SECOND U.S. ROADWAY SAFETY 

DATA CAPABILITIES ASSESSMENT 

http://safety.fhwa.dot.gov

FHWA Safety Program
SECOND U.S. ROADWAY SAFETY DATA CAPABILITIES ASSESSMENT FINAL REPORT

 

FOREWORD

The Federal Highway Administration (FHWA) conducted a second capabilities assessment for each State 

plus the District of Columbia and Puerto Rico in terms of the collection, analysis, management, and 

integration of roadway, traffic, and crash data. The previous U.S Roadway Safety Data Capability 

Assessment was conducted in 2012 resulting in the establishment of the FHWA Roadway Safety Data 

Program (RSDP). The current assessment is part of the RSDP’s, continuing collaborative effort between 

the FHWA and States to ensure that they are best able to develop robust data-driven safety capabilities. 

This final report provides an overview of findings based upon the assessment of each State. 

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.
TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-19-018

2. Government Accession

No. N/A

3. Recipient's Catalog No.

4. Title and Subtitle

Second U.S. Roadway Safety Data Capabilities Assessment Final

Report

5. Report Date

September 19, 2019

6. Performing Organization Code

N/A

7.Author(s)

Bob Scopatz, Matt Albee, Geni Bahar, Richard Brown, Daniel Carter,

Joshua DeFisher, Claudio Figueroa Bueno, Jeff Gooch, Elissa

Goughnour, Frank Gross, Ian Hamilton, Tim Harmon, Frances Harrison,

Kari Signor, Sarah Smith, Jack Stickel, Saleem Taha, Eric Tang, Bethany

Turner, Tony Wyatt, Yuying Zhou, Esther Strawder, Dana Gigliotti

8. Performing Organization Report

No.

N/A

9. Performing Organization Name and Address

VHB

Venture 1: 940 Main Campus Dr., Suite 500

Raleigh NC 27606-5217

10. Work Unit No. N/A

11. Contract or Grant No.

DTFH61-16-D-00005

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 2019

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this project was Esther Strawder.

16. Abstract

The Federal Highway Administration (FHWA) conducted a second roadway data capabilities assessment of each

State’s collection, analysis, management, and use of roadway safety data. This final report provides an overview

of findings based upon the assessment of fifty States plus the District of Columbia and Puerto Rico.

17. Key Words:

Roadway safety data, data collection, data

management, data governance, data

analysis, data integration, safety

performance measurement, capability

assessment, capability maturity model

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this

page) Unclassified

21. No. of

Pages

57

22. Price

N/A

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

EXECUTIVE SUMMARY............................................................................ 1

CHAPTER 1. INTRODUCTION................................................................ 7

BACKGROUND............................................................................................................ 8

Data Capabilities Peer Exchanges ............................................................................ 8

CHAPTER 2. OBJECTIVE ........................................................................11

CHAPTER 3. METHODOLOGY..............................................................13

ASSESSMENT QUESTIONNAIRE UPDATE..........................................................13

CAPABILITY MATURITY MODEL UPDATE AND EXPANSION......................13

Maturity Levels..........................................................................................................13

ASSESSMENT AREAS ...............................................................................................14

Area 1: Roadway Inventory Data Collection/Technical Standards .....................14

Area 2: Data Analysis Tools and Uses ....................................................................15

Area 3: Data Management and Governance .........................................................15

Area 4: Data Integration..........................................................................................15

Area 5: Safety Performance Management.............................................................16

PROCESS.....................................................................................................................16

Final Steps .................................................................................................................16

CHAPTER 4. FINDINGS ..........................................................................19

FINDINGS ABOUT THE OVERALL ASSESSMENT PROCESS ..........................19

GENERAL ASSESSMENT FINDINGS......................................................................19

KEY FINDINGS FOR AREA 1: DATA COLLECTION AND DATA QUALITY . 20

Suggested Actions ....................................................................................................22

KEY FINDINGS FOR AREA 2: DATA ANALYSIS .................................................22

Suggested Actions ....................................................................................................24

KEY FINDINGS FOR AREA 3: DATA MANAGEMENT & GOVERNANCE.......24

Suggested Actions ....................................................................................................26

KEY FINDINGS FOR AREA 4: DATA INTEGRATION .........................................27

Suggested Actions ....................................................................................................28

KEY FINDINGS FOR AREA 5: SAFETY PERFORMANCE MANAGEMENT .....28
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Suggested Actions ....................................................................................................30

NATIONAL GAPS SUMMARY .................................................................................30

CHAPTER 5. CONCLUSIONS ................................................................33

APPENDIX A – STATE MATURITY LEVELS........................................35
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LIST OF TABLES

Table 1. State capability level scores...............................................................47

 LIST OF FIGURES

Figure 1. Radar chart of national average scores and goals for data quality........... 21

Figure 2. Radar chart of national average scores and goals for safety analysis........23

Figure 3. Radar chart of national average scores and goals for data management 

and governance...........................................................................................25

Figure 4. Radar chart of national average scores and goals for data integration.....27

Figure 5. Radar chart of national average scores and goals for safety performance 

management.............................................................................................. .29

Figure 6. National average baseline for State data capability..............................32

Figure 7. Capability maturity level distribution for data collection: Completeness.

...........................................................................................................................................36

Figure 8. Capability maturity level distribution for data collection: Timeliness...... 36

Figure 9. Capability maturity level for data collection: Accuracy.........................37

Figure 10. Capability maturity level distribution for data collection: 

Uniformity/Consistency.................................................................................37

Figure 11. Capability maturity level distribution for data analysis: Network 

Screening (Data).......................................................................................... 38

Figure 12. Capability maturity level distribution for data analysis: Network 

Screening (Methodology).............................................................................. 38

Figure 13. Capability maturity level distribution for data analysis: Network 

Screening (Coverage)................................................................................... 39

Figure 14. Capability maturity level distribution for data analysis: Diagnosis........ 39

Figure 15. Capability maturity level distribution for data analysis: Countermeasure 

Selection. ..........................................................................................................................40
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Figure 16. Capability maturity level distribution for data analysis: Evaluation....... 40

Figure 17. Capability maturity level distribution for data analysis: Accessibility.... 41

Figure 18. Capability maturity level distribution for data management & 

governance: Roles and Responsibilities............................................................41

Figure 19. Capability maturity level distribution for data management & 

governance: Policies.................................................................................... 42

Figure 20. Capability maturity level distribution for data management: Technology.

...........................................................................................................................................42

Figure 21. Capability maturity level distribution for data integration: General 

Integration................................................................................................. 43

Figure 22. Capability maturity level distribution for data integration: Expandability.

...........................................................................................................................................43

Figure 23. Capability maturity level distribution for data integration: Spatial Data 

Integration................................................................................................. 44

Figure 24. Capability maturity level distribution for safety performance 

management: Data...................................................................................... 44

Figure 25. Capability maturity level distribution for safety performance 

management: Processes............................................................................... 45

Figure 26. Capability maturity level distribution for safety performance 

management: Analysis Capabilities................................................................ 45

Figure 27. Capability maturity level distribution for safety performance 

management: Coordination.......................................................................... 46
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ACRONYMS

DOT Department of Transportation

CDIP Crash Data Improvement Program

CMM Capability Maturity Model

CMF Crash Modification Factor

EMS Emergency Medical Service

FDE Fundamental Data Elements (of MIRE)

FMCSA Federal Motor Carrier Safety Administration

FHWA Federal Highway Administration

GAO Government Accountability Office

GIS Geographic Information System

GHSO Governor’s Highway Safety Office

HPMS Highway Performance Monitoring System

HSIP Highway Safety Improvement Program

HSIS Highway Safety Information System

HSM Highway Safety Manual

HSP Highway Safety Plan 

IHSDM Interactive Highway Safety Design Model

IT Information Technology

LIDAR Light Detection and Ranging

MIRE Model Inventory of Roadway Elements

MPO Metropolitan Planning Organization

NHTSA National Highway Traffic Safety Administration

NRSAP National Roadway Safety Data Action Plan

RDIP Roadway Data Improvement Program

RITA Research and Innovative Technology Administration

RSDP Roadway Safety Data Partnership 

SHSP Strategic Highway Safety Plan

SPF Safety Performance Function

TRCC Traffic Records Coordinating Committee

TRSP Traffic Records Strategic Plan

USDOT United States Department of Transportation

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EXECUTIVE SUMMARY

The Federal Highway Administration (FHWA) conducted a second capabilities assessment for 

each State, the District of Columbia, and Puerto Rico on the collection, management, and use 

of roadway safety data. The first-round of the capabilities assessment ended in 2012. Its results 

helped FHWA develop technical assistance, guidance, peer exchanges, and noteworthy practice 

writeups aimed at helping States improve their data collection, analysis, data governance, and 

data integration efforts—the four key areas of the original assessment. In this new assessment, 

FHWA identified how the States have progressed since the first assessment and baselined a 

new area of Safety Performance Management. 

The results of the second-round capability assessment will help drive FHWA’s safety assistance 

and resources to States for the next several years. The three primary objectives of this project 

were to: 

• Update the assessment process, forms, and scoring, while adding the new Safety

Performance Management Area.

• Quantify States’ status and goals and help them identify critical gaps and potential actions

to achieve those goals.

• Identify safety programs, guidance, and technical assistance that States could use to

reach their goals and promote data-driven highway safety management.

The capability assessment process used a five-level Capability Maturity Model (CMM) with 1 

(lowest) to 5 (highest) scoring to describe the current capabilities of each State. The 

assessment focused on the following five areas in each State:

• Area 1: Roadway Safety Data Collection / Technical Standards – This section

developed two types of capability scores: how good are the data, and how good are the

management practices applied to the data. Unlike the first-round survey, which

concentrated mainly on roadway inventory data, the second-round survey addressed

roadway inventory, traffic volume, and crash data. States provided information in the

sub-areas of completeness, timeliness, accuracy, and uniformity of their safety data, and

received scores based on these quality attributes (data scores) and their management of

the data (CMM scores).

• Area 2: Data Analysis Tools and Uses – This section focused on how well

States are implementing advanced analytic methods and using the various tools that

support those methods. States provided information in the sub-areas of network
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screening (method, data, and coverage); diagnosis; countermeasure selection; evaluation; 

and accessibility. 

• Area 3: Data Management and Governance – This section focused on States’ data 

management and data governance in the sub-areas of roles & responsibilities, policies, 

and processes. 

• Area 4: Data Integration – This section focused on integration among the six core 

traffic records system components: crash, roadway, driver, vehicle, citation and 

adjudication, and injury surveillance data. States received scores on data integration in 

general, expandability, and spatial data integration. 

• Area 5 (new): Safety Performance Management – This section focused on States’ 

safety performance management. States received scores in the sub-areas of safety 

performance measurement data, safety performance management processes, and 

performance analysis capabilities. 

The project team used questionnaires and interviews to describe the capability of each State in 

the five areas. The States participated by providing relevant information, reviewing, and 

completing the questionnaire, and discussing the answers in a half-day meeting. The project 

team used a standardized scoring procedure to develop CMM scores for each State on each 

section of the assessment. Finally, each State confirmed their current capability level and 

selected their desired capability level. Along with this final report, FHWA will receive a 

database of all questionnaire answers and notes, the States’ action plans, plus the capability 

scores and goals on every segment of the CMM. This will help FHWA identify high (and low) 

performers in each area, and scan for agencies that plan to improve in specific areas. FHWA 

will review the scores and the notes from the assessments to identify candidates for hosting 

and attending peer exchanges, and the most requested areas of technical assistance, training, 

and guidance. 

The overall national average capability level scored 3.20 on a scale of 1 (low defined as ad hoc) 

to 5 (high defined as optimized). The capability level is below 3 in several key subareas: data 

quality management practices for completeness, timeliness, accuracy, and uniformity; analyses 

supporting countermeasure selection, the roles, responsibilities and policies for data 

management and governance; and safety performance management analysis. The national 

average of States’ desired capability level is 3.97, i.e., States wish to improve, on average, by 

almost one full capability level. 

In general, States want to improve their current roadway safety data capabilities and take the 

necessary steps to achieve their desired level. Most States are currently in the process of 

implementing data improvement projects, where their desired level is higher, and following a 
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plan for improvement, in one form or another. FHWA has access to each States desired levels 

through the States’ Action Plans.

Overall, the States appear challenged by managing data quality using formal processes. In Area 

1: Roadway Safety Data Collection/Technical Standards, the management process (CMM) 

scores are all much lower than the scores for data quality (average of 2.28 versus 3.64, 

respectively). Further, the data management and governance scores for both sub-areas roles 

and responsibilities and policies are low (2.46 and 2.3, respectively). The data management and 

governance area is one where capabilities appear to have dropped since 2012’s assessment. 

This is most likely due to States learning more about data governance in the intervening 

years—the lowest scores are in the subarea of data governance roles and policies. There are 

improvements in data governance processes since the first assessment. This implies that States 

have learned about data governance between 2012 and 2018. States are performing some data 

governance, but they are mostly doing it for single systems and without including agencies 

outside the State’s Department of Transportation (DOT).

In Area 1: Roadway Safety Data Collection/Technical Standards, national scores for 

completeness, timeliness, accuracy, and uniformity have improved from 2012 to 2018. 

However, scores for States’ capabilities in managing these same attributes using formal data 

quality management processes are much lower. These scores were disaggregated for this 

second-round capability assessment. The low CMM scores in Timelines (2.13), Uniformity 

(2.25), Completeness (2.32), and Accuracy (2.4) indicate that States could use assistance 

establishing data management practices including establishing data standards; measuring and 

routinely reporting data quality; setting data quality goals; and establishing quality control 

checks. Overall, States reported that their data quality itself was good, but there is a clear 

desire to improve quality as well. The national average goals showed increases for 

completeness (3.29 to 4.16), timeliness (3.38 to 3.94), accuracy (3.94 to 4.45), and uniformity 

(3.94 to 4.36). States can coordinate these efforts through expanded data governance. In 2012, 

the Data and CMM scores were combined into an overall score and may not be directly 

comparable to the 2018 scores; however, the 2012 version of the questionnaire was more 

heavily weighted toward the data quality measurements versus management practices (the 

CMM scores in 2018). Using those as a baseline for data quality, there are noticeable 

improvements between 2012 and 2018 in data quality as follows: Completeness increased from 

2.6 to 3.29 from 2012 to 2018. Timeliness improved from 3.3 to 3.38; accuracy improved from 

3.7 to 3.94; and uniformity improved from 3.8 to 3.94 

In Area 2: Data Analysis Tools and Uses identified national needs for improved countermeasure 

selection and expanded coverage for all public roads in network screening. States appear to 

have sufficient data for network screening and most are using advanced methods for network 

screening, diagnosis, and evaluation. Improved countermeasure selection would include 

expanded use of the Crash Modification Factor (CMF) Clearinghouse and enhanced analytic 
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capabilities. It is important to note that the national average for countermeasure selection has 

not changed in this area since the 2012 assessment—States still average roughly 2.8 on the fivepoint scale. The needed improvements may include complete data on all public roads as well as 

stronger reliance on advanced analyses and tools. Other scores did improve as follows: 

network screening methods improved from 3.6 in 2012 to 3.94 in 2018; network screening 

data improved from 3.5 to 3.79; and accessibility improved from 4 to 4.33. Only one area 

showed a decreased score: network screening coverage went from 3.4 in 2012 to 3.25 in 2018.

In Area 3: Data Management and Governance, the scores show that States are practicing data 

governance (3.25 score for processes) but are not well advanced in defining roles and 

responsibilities (2.46) or setting and enforcing data governance policies (2.31). These aspects of 

data governance would affect data quality management directly by setting and enforcing data 

standards and establishing data quality measurement as a standard business practice. The 

national average goals for roles and responsibilities (3.55) and policies (3.43) show that the 

States are interested in making large improvements in both aspects of data governance. Still, 

only the national goal for data governance processes reached a high level (4.08). There was a 

noticeable decrease in scores between 2012 and 2018 for data governance roles and 

responsibilities (from 3.1 to 2.46). The team believes this is due to many States not truly 

understanding what is involved in data governance in 2012, especially with respect to formal 

assignments of roles as well as policies. The policy score also showed a slight decrease between 

2012 and 2018 (from 2.5 to 2.31). The data governance processes score did increase, from 2.9 

to 3.25, indicating that States have adopted some of the data governance procedures that match 

the available guidance.

In Area 4: Data Integration, which was referred to as Interoperability and Expandability in 2012,

the scores improved and show that States rely heavily on spatial data integration tools and 

methods, and this likely is also reflected in a relatively high score for expandability. States 

scored lower on overall integration. There were small increases in the scores between 2012 

and 2018 as follows: data integration overall increased from 3.1 to 3.19; expandability increased 

from 3.7 to 3.75; and spatial data integration increased from 3.6 to 3.83. States’ goals for spatial 

data integration (4.55) are higher than for the other aspects of data integration as well. It is 

noteworthy that many more States achieved level 4 on the five-point scale for spatial data 

integration than in 2012 (32 versus 20, respectively). This reflects a major expansion in GIS for 

spatial data integration. 

In Area 5: Safety Performance Management, the scores show that processes (3.44) and analytics 

(2.96) lag data availability (3.71) and coordination (3.82). This reflects similar findings in the 

earlier areas of the questionnaire where the data are generally at a higher capability level than 

analyses and processes. States’ averaged goals are quite high for safety performance 

management in general with analysis at 3.98 and all other national goals above 4. This is an area 

where targeted assistance and guidance may be useful. 
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The findings of this effort present not only the current capabilities of the States through a 

consistent and thorough assessment process; but, also provide insight on the States’ desired 

capabilities and the actions needed to move the Nation forward in roadway safety data. States 

worked with the assessment team to develop an action plan as a source of specific steps 

toward desired improvements. The States can also use their action plans when they develop 

their next Traffic Records Strategic Plan (TRSP) and their Strategic Highway Safety Plan (SHSP) 

and Highway Safety Plan (HSP). FHWA will follow up on these report findings through a series 

of peer exchanges developed by the FHWA Office of Safety.

Appendix A presents additional results including State-specific anonymized results for each 

assessment area. 

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CHAPTER 1. INTRODUCTION

This is the second-round of the U.S. Roadway Safety Data Capabilities Assessment. It includes 

all 50 States plus the District of Columbia and Puerto Rico (hereafter referred to collectively as 

“States”). The first round, completed in 2012, showed where the States shared some common 

needs. Those included specific data quality improvements, data governance in general, and data 

integration to support data driven decision-making. At that time, the Highway Safety Manual was 

new, as were many of the tools available to States to support advanced safety analysis. 

Geographic Information System (GIS), though well established as a tool for safety data 

management, was just beginning to become accepted as a tool for data integration and analysis 

beyond its core utility for mapping and overlays. Data governance was an entirely new concept 

for many safety practitioners even in States where the Information Technology (IT) support 

staff were aware of the value of more formal methods of data management. 

Now, in 2019, highway safety analysis is still evolving, and the importance of integrated data has

grown. The past few years have seen new requirements established so that every State is 

working toward a public roadways database, acquisition of the Model Inventory of Roadway 

Elements (MIRE) Fundamental Data Elements (FDE) applicable to segments and intersections, 

and finding accessible and maintainable ways to coordinate State, regional, and local efforts to 

improve the data. This is all in support of a level of safety management that relies on better data 

for better decision making. It aims to support more advanced safety analysis and yield decisions 

that are more cost effective in reducing serious injuries and fatalities. 

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

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

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

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

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

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

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

addressing sites that share those features. The three datasets together support both systemic 

and location-specific predictive safety improvement. With the addition of other safety data 

sources describing drivers, vehicles, violations, and injury outcomes, engineering practitioners 

can collaborate with others seeking to affect behavior and emergency responses in ways that 

further reduce the frequency and severity of crashes. 

FHWA developed the Roadway Safety Data Program (RSDP) after the first assessment as a 

collaborative effort between FHWA and States to ensure that they are best able to develop 

robust data-driven safety capabilities. RSDP includes a variety of projects all aimed at improving 

the collection, analysis, management, and expansion of roadway data for use in safety programs 
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and decision-making. This report provides the highlights, methodology, and lessons learned 

from the assessments of each State in terms of the collection, analysis, and management of 

roadway safety data. This assessment describes national gaps, recommends actions, and 

provides FHWA with the information to develop guidance, peer exchanges, noteworthy 

practice descriptions, case studies, training, and technical assistance. 

BACKGROUND

A mature data-driven safety program requires that analysts can combine reliable crash, roadway 

inventory, and traffic data for the entire public roadway network. Using reliable tools and 

methods, analysts must provide usable information to decision makers. Decision makers must 

select and implement cost-effective countermeasures and evaluate their impact after 

implementation. All this work traditionally falls within the purview of the State DOT. That is 

why this assessment focuses on the State DOTs and on State Safety Data Systems. That focus 

does not end with the State’s DOTs, however. Managing safety performance of an entire State’s 

roadway network involves multiple business units and agencies, some responsible for 

engineering decisions at the local and regional levels, and others with responsibility for 

behavior, enforcement, courts, and injury surveillance. All of them need reliable data. Within 

FHWA, the agency’s safety improvement efforts involve multiple areas including Operations, 

Infrastructure, Asset Management, Policy, and Planning. Additionally, within the United States 

Department of Transportation (USDOT), safety improvement efforts involve the National 

Highway Traffic Safety Administration (NHTSA), the Federal Motor Carrier Safety 

Administration (FMCSA), and the Research and Innovative Technology Administration (RITA). 

This report is meant to be inclusive of FHWA’s efforts for improving safety data. It also 

incorporates some aspects of other USDOT efforts (such as crash data improvement).

Throughout, the reader is invited to expand their own appreciation for the inter-connected 

nature of safety management and to consider ways to increase safety data capabilities while 

including State and local agency partners across the broad spectrum of engineering, education, 

enforcement, and emergency response.

Data Capabilities Peer Exchanges

Following completion of this assessment project, FHWA will invite States to participate in one 

of three Peer-to-Peer (P2P) exchanges based on the results of their assessment. In these 

meetings, participants will exchange ideas and provide information about their practices that 

other States can adopt and adapt. The P2P exchanges will help strengthen State safety data 

action plans, leadership plans, and strategies. 

Each P2P exchange will have differences in composition and theme. The needs and areas of 

interest identified by the participating States will determine the peer exchange topics. FHWA 
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will invite experts and States that have strengths and needs within each area. All peer exchanges 

will touch on the best practices, approaches, and techniques in the following areas:

• Data Collection and Capability Maturity Models covering Areas 1 and 5 of the capability 

assessment, plus the overall assessment process. 

• Data Management and Integration covering Areas 3 and 4 of the capability assessment. 

• Data Analysis and Tools covering Area 2 of the capability assessment. 

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CHAPTER 2. OBJECTIVE

The purpose of this project was to conduct a consistent and thorough roadway safety data 

capabilities assessment and record goals for each of the States. The resulting information is 

useful in two ways. First, States can use their specifically created Action Plan to promote 

activities that will increase their capability levels in data, analysis, governance, integration, and 

safety performance management. Second, FHWA can use the updated status and goals to 

identify areas of common interests and needs among the States. This will help FHWA decide 

how best to support States and meet the shared needs. This will include technical assistance, 

training, noteworthy practices, case studies, guidance, and peer exchanges over the next several 

years. 

The three primary objectives of this project are:

• Update the assessment process, forms, and scoring, while adding the new Safety 

Performance Management Area.

• Quantify States’ status and goals and help them identify critical gaps and potential actions 

to achieve those goals. 

• Identify safety programs, guidance, and technical assistance that States could use to 

reach their goals and promote data-driven highway safety management. 

The assessment teams pre-filled the questionnaires and provided the results to their States. The 

assessors asked States to complete and correct the questionnaires, and then review the final 

answers with the consultant teams. The assessors then scored the final questionnaire answers 

using a CMM process, giving each State an objective review of their capabilities. The States met 

with their assigned assessment team and decided on goals for each area of the CMM and 

discussed the best ways for the State to meet those goals. This final meeting resulted in an 

Action Plan for each State that shows status, goals, and methods for reaching those goals. The 

CMM and Action Plans have five focus areas, each with appropriate sub-areas: 

• Area 1: Roadway Safety Data Collection / Technical Standards – This section 

developed two types of capability scores: assessing the data, and assessing the 

management practices applied to the data. Unlike the first-round survey, which 

concentrated mainly on roadway inventory data, the second-round assessment

addressed roadway inventory, traffic volume, and crash data. States provided 

information on the completeness, timeliness, accuracy, and uniformity of their safety 

data, and received scores based on these quality attributes (data scores) and their 

management of the data (CMM scores).
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• Area 2: Data Analysis Tools and Uses – This section focused on how well States 

are implementing advanced analytic methods and using the various tools that support 

those methods. States provided information on network screening (method, data, and 

coverage); diagnosis; countermeasure selection; evaluation; and accessibility. 

• Area 3: Data Management and Data Governance – This section focused on 

States’ data management and data governance roles & responsibilities, policies, and 

processes.

• Area 4: Data Integration – This section focused on integration among the six core 

traffic records data sets: crash, roadway, driver, vehicle, citation and adjudication, and 

injury surveillance data. 

• Area 5 (new): Safety Performance Management – this section focused on States’ 

safety performance management. States received scores on safety performance 

measurement data, safety performance management processes, and safety performance 

analysis capabilities. 
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CHAPTER 3. METHODOLOGY

The project team developed and conducted a capability assessment for each of the 52 States. 

The capability assessment uses a methodology that is flexible in working with States; but, 

consistent in process to provide a fair and accurate assessment across the States reviewed. 

After testing the process through four pilot States, the assessment in each State followed a 

three-stage process: pre-assessment, assessment, and post assessment. The following sections 

describe the CMM as it applies to this project, the development of the assessment process 

through the pilot phase, some of the challenges addressed, and the resulting three-stage 

process. The project’s technical advisory group reviewed all materials and provided comments, 

which the assessment team addressed in final versions of each product.

ASSESSMENT QUESTIONNAIRE UPDATE

Technical advisors from USDOT, States, and consultants updated the first-round questionnaire. 

The changes included:

• Rewording questions for clarity and focus.

• Expanding the scope of Area 1 to include crash and traffic volume data.

• Adding items and specificity to Area 2’s list of analyses and tools.

• Creating a new section (Area 5) on Safety Performance Management.

CAPABILITY MATURITY MODEL UPDATE AND EXPANSION

This first round assessment used a custom CMM developed specifically for that project. The 

second assessment used an updated CMM to allow for separate scoring of data and 

management practices in Area 1: Data Collection/Technical Standard. The second assessment 

also included a CMM for Area 5-Safety Performance Management. 

Maturity Levels

• Initial / Ad hoc: The organization does not possess a stable implementation 

environment and the safety data collection, management (entering/coding, processing, 

and evaluating) and maintenance process is ‘ad hoc’ with no interconnection within the 

organization. There is no plan for interoperability or expandability.
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• Repeatable: The results of previous projects and the demands of the current project 

drive activities and actions. Individual managers decide what to do on a case-by-case 

basis during individual projects.

• Defined: The organization documents the process rather than on a per-project basis. 

The organization's standards tie to an adopted strategy and this guidance determines 

project outcomes.

• Managed: Process management initializes and supervises individual projects. Through 

safety performance management, processes are predictable, and the organization can 

develop rules and conditions regarding the quality of the products and processes.

• Optimizing: The whole organization focuses on continuous improvement. The 

organization possesses the means to detect weaknesses and to strengthen areas of 

concern proactively.

ASSESSMENT AREAS

The assessment focused on identifying a capability level for each State within each of the five 

areas. 

The following sections present an overview of each area in the assessment. 

Area 1: Roadway Inventory Data Collection/Technical Standards

This area covered four elements: completeness, timeliness, accuracy, and uniformity. Assessors 

scored each area separately for data capabilities and data management as follows:

• 1A Completeness (data): addressing perceived completeness of the State’s safety data.

• 1A Completeness (CMM): addressing completeness quality control processes. 

• 1B Timeliness (data): addressing perceived timeliness of the State’s safety data.

• 1B Timeliness (CMM): addressing timeliness quality control processes. 

• 1C Accuracy (data): addressing perceived accuracy of the State’s safety data.

• 1C Accuracy (CMM): addressing accuracy quality control processes. 

• 1D Uniformity (data): addressing perceived uniformity of the State’s safety data.

• 1D Uniformity (CMM): addressing uniformity quality control processes. 
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Area 2: Data Analysis Tools and Uses

This area covered five steps in the safety management process: network screening, diagnosis, 

countermeasure selection, evaluation, and accessibility. States received CMM scores in the 

following:

• 2A Network Screening:

o Method: addressing the analytic methods used for network screening.

o Data: addressing the data available for network screening.

o Coverage: addressing the application of network screening to all public roads.

• 2B Diagnosis: addressing the processes for diagnosing safety conditions.

• 2C: Countermeasure Selection: addressing the processes for choosing treatments.

• 2D: Evaluation: addressing the processes for assessing effectiveness of treatments after 

implementation.

• 2E Accessibility: addressing the availability of safety analyses and tools to a wide range of 

users.

Area 3: Data Management and Governance

This area covers three elements as follows:

• 3A Roles and Responsibilities: addressing the defined duties of participants in the data 

governance process.

• 3B Policies: addressing the formal policies for pursuing data governance.

• 3C Processes: addressing the formal processes for data governance. 

Area 4: Data Integration

This area covered three aspects of data integration as follows:

• 4A Data Integration: addressing the capabilities to integrate safety data including crash, 

roadway, driver, vehicle, citation and adjudication, and injury surveillance information.
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• 4B Expandability: addressing the capabilities for adding new data sources and combining 

State and local data sources to an integrated dataset.

• 4C Spatial Data Integration: addressing the use of GIS platforms to facilitate spatial data 

combination and analysis.

Area 5: Safety Performance Management

This area covered overall safety performance management and safety performance management 

coordination as follows:

• 5A Safety Performance Management:

o Data: addressing the quality and availability of data needed for safety 

performance management.

o Processes: addressing the formal processes used to conduct safety performance 

management.

o Analysis Capabilities: addressing the ability to conduct analysis in support of 

safety performance management.

• 5B Safety Performance Management Coordination: addressing the methods of 

interagency coordination including State, local, tribal, and regional participants.

PROCESS

The assessment team pilot tested the updated questionnaire in four States: Arizona, Louisiana, 

Missouri, and Washington State. The team updated the questionnaire and process and then 

scheduled full assessments with the remaining States. Each State reviewed the pre-filled 

questionnaire provided by their assessment team and participated in three meetings with that 

team. The first meeting was a review of the questionnaire to finalize all answers. The second 

meeting was scheduled after the assessment team had scored the State’s answers using the 

updated CMM. At the second meeting, the State reviewed and accepted the scores on the 

CMM and decided what CMM levels they would like to achieve over the next five years. During 

the third meeting, the assessment team met to provide an Action Plan to each State describing 

how the State could achieve their target CMM levels.

Final Steps

During the three meetings (kickoff, assessment, and Action Plan), the project leaders gave each 

State information about peer exchanges planned as the final phase of this project. Based on 
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States’ scores and Action Plans, FHWA identified hosts and participating States in three peer 

exchanges held in 2019. 

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CHAPTER 4. FINDINGS

This section presents the national averages and highlights of the State assessments. These 

results come directly from conversations between the States’ participants and the lead 

assessors, the completed questionnaires, as well as the Action Plans. See Appendix A for 

additional results.

FINDINGS ABOUT THE OVERALL ASSESSMENT PROCESS

The assessment process worked well. Those States that had previously worked with Federal 

agencies on other data-related efforts (e.g., the Crash Data Improvement Program (CDIP) 

Roadway Data Improvement Program (RDIP), and various technical assistance projects) 

discussed goals and specific actions arising from those efforts. 

One of the overwhelming findings, related to both data capability and project approach, is that 

each State is unique. Various divisions, districts, and regions within a State may operate in an 

independent and decentralized manner to collect, use, and manage data. Coupled with the fact 

that there are multiple data files managed by different business units within a DOT as well as 

agencies outside the DOT, it is unlikely that one answer from the questionnaire would 

adequately capture the State’s true capability. In these cases, the questionnaire alone would not 

yield a consistent and thorough assessment. The conversation about the answers to the 

questionnaire proved extremely valuable to the objectives of the assessment. One of the most 

important changes implemented for this second-round assessment was the addition of 

comment fields for every question in the questionnaire and every score in the CMM and Action 

Plan worksheets. 

States found value in assembling multiple internal agencies and professionals together to 

respond to the assessment questions. This process led several States to strengthen their 

interagency coordination and data ties. The assessment process will lead to further 

conversations at the State level.

GENERAL ASSESSMENT FINDINGS

• In general, States want to improve their current roadway safety data capabilities and 

take the necessary steps to achieve their desired level. Most States are currently in the 

process of implementing data improvement projects, where their desired level is higher, 

and following a plan for improvement, in one form or another. FHWA has access to 

each States desired levels through the States’ Action Plans.
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• States indicated the need for a focused USDOT effort to increase awareness of the 

technical assistance, tools, training, and other resources available to improve their 

roadway safety data.

• In some States, the organizational structure, and relationships across State agencies and 

with localities impede data quality improvement efforts. A more inclusive data 

governance process with assigned roles for local, regional, and tribal participants offers a 

solution.

• Data quality management practices lag well behind the perceived quality of the data. The 

result is a lack of governance and specifically of data quality performance measurement. 

In short, States appear satisfied with the quality of their core safety data, but they do not 

quantify its quality attributes and thus are not certain of the precise level of 

completeness, timeliness, accuracy, or uniformity achieved. The CDIP and RDIP 

programs help States develop their own measurement systems now. This can be a key 

step in data governance as well since that is an effective process for establishing and 

enforcing data standards. 

• States are expanding their analytic capabilities in most areas of safety management. The 

States plan to improve safety analytic capabilities, especially for network screening. The 

most frequently cited examples of how States plan to increase their safety management

and analysis capabilities center on adopting modern tools that support decision making. 

These include the latest generation of software from GIS vendors as well as tools like 

AASHTOWare Safety AnalystTM and the Interactive Highway Safety Design Model 

(IHSDM). 

KEY FINDINGS FOR AREA 1: DATA COLLECTION AND DATA QUALITY

Figure 1 shows the national average capability scores and goals for Data Quality. For each of 

the four data quality attributes (completeness, timeliness, accuracy, and uniformity) each State 

received two sets of scores. The data scores reflect the perceived level of data quality achieved. 

The CMM scores reflect the States’ capabilities for formal management of that data quality 

attribute. The data quality scores are consistently higher than the data management scores. On 

average, the States set goals for improving both data quality and data quality management.

Figure 1 also shows the scores from the first assessment in 2012. In that assessment, the data 

and CMM scores were combined for each data quality attribute. The 2012 data is shown 

alongside the second assessment’s data scores for each attribute. The comparison shows that 

scores rose between the first and second assessments, with the largest gain in completeness.
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Figure 1. Radar chart of national average scores and goals for data quality. 

The following highlights some of the key findings under Data Collection.

• On average, States set goals to improve both data quality and their data management 

practices. Planned improvements in CMM scores aim for more than one full point 

improvement in data management capabilities. The data quality goals are near or above 

level 4. 

• Perceived data quality is consistently higher than the States’ capabilities for managing 

data quality. This is not to say that those perceptions are inaccurate or indicative of an 

inflated score for data quality. The growing use of automated systems for data collection 

is raising the levels of quality even in the absence of formal data quality measurement or 

data governance. States are implementing automated methods for collecting roadway 

inventory data. Field data collection systems for crash reporting are common as well. 

• Elements collected at the State level are not the same level of detail as those on locallymaintained roadways or functionally-classified local roads. The MIRE FDE clarifies for 

States the recommended minimum set of data elements for each class of roadway. 
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However, even with this guidance, the State-maintained portion of the network is more 

likely to have more complete and timely data. States are making progress toward 

collecting the appropriate MIRE FDE for each class of roadway. States have provided 

plans for their collection of the MIRE FDE by 2026 and several states are actively 

engaged in collecting an expanded set of data elements. Examples include States using 

automated collection systems to gather roadway inventory and asset management data 

on the entire network and tying that information to their linear referencing system 

through a GIS.

• Intersection inventory systems lag the segment-oriented data. This situation is improving 

based on some GIS-based automated methods for creating an intersection inventory.

• Data quality measurements are more robust for traffic count and crash data than for 

roadway inventory data.

Suggested Actions

• Help States move forward with cost-effective, accurate, and innovative data collection 

practices. Specifically, States need examples of how to fund, process, and extract 

roadway inventory items, especially with respect to automated methods for collection 

and extraction and for intersections and interchanges. Arranging peer-to-peer 

information sharing, example contracts, and direct technical assistance are three ways 

that continue to help States in recent years with MIRE roadway data collection efforts.

• States need help deciding how best to formalize their data quality management 

practices. This is best done in the context of a formal data governance process which, as 

seen in Area 3’s results, States have a good understanding of their needs and capabilities.

• Develop guidance for collecting intersection, curve, and grade inventory data, including 

automated methods for extracting the information and methods of quality control.

• Encourage States to use the CDIP, RDIP, and technical assistance programs.

KEY FINDINGS FOR AREA 2: DATA ANALYSIS

Figure 2 shows the national average scores and goals for Safety Analysis Capabilities. Except for 

countermeasure selection, the national average is high for each facet of safety analysis. States 

are using advanced methods for network screening and diagnosis. They are making the analyses 

accessible to decision makers across multiple agencies and jurisdictions. Countermeasure 

selection is clearly lagging the other data analysis areas. 

Figure 2 also shows the first capabilities assessment results for comparison. The largest 

improvements by the second assessment were in Network Screening: Method and Data, and in 
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Accessibility. The other scores are essentially unchanged between the two assessments, 

although there does appear to be a slight drop in the score for Network Screening: Coverage.

Figure 2. Radar chart of national average scores and goals for safety analysis. 

The following highlights some of the key findings under Data Analysis.

• Use of data analysis tools and support varies from State to State, with some States using 

State-specific tools and some using national tools. There are several States already using 

advanced analytic methods and tools, including AASHTOWare Safety AnalystTM. Some 

States are pursuing their own custom analytic tools. The Highway Safety Manual is 

serving as a guide for States’ use of advanced analytic techniques and many States are 

developing State-specific Safety Performance Functions (SPFs) and Crash Modification 

Factors (CMFs). 

• Most States are using the Crash Modification Factor (CMF) Clearinghouse. Nonetheless, 

average scores for countermeasure selection lag the other safety analysis areas. This is 

due to States not adopting the CMF methodology in all relevant cases, most notably 

local roads. 
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• Network screening and countermeasure selection require complete and integrated

roadway inventory, traffic volume, and crash data. States are using GIS to integrate 

spatial data (as shown in Area 4). This supports more efficient use of the advanced 

analytic methods and tools. Five States specifically mentioned using systemic safety 

methods to identify countermeasures that they can apply to any location matching a 

specific configuration of attributes.

Suggested Actions

• Continue to promote advanced safety analysis methods and tools with guidance, 

training, and technical assistance. Specific suggestions include:

o Continue promoting States’ and local agencies’ use of the CMF clearinghouse for 

a broad range of countermeasure selection opportunities. This could include 

webinars, case studies, peer exchanges, and training.

o Develop technical assistance for States interested in improving any of the five 

subareas of data analysis. Network screening coverage, countermeasure 

selection, and evaluation show the largest needs; however, States are interested 

in improving in all data analysis areas. This suggestion could include hands-on 

training and assistance in analysis methods and how to use the analyses in 

decision-making.

o Continue and expand guidance and training on systemic safety analysis and 

decision making.

• Work with States to find ways to incorporate advanced safety analysis (and, 

countermeasure selection methods) into a broader range of DOT business processes. In 

particular, DOTs (and their partner agencies) could use guidance and assistance bringing 

safety analysis into the standard operating procedures in planning, design, maintenance, 

and asset management.

• Consider a continuous tracking of States’ analysis improvements to identify and address 

the circumstances when implementations stall or fail. This is especially important to 

consider with respect to software tool implementations that can take several years and 

may hit roadblocks along the way.

KEY FINDINGS FOR AREA 3: DATA MANAGEMENT & GOVERNANCE

Figure 3 shows the national average scores and goals for Data Management and Governance. 

Compared to other areas of the assessment, scores in this area are low. This indicates that 

States are challenged, especially with formalizing policies and roles and responsibilities in a data 

governance framework. Scores are slightly higher for processes, indicating that States are 

implementing some aspects of data governance, but statewide, institutionalized, and enforced 
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data governance is not the norm. The goals indicate that States are interested in strengthening 

their data management and governance practices. 

Figure 3 also shows the first assessment results for comparison. In the Data Management and 

Governance area, two scores dropped between the first and second assessments: Roles and 

Responsibilities, and Policies. The third score, Processes, improved from 2.9 to 3.25 nationally. 

The two scores may have dropped because, in 2012, States over-estimated their capabilities in 

data governance, reporting that they had these pieces in place. In fact, most States in 2012 had 

not engaged in formal data governance and the concepts had to be defined for practitioners 

during the assessment meetings. By 2018, most States had extensive experience with data 

governance and the scores probably reflect this more accurate understanding of what is 

involved.

Figure 3. Radar chart of national average scores and goals for data management

and governance. 

The following highlights some of the key findings under Data Management and Governance. 

• Compared to the 2012 capabilities assessment, scores in this area slipped lower. This 

likely indicates a more complete understanding of data management and governance. 

Unlike the first-round assessment, State safety engineers generally understand what data 

governance is and how far their State is from optimal performance. 
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• States are engaged in formal data governance now, but very few are at the highest levels 

on the CMM. Many of the implementations affecting safety data are quite recent. Most 

States are not yet comfortable with the idea of going outside the DOT to include local 

and tribal representatives in data governance committees.

• Enforcing data standards is difficult. States are recognizing the need, but have a 

challenging time deciding how to work with the local agencies. 

• The safety engineering data users often do not own the systems they rely on to do their 

jobs. In that environment, users are often the strongest supporters of improved data 

governance. Within State DOTs, the agency’s subject matter experts are getting 

involved; however, the IT (information technology) and GIS groups are concerned that 

implementing more formal data governance will stretch their staff and, at least at the 

beginning, pull too many resources away from high-priority efforts like implementing an 

enterprise GIS or advanced analytic system. Data governance helps those 

implementations work better, but some States view it as slowing progress. 

• Data system documentation is incomplete. This makes it difficult to establish and 

enforce data standards. It also impedes system improvements and makes it more likely 

that application development will stall for lack of a clear definition. As States move to 

enterprise GIS solutions the interactions among IT, GIS, and business unit experts is 

critical to success.

Suggested Actions

• The planned peer exchanges for this project will include at least one session covering 

data governance practices. The higher-performing States’ examples will be useful to 

many of the States who set ambitious goals on the CMM. Sharing the results of the 2019 

peer exchanges and continuing to offer relevant peer exchanges in future years will 

meet many States’ needs for shared experience and knowledge.

• Continue to support data governance efforts with technical assistance and guidance. 

State DOTs need help identifying the right time and circumstances for bringing local and 

tribal agencies into the data governance process.

• Develop case studies, guidance, and technical assistance on enforcing data standards 

among multiple agencies. Specifically, States and their local and regional partners need to 

understand how they benefit from enforced standards. As indicated in Area 1, this effort 

should include formal data quality measurement and management.
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KEY FINDINGS FOR AREA 4: DATA INTEGRATION

Figure 4 shows the national average scores and goals for Data Integration. The scores for 

Spatial Data Integration are the highest of the three. This is a direct result of expanded GIS 

implementation for safety data integration and analysis. 

Figure 4 also shows the first assessment average scores for comparison. For each assessment 

component in Data Integration, there were small improvements between the first and second 

assessments. The largest gains were in Spatial Data Integration.

Figure 4. Radar chart of national average scores and goals for data integration. 

The following highlights some of the key findings under Data Integration: 

• States are using GIS as a key tool for integration of roadway inventory, traffic, and crash 

data. Integrating other traffic safety-relevant data from driver, vehicle, citation and 

adjudication, and injury surveillance sources are lagging.

• Business units outside of the State DOT safety sections are more concerned with

expanding data integration beyond the crash, roadway, and traffic data. Traffic Records 

Coordinating Committees (TRCCs) may be an appropriate venue for those discussions.
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• States are interested in expanding all forms of data integration. This is a major change

since the first-round capabilities assessment and may indicate a growing awareness of

the value of merged data, especially for including outcome data from the injury

surveillance system.

Suggested Actions

• Expand guidance for data integration to include high-value items like crash outcome

analysis and a behavioral focus as part of safety planning and countermeasures. Highvalue, in this case, refers to examples of expanded analyses and deeper insights that

States have gained using integrated crash and injury surveillance data. One potential

benefit is that the economic appraisal analyses include State-specific costs of treatment

for those injured in crashes—a method that corrects for some of the inherent

limitations in using the at-scene data recorded by law enforcement immediately after a

crash. This could be done in conjunction with NHTSA. NHTSA and FHWA both have

relevant experience to share with States on how to prioritize, finance, and manage a

data governance effort.

• Consider peer exchanges and case studies examining a more holistic or safe systems

approach to safety that would engage the engineering, enforcement, education, and

emergency response (4-Es) communities.

• Continue promoting the all public roads safety data integration efforts, especially

through advances in spatial data integration and GIS. This is an ongoing project by

FHWA and includes a recently funded pooled fund study on Enterprise GIS for

Transportation.

KEY FINDINGS FOR AREA 5: SAFETY PERFORMANCE MANAGEMENT

Figure 5 shows the national average scores and goals for Safety Performance Management. The 

lowest score was for Safety Performance Management Analysis Capabilities. The highest score 

was for Coordination. This reflects the benefits of States’ efforts to improve data for decision 

making. Since the Safety Performance Management area did not appear in the first-round 

capabilities assessment, there are no comparisons to make with the past. The results in Area 5 

fit well with those of Areas 1 through 4 showing analysis capabilities lagging the data capabilities. 

It is somewhat surprising that Coordination scores are high in this area, but that is likely a 

reflection of the requirements for States to include local agencies in their safety performance 

management planning. Local plans for safety improvement targets must align with the State-level 

plans, so there is mandatory coordination. Also, States have well established safety planning 

processes that typically include some coordination within the DOT and with groups like the 

State TRCC and SHSP stakeholders. 
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Figure 5. Radar chart of national average scores and goals for safety performance 

management. 

The following highlights some of the key findings under Safety Performance Management:

• The relatively low scores for safety performance management analytic capabilities 

mirrors the gaps in States’ capabilities in network screening for all public roads and 

advanced countermeasure selection processes. Without strong safety analysis for local 

roads, the States have limitations on their ability to plan and monitor performance.

• Coordination has a high capability score now. It is possible, however, that without local 

and regional involvement in data governance, States will find it difficult to reach their 

goals for improved coordination. Coordinating on annual safety performance targets 

has made this a priority, but full integration of MPOs and local agencies into the data 

management process could stall efforts to coordinate safety performance management. 
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Suggested Actions

• Develop guidance for effective and inclusive safety performance management. Training

and technical assistance could also help States reach their safety performance

management targets.

• Help States prioritize safety performance management analysis as part of the data

governance and analysis improvement efforts. This could take the form of guidance, case

studies, and peer exchanges.

NATIONAL GAPS SUMMARY 

Relatively high scores nationally appear in data accuracy and uniformity; network screening 

methods and data; safety diagnosis, and safety evaluation; spatial data integration and 

expandability; and safety performance management coordination and data.

These comparisons suggest areas for increased focus by States and FHWA. There are multiple

resources that States can use to guide their improvement efforts. These include:

• FHWA’s data prioritization guide (FHWA-SA-17-032) provides a practical method for

deciding how best to meet important safety data collection needs. The data integration

(FHWA-SA-16-118) and data business planning (FHWA-SA-17-047) guides include

methods for a data gaps analysis planning data collection to fill the gaps.

• CDIP and RDIP efforts that focus on data quality management of crash and roadway

data, respectively. These programs could help States improve the CMM scores for

completeness, timeliness, accuracy, and uniformity. Both programs provide suggested

data quality measurements and describe noteworthy data management practices.

• The RSDP Toolbox lists analytic tools and resources for all facets of safety data

management. States can use the toolbox to support improvements in any area. FHWA

could prioritize advanced methods of countermeasure selection to help States improve

in that area. The CMF Clearinghouse continues to expand and provide well

documented CMFs. States could use help in applying CMFs more broadly as part of

their safety business practices. FHWA developed a set of instructional videos

encouraging expanded use of CMFs. These are available through the RSDP website.

• Data management and governance technical assistance is available through the FHWA

Office of Safety. States can ask for help developing a Safety Data Business Plan or

through separate technical assistance with data governance implementation. FHWA

may wish to prioritize data governance policy development and procedures for
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establishing formal roles and responsibilities as these scored lower than the actual 

governance processes.

• FHWA offers data integration technical assistance for the core datasets of crash,

roadway, and traffic volumes. The assistance focuses on integrating State, tribal, and

local data into a resource that supports advanced safety analysis and decision making.

NHTSA and FHWA both offer technical assistance to States that can go beyond the

core safety data sets. Within the DOT setting, integrating safety with asset and other

spatial data is part of the enterprise GIS pooled fund study. Beyond the DOT,

integrating crash data with injury surveillance and other traffic records system

components is part of NHTSA’s traffic records assessments, the CDIP, and GO Team

efforts.

• FHWA’s guidance and safety program technical assistance on safety performance

management is also available to States.

Figure 6 on the next page shows a side-by-side comparison of the national average capability 

scores for all sections of the assessment. This view makes it easy to spot gaps in State 

capabilities. The lowest scores are in CMM data quality management, even while States scored 

relatively high on data itself. Other low scores appear in countermeasure selection; data 

management and governance policies and roles and responsibilities; and in safety performance 

management analysis capabilities. Scores for the first round of the assessment are also noted in 

the chart where appropriate. The safety performance management scores (Area 5) were not 

collected in the first round, so no prior scores are indicated.
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Figure 6. National average baseline for State data capability.

3.29 (2012 - 2.6)

2.33

3.38 (2012 - 3.3)

2.13

3.94 (2012 - 3.7)

2.4

3.94 (2012 - 3.8)

2.25

3.94 (2012 - 3.6)

3.79 (2012 - 3.5)

3.25 (2012 - 3.4)

3.96 (2012 - 4)

2.79 (2012 - 2.8)

3.65 (2012 - 3.6)

4.33 (2012 - 4)

2.46 (2012 - 3.1)

2.31 (2012 - 2.5)

3.25 (2012 - 2.9)

3.19 (2012 - 3.1)

3.75 (2012 - 3.7)

3.83 (2012 - 3.6)

3.71

3.44

2.96

3.82

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

1A: Completeness_Data

1A: Completeness_CMM

1B: Timeliness_Data

1B: Timeliness_CMM

1C: Accuracy_Data

1C: Accuracy_CMM

1D: Uniformity_Data

1D_Uniformity_CMM

2A: Network Screening: Method

2A: Network Screening: Data

2A: Network Screening: Coverage

2B: Diagnosis

2C: Countermeasure Selection

2D: Evaluation

2E: Accessibility

3A: Data Management & Governance: Roles & Resp

3B: Data Management & Governance: Policies

3C: Data Management & Governance: Processes

4A: Data Integration

4B: Expandability

4C: Spatial Data Integration

5A: PM_Data

5A: PM_Processes

5A: PM_Analysis Capabilities

5B: PM_Coordination

DATA CAPABILITY ELEMENT

Capability Level
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CHAPTER 5. CONCLUSIONS

This effort measured roadway safety data capability in each of the 50 States, plus the District of 

Columbia and Puerto Rico. Experience from the first-round capabilities assessment showed that 

the results are useful to the States and to FHWA. In the second-round capabilities assessment, 

FHWA and the States asked that the Action Plans be made more specific to the plans that 

States themselves developed in response to their CMM scores. The assessment teams worked 

to tailor the Action Plans to the activities that the State is likely to perform.

The second-round assessment will be useful to FHWA. Following the first-round assessment in 

2012, FHWA used the results to identify the most pressing needs and develop new programs 

or expand existing programs. This led directly to resources like the RSDP toolbox, multiple 

case studies and noteworthy practice descriptions, web-based training, guidance on data 

integration and data governance, and related technical assistance. The updated assessment data 

will serve a similar purpose for the FHWA Office of Safety in the future. FHWA plans peer 

exchanges to address key needs identified in the assessment. FHWA also plans to use the 

information from this second-round assessment to refine and expand the resources available 

through the RSDP and promote States’ use of the information, guidance, technical assistance, 

and tools included in the RSDP. 

The appendix to this report includes detailed information comparing the first and second round 

assessment results. There are many areas of improvement and some surprising results 

indicating a lowering of capabilities—notably in data governance. This unexpected result is most 

likely due to the growing understanding of data governance among the States since 2012. They 

have acknowledged the gap between ideal practices and their present state. The true situation 

is that the scores for data governance capabilities were likely too high in 2012, not that States 

have somehow failed to maintain the efforts they had implemented in the past.

States identified the ways they hope to advance their data capability. Many had 

recommendations for FHWA as well. Ultimately, FHWA could tailor the approach to each 

State, through consideration of a “Focused Approach to Safety” category for data 

improvements or other methods, to implement their Roadway Safety Data Action Plans in 

alignment with their safety goals outlined in their SHSPs or TRSPs. While this initiative looked 

at the State-level data capability, parallel work has revealed that safety data capability at the 

regional MPO and local level may be quite different, and in a few cases, more robust. States are 

seeing the value of coordinating with local agencies on data collection and safety performance 

management. They will need help and assistance involving local agencies in data governance.

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APPENDIX A – STATE MATURITY LEVELS

One of the more useful components of this Assessment will be the determination of capability 

maturity at the State and national levels. The State Maturity Levels are based on the principles 

of the “Capability Maturity Model” – CMM. This approach provides the project team the ability 

to subjectively assess the States. The principles of the CMM place each State into “capability 

categories.” These categories are based on a five-point scale from less to more mature. The 

five maturity levels used in this analysis are:

• Initial / Ad hoc (Capability Level 1): The organization does not possess a stable

implementation environment and the safety data collection, management

(entering/coding, processing, and evaluating) and maintenance process is ‘ad hoc’ with

no interconnection within the organization. There is no plan for interoperability or

expandability.

• Repeatable (Capability Level 2): The results of previous projects and the demands

of the current project drive activities and actions. Individual managers decide what to do

on a case-by-case basis during individual projects.

• Defined (Capability Level 3): The organization documents the process rather than

on a per-project basis. The organization's standards tie to an adopted strategy and this

guidance determines project outcomes.

• Managed (Capability Level 4): Process management starts and supervises individual

projects. Through safety performance management, processes are predictable, and the

organization can develop rules and conditions regarding the quality of the products and

processes.

• Optimizing (Capability Level 5): The whole organization focuses on the continuous

improvement. The organization possesses the means to detect weaknesses and to

strengthen areas of concern proactively.
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Figure 7. Capability maturity level distribution for data collection: Completeness. 

Figure 8. Capability maturity level distribution for data collection: Timeliness. 
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Figure 9. Capability maturity level for data collection: Accuracy. 

Figure 10. Capability maturity level distribution for data collection: 

Uniformity/Consistency. 
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Figure 11. Capability maturity level distribution for data analysis: Network 

Screening (Data). 

Figure 12. Capability maturity level distribution for data analysis: Network 

Screening (Methodology). 
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Figure 13. Capability maturity level distribution for data analysis: Network 

Screening (Coverage). 

Figure 14. Capability maturity level distribution for data analysis: Diagnosis. 
SECOND U.S. ROADWAY SAFETY DATA CAPABILITIES ASSESSMENT FINAL REPORT

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Figure 15. Capability maturity level distribution for data analysis: Countermeasure 

Selection. 

Figure 16. Capability maturity level distribution for data analysis: Evaluation. 
SECOND U.S. ROADWAY SAFETY DATA CAPABILITIES ASSESSMENT FINAL REPORT

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Figure 17. Capability maturity level distribution for data analysis: Accessibility. 

Figure 18. Capability maturity level distribution for data management & 

governance: Roles and Responsibilities. 
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Figure 19. Capability maturity level distribution for data management & 

governance: Policies. 

Figure 20. Capability maturity level distribution for data management: Technology. 
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Figure 21. Capability maturity level distribution for data integration: General 

Integration. 

Figure 22. Capability maturity level distribution for data integration: Expandability. 
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Figure 23. Capability maturity level distribution for data integration: Spatial Data 

Integration. 

Figure 24. Capability maturity level distribution for safety performance 

management: Data. 
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Figure 25. Capability maturity level distribution for safety performance 

management: Processes. 

Figure 26. Capability maturity level distribution for safety performance 

management: Analysis Capabilities. 
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Figure 27. Capability maturity level distribution for safety performance 

management: Coordination.
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The following table presents the results of each maturity level for the States that completed their assessments. States are 

de-identified and randomized in this presentation.

Key for Table 1: 1 = Initial / Ad hoc 2 = Repeatable 3 = Defined 4 = Managed 5 = Optimizing 

Table 1. State capability level scores. NOTE: 3 States were assigned the national average because of missing data

STATE / JURISDICTION

AREA 1: 

DATA COLLECTION

AREA 2: 

DATA ANALYSIS

AREA 3: DATA 

MANAGEMENT

AREA 4: DATA 

INTEROPERABILITY

AREA 5: PERFORMANCE 

MANAGEMENT

Completeness DataCompleteness CMMTimeliness DataTimeliness CMMAccuracy DataAccuracy CMMUniformity DataUniformity CMM

Network 

Screening

DiagnosisCountermeasure SelectionEvaluationAccessibility

Data Mgt & Gov

Data IntegrationExpandabilitySpatial Data IntegrationPM DataPM ProcessesAnalysis CapabilitiesPM CoordinationMethodDataCoverageRoles & RespPoliciesProcesses

1A 1A 1B 1B 1C 1C 1D 1D 2A 2A 2A 2B 2C 2D 2E 3A 3B 3C 4A 4B 4C 5A 5A 5A 5B

1 4 3 3 3 4 3 5 3 5 4 3 5 3 4 5 4 3 4 4 4 4 4 4 4 4 

2 5 3 5 3 4 3 5 3 3 4 5 3 4 2 5 3 2 5 3 5 5 5 5 2 5 

3 3 1 1 1 2 1 2 1 2 4 3 2 3 3 2 2 2 2 2 4 3 3 3 2 2 

4 3 2 4 1 4 2 5 1 4 4 3 4 2 5 5 2 1 2 3 3 4 3 1 2 4 

5 5 3 3 2 4 3 4 3 4 3 4 5 4 3 4 2 1 3 4 4 5 4 4 3 5 

6 2 1 3 1 2 1 2 1 2 3 2 3 2 2 4 2 2 1 1 2 2 2 2 2 2 

7 3 3 5 3 4 3 5 3 4 4 2 4 4 3 4 2 2 4 3 4 4 4 3 2 2 

8 4 3 3 3 4 3 5 3 5 4 3 5 2 5 5 4 2 3 3 4 4 4 4 5 4 

9 3 3 3 4 5 4 4 2 4 4 3 4 2 4 5 2 2 4 3 4 4 4 3 3 4 

10 4 3 2 2 5 4 3 2 5 4 4 4 2 3 4 3 2 4 4 4 3 3 3 3 3 

11 2 1 4 2 4 1 3 1 2 3 2 3 2 4 3 3 3 3 2 1 1 3 1 3 4 

12 4 1 4 1 3 1 3 2 3 4 3 4 2 4 5 4 3 3 4 3 3 4 4 3 4 

13 4 3 4 3 5 3 5 3 5 4 3 4 3 4 5 1 2 2 3 3 4 4 4 4 4 

14 4 1 2 1 3 1 4 1 5 4 3 4 2 5 2 4 3 4 4 4 4 3 3 3 2

15 4 3 3 3 3 3 4 3 5 4 4 5 4 5 5 3 2 2 4 4 4 4 5 3 5 

16 2 1 3 1 4 1 3 1 4 4 2 4 2 4 5 2 2 3 3 4 4 2 3 3 4 

17 4 3 3 3 5 3 4 3 5 4 3 5 4 5 5 4 3 4 3 3 4 4 3 3 4 

18 4 2 3 2 3 2 4 2 4 4 3 3 3 3 3 2 3 3 4 4 4 4 3 3 5 
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19 4 1 4 1 4 1 4 1 4 4 4 4 4 4 3 2 2 4 4 4 5 4 3 2 4 

20 3 3 4 3 5 3 4 3 4 3 3 5 3 5 5 2 2 4 4 4 4 4 4 3 4 

21 4 3 3 3 4 4 5 3 5 4 3 4 3 5 5 2 2 4 3 4 4 4 4 3 4 

22 3 3 2 2 3 2 3 1 3 3 2 4 2 2 5 1 1 3 2 4 4 3 4 3 4 

23 4 3 5 2 4 1 5 1 4 4 4 5 4 2 5 2 2 2 3 4 4 4 4 2 5 

24 4 3 3 3 4 3 5 3 3 5 4 5 3 3 5 2 2 4 3 4 4 4 3 2 4 

25 2 1 1 1 3 1 2 1 4 4 2 3 2 3 4 2 2 2 2 3 3 3 3 2 4 

26 2 3 4 3 4 4 3 3 3 3 4 3 2 4 5 4 3 3 3 4 4 3 3 2 3 

27 2 1 3 1 3 1 1 1 5 4 4 2 2 3 2 2 2 3 3 4 4 3 2 2 2 

28 2 2 4 3 5 2 4 4 4 4 3 4 2 4 5 2 2 3 4 4 4 3 3 3 4 

29 1 1 2 2 3 3 4 5 3 4 3 3 2 2 4 3 3 2 3 4 3 2 3 3 5 

30 4 3 3 3 4 3 5 3 4 3 4 4 4 4 4 4 4 4 3 4 4 4 4 5 4 

31 4 5 4 3 5 5 5 5 5 4 3 5 3 5 5 2 2 4 3 3 4 4 4 5 3 

32 4 3 3 3 4 3 4 1 3 3 4 4 3 3 4 2 2 4 3 4 4 4 3 2 4 

33 4 3 4 1 4 3 5 5 4 4 3 5 2 2 5 4 3 4 4 3 3 5 4 4 4 

34 4 5 4 2 4 3 4 5 4 4 3 5 4 5 5 3 3 3 4 5 5 5 4 2 3 

35 4 2 5 2 5 2 5 2 4 4 4 5 4 4 5 3 3 5 3 3 5 5 5 5 4 

36 3 3 3 3 4 3 4 1 5 4 3 4 3 4 5 2 2 4 3 4 4 4 3 3 5 

37 4 1 3 1 4 1 4 1 3 4 4 4 4 3 3 2 2 4 3 4 4 4 3 3 4 

38 4 2 3 2 5 2 5 2 5 4 4 4 2 4 4 4 3 5 3 4 4 5 3 3 5 

39 3 1 2 1 2 3 5 3 3 3 2 2 2 5 5 1 2 2 4 4 5 3 3 3 4 

40 2 3 1 3 4 1 3 1 4 4 5 4 2 4 4 2 2 3 3 3 4 3 3 3 4 

41 1 2 4 2 3 2 3 2 5 4 4 4 2 4 5 2 3 2 4 5 4 4 4 3 4 

42 4 2 4 1 5 2 4 2 3 4 4 4 4 4 5 2 2 4 4 4 5 4 5 5 3 

43 4 2 2 2 5 2 5 2 4 4 5 4 4 4 5 2 2 4 4 4 2 5 4 4 3 

44 3 3 5 3 5 3 5 3 5 4 3 5 2 5 5 3 3 4 4 4 4 5 5 3 4 

45 4 2 4 2 4 2 4 2 4 4 3 4 3 3 3 2 3 2 4 4 4 4 3 3 3 

46 2 2 4 1 3 2 3 1 5 4 4 4 2 4 5 2 2 2 3 4 4 3 4 3 3 

47 2 1 5 1 4 1 3 1 4 4 2 4 2 4 5 2 2 2 3 4 4 3 3 3 3 

48 4 4 5 5 5 5 4 3 4 4 4 5 4 4 5 4 4 5 3 5 5 5 4 3 5 

49 2 3 3 1 4 3 5 3 2 2 2 4 2 3 5 2 2 3 1 3 2 2 4 2 4 

50 4 1 4 1 4 2 3 1 3 4 2 3 3 2 2 2 3 4 3 4 4 4 3 2 4 

51 3 2 5 2 4 2 4 2 4 4 4 4 3 3 5 1 2 2 4 3 3 5 5 3 5 

52 3 2 3 3 4 3 3 2 5 3 2 2 2 2 2 2 1 3 2 3 3 2 1 2 4 
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ACKNOWLEDGEMENTS

The project team would like to thank all the participating States and their leaders who are 

actively working to improve their roadway safety data. The information collected through this 

assessment process and the resulting improvements in data capabilities would not be possible 

without these key leaders.
For More Information:

http://safety.fhwa.dot.gov

FHWA, Office of Safety

Under Contract 

DFTH-16-D-00005 

Task Order 003

Submitted by VHB Submitted on September 19, 2019

FHWA-SA-19-018