarize suggestions for improvements). The second measure related to percent of 

requests filled with a given time period can be calculated directly from the request 

documentation system described in Step 1 above. 

7. Specify the performance goal(s). The State’s system can be used to define three 

different goals: (1) the percentage of responders fully satisfied with the data received, (2) 

the percentage of responders who were fully satisfied with the timeliness of the 

response, and (3) the percentage of requests satisfied (from the internal system) within 

two weeks. The State could then specify three different performance goals. The initial 

calculation will provide a baseline for use in each goal. Assume, for example, that the 

percentage fully satisfied with the timeliness of the data delivery was 70 percent. The 

State can then decide to increase that percentage by five percent each year, with a final 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

53

target goal of 95 percent satisfied (i.e., 75 percent at the end of year two, 80 percent at 

the end of year three, etc.). Note that the suggestions received may provide 

information on both the problems and the potential solutions. 

8. Recalculate the measure each year. The same procedure would be followed each year 

to allow the State to chart the progress in reaching its goal. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

54

Dear DOT Customer, 

You recently requested roadway inventory data from the (State X) DOT Information and 

Technology office. Please take a few minutes and respond to the following questions. 

Input from customers like you will help us to improve our program. 

1. Did you receive the data, information, or assistance that you requested? 

__ Yes 

__ No (If “No”, stop here and send reply). 

2. Were you satisfied with the data? Was it what you needed? 

__ Yes, fully satisfied 

__ Yes, satisfied for the most part 

__ Only partially satisfied 

__ Not satisfied 

3. Approximately how many days did it take us to respond to your request? 

(Enter number of days here: ________) 

4. Were you satisfied with the speed of the response to your latest request? 

__ Yes, fully satisfied 

__ Somewhat satisfied 

__ Not satisfied 

5. Do you have any suggestions concerning how we can improve our data or our data 

delivery system? 

If you wish to discuss your experience with one of our staff, please call xxx-yyy-zzzz. 

Thank you!
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

55

CHAPTER 3: MEASURE-RELATED BUSINESS PRACTICES 

The preceding discussion has detailed specific roadway inventory metrics whose regular use can 

provide insight into the health of the inventory system and the issues and problems that need 

to be addressed. Clearly, the regular review of these measures without follow-up problemcorrection efforts is of little value to a data agency. The purpose of this section is to 

enumerate business practices that may help to improve the correction process. Many of these 

practices are either in use in State roadway inventory agencies or have been suggested in other 

data-related discussions. 

ESTABLISH/IDENTIFY A ROADWAY INVENTORY LEADER 

Roadway inventory systems are usually comprised of multiple files with multiple owners. 

Placing a single person in charge can enhance coordination among the owners and ensure that 

the necessary modifications are made. The job description of the roadway inventory leader 

should be clearly defined, and the position should include adequate support in both staffing and 

funding. The term “leader” is used rather than coordinator (since the job will include, but not be 

limited to, coordination) or czar (since this person is not likely to officially oversee all other 

inventory data owners). This leader should be a permanent member of the State’s Traffic 

Records Coordinating Committee (TRCC). 

Based on the experience of State agencies around the U.S. who have superior inventory data, 

this person not only needs to be a leader, but also a champion—someone with a passion for the 

expansion and enhancement of roadway inventory data. 

ESTABLISH AN INVENTORY COORDINATING COMMITTEE 

The TRCC should help a State to meet the coordination needs of all traffic records efforts and 

systems. A suggested subcommittee of the TRCC would be an Inventory Coordinating 

Committee. The Inventory Coordinating Committee would focus exclusively on inventory 

data. It would have membership representing each of the various existing inventory files (e.g., 

basic roadway inventory, pavement management, asset management, etc.) and a representative 

of the information technology group or GIS group who works with the different types of 

inventory data. The committee would meet on a regular basis, with the inventory leader and 

his or her staff responsible for setting an agenda, calling the meeting, and recording and 

distributing meeting minutes. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

56

ESTABLISH ROADWAY INVENTORY MEASURES 

The inventory committee and leader should establish a set of measures that will assist in the 

management of the inventory system. Two or more are suggested for each of the six 

performance attributes described above. If the measures posed in Chapter 2 are used, the 

committee will also have to address the noted corresponding issues (e.g., number versus 

percentage, whether separate measures are needed for the inventory data versus the basemap, 

etc.). 

CALCULATE THE MEASURES REGULARLY 

The leader and staff would be responsible for the regular (e.g., annual) calculation of each of the 

measures and reporting to the committee and other pertinent parties like the State TRCC. 

CONDUCT PERFORMACE MEASURE RELATED FOLLOW-UP ANALYSES 

As noted above, many of the measures will help to identify the basic nature of issues or 

problems in the inventory system, such as unacceptable levels of missing data, or a failure to 

link specific files. However, the performance measures will not produce the level of 

information needed to define the problem in the detail required to allow for correction, and 

additional analyses will be needed. These would primarily be the responsibility of the inventory 

leader and staff but may require the participation of other owner-agencies, as well. 

ESTABLISH PERFORMANCE GOALS 

Performance goals define improvements (e.g., the reduction of missing data in critical roadway 

elements to less than five percent in two years) and would be the responsibility of the 

committee. Bearing in mind that a goal is useless unless agreed upon by the data owner, all 

goals should not only be clear and realistic but also cause the agency to strive to improve upon 

the status quo. States are encouraged to develop goals based (at least in part) on historical 

trends, predictive analyses, or some other analytically based method. 

DEVELOP A SYSTEM OF INTERNAL QUALITY-CONTROL CHECKS 

The development of a method of internal quality-control checks was discussed under the 

Accuracy section in Chapter 2, with a suggested additional measure concerning attribute values 

within reasonable ranges and consistency with related variables. 

It was also noted under the discussion of “consistency across time” that there needs to be 

computer-based practices that can help detect shifts in coding across time. While one would 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

57

expect the same location to change over the years due to construction modifications, one 

would also expect the basic attributes of an inventory file in total to stay somewhat consistent 

across years. Simple checks, such as the number of miles or junctions inventoried each year, 

could detect major changes in the inventory database that are likely erroneous. Year-to-year 

comparisons of the percentage for each attribute for a given variable (i.e., the percent within 

each median type, shoulder type, degree of curve category, etc.) can provide even more 

detailed information on possible undocumented coding changes. The FHWA’s HSIS has 

computerized such a series of checks and annually compares the coding for each variable in the 

new inventory file (and all other files) to the coding in the file from the previous year. The 

program has a defined set of allowable changes to 

percentage or frequency and outputs only those 

variables that fail to meet these criteria. Those 

outputted variables are then examined in more 

detail by looking at the codes across multiple 

years. Questions are then directed back to the 

owner agency, and, based on their input, 

corrections are made where necessary. 

Documentation of legitimate changes is then 

included in the User Manual under the relevant 

variable. To make multiyear analyses easier, the 

HSIS also annually changes the codes and 

attributes for a given variable from all previous 

years to match the current year codes. (Since 

attributes are often added in the middle of a set of codes, such action does require significant 

effort.)

A second consistency-across-time issue that is much more difficult to solve is that the address 

of the same roadway location can change across years in linear referencing systems (e.g., 

route/milepost). Because most analyses are multiyear, it is often important that an analyst be 

able to trace the same roadway segment or intersection across multiple years even if the 

address has changed. (Note that this problem is essentially solved by the use of a spatial-data 

system whose coordinates remain constant from year to year.) While this is not a simple 

problem to resolve with a computerized fix, WSDOT has developed a program for their 

inventory system that does just this. Other States solve the problem by manually changing the 

crash mileposts from past years to match the current inventory milepost. 

INCREASE INVENTORY DATA USE 

While the previous discussion of accessibility leaves it to the discretion of an agency to 

determine its targeted level of data usage, a good business practice would be to become a 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

58

“Goal 2” agency—one that strives to increase data use by new clients, whether they be inside 

or outside the agency. When there are more users of a dataset, there is a higher probability 

that problems and issues with the data can be identified. Although FHWA’s HSIS attempts to 

limit errors in the data through a series of quality control checks before the data are released 

each year, it is still the case that important issues and problems are discovered by the users. 

Some key components of increased usage may include the following: 

 Providing detailed documentation of the data in some type of “User Guide.” 

 Marketing the data beyond the normal users. 

 Meeting data request within a short timeframe. 

 Obtaining feedback from the users and resolving their issues. 

DEVELOP HIGH-LEVEL SUPPORT FOR “GETTING IT RIGHT” 

In the opinion of this author, some of the best State inventory systems in the Nation not only 

have very good leaders at the working level but also have high-level support for ensuring that 

both the data and data systems are of superior quality. In this context, “high-level” refers to 

political power in the State DOT. Unfortunately, it is not easy to determine how such support 

can be developed. Paramount to an executive-level backing may be a thorough understanding 

by the administration of (1) the data system itself, (2) how the system is used, and (3) who the 

key customers are, as well as continual communication between the inventory leaders and the 

administration. Developing innovative ways to use premium roadway data in legal liability cases 

may also help to win high-level support. 
PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

59

SUMMARY 

Inventory data are becoming increasingly critical as more advanced safety management tools— 

ch as the Highway Safety Manual, the IHSDM, and SafetyAnalyst—are introduced that require 

ese data. Future safety program decisions made by State and local agencies will be predicated 

pon high-quality roadway inventory data. Performance measures are tools that can be used to 

crease the quality of inventory data. Significant effort will be required to realize the full 

enefits of incorporating measures into an agency’s data inventory program. Decisions will 

ave to be made concerning what performance measures are most likely to be useful to a given 

ser agency, the specific details of how each measure will be defined, and who will be 

esponsible for their periodic measurement. Even if gauged regularly, measures alone will be of 

ttle use to an agency unless a corresponding problem-correction process and dedicated staff 

re established. Well-defined and agency-accepted performance measures can improve 

ventory data when combined with sound performance measure-related business practices. 

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PERFORMANCE MEASURES FOR ROADWAY INVENTORY DATA 

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REFERENCES 

1. Lefler, Nancy X., F. Council, H. McGee, D. Harkey, D. Carter, M. Daul. Model Inventory 

of Roadway Elements – MIRE, Version 1.0, Final Report. Washington, D.C.: Federal Highway 

Administration, June 2010. 

2. NHTSA. Traffic Safety Performance Measures for States and Federal Agencies. DOT HS 811 

025. Washington, D.C.: National Highway Traffic Safety Administration, 2008. 

3. Hedlund, James. Model Performance Measures for State Traffic Record Systems White Paper: 

Request for Comment on Proposed Measures and Guidance. Washington, D.C.: National 

Highway Traffic Safety Administration, July 2009 (Unpublished). 

4. Hedlund, James. Model Performance Measures for State Traffic Records Systems – Draft 

Report. Washington, D.C.: National Highway Traffic Safety Administration, Nov. 2009. 

5. NHTSA. Model Performance Measures for State Traffic Records Systems. DOT HS 811 441. 

Washington, D.C.: National Highway Traffic Safety Administration, February 2011. 

6. Boodlal, L., J. Emery and R. Souleyrette. Crash Data Improvement Program Guide. 

Washington, D.C.: Federal Highway Administration, April 2010. (See 

http://safety.fhwa.dot.gov/cdip/finalrpt04122010/). 

7. Traffic Safety Analysis Systems & Services. Current State of Traffic Safety Information 

Systems Quality Metrics. Washington, D.C.: National Highway Traffic Safety 

Administration, 2008 (Unpublished Draft Report). 

8. Furst, Anthony. Guidance Memorandum on Fundamental Roadway and Traffic Data Elements 

to Improve the Highway Safety Improvement Program. Washington, D.C.: Federal Highway 

Administration, August 1, 2011. (See 

http://safety.fhwa.dot.gov/tools/data_tools/memohsip072911/). 

9. Winter, David R. and James Cheatham. Geospatial Network for All Public Roads 

(Memorandum). Washington, D.C.: Federal Highway Administration, August 7, 2012. 
For More Information:

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

FHWA, Office of Safety

Robert Pollack 

Robert.Pollack@dot.gov

202-366-5019

Dr. Carol Tan 

Carol.Tan@dot.gov

202-493-3315

FHWA–SA–12–036