Priorities in Roadway Safety Data Guide 

FHWA Safety Program February 2017 

FHWA-SA-17-032
 

http://safety.fhwa.dot.gov 

Analysis Type 

and Usage 

Data-related 

Costs 

Existing Data 

and 

Management 

Regional and 

Jurisdictional 

Data 

Data 

Variability 

Over Time 

Data Sharing 

Prioritization 

Considerations 
PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

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. 
 

 

 

 

 

 

 

 

 

 

PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

TECHNICAL DOCUMENTATION PAGE
 

1. Report No. 

FHWA-SA-17-032 

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

4. Title and Subtitle 

Priorities in Roadway Safety Data Guide 

5. Report Date 

February 2017 

6. Performing Organization Code 

7.Author(s) 

T. Harmon, R.A. Scopatz, Y. Zhou, M. Bryson, and J. DeFisher 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 

VHB 

8300 Boone Blvd., Suite 700 

Vienna, VA 22182-2626 

10. Work Unit No. 

11. Contract or Grant No. 

DTFH61-10-D-00022 (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, Sept. 2015 – Feb. 2017 

14. Sponsoring Agency Code 

FHWA 

15. Supplementary Notes 

The government task manager for this report was Robert Pollack. The contracting officer’s representative for this report was 

Deena Payton. 

16. Abstract 

This Priorities in Roadway Safety Data Guide is the capstone of more than five years of related projects aimed at helping State, 

Tribal, and local agencies develop more complete data to support advanced safety analysis and decision-making. The Guide 

follows, and promotes, the data-driven approach to safety decision making. It introduces users to the data needs to support 

Federal programs like the Highway Safety Improvement Program and addresses the importance of data quality to support 

performance measurement, Strategic Highway Safety Plans, and other important planning efforts. The Guide provides 

information on data elements and requirements, a detailed overview of analysis methods and their data requirements, guidance 

on prioritizing data elements, and methods for planning, coordinating, and managing major data collection efforts. 

The Guide promotes an all-public-roads approach to safety that addresses the need for local data and emphasizes the 

importance of partnerships among agencies to achieve the desired data goals. The Guide also provides safety engineers and 

analysts with information about data needs in planning, programming, and developing projects under all highway programs. 

17. Key Words: 

safety data, roadway inventory data, traffic 

data, data collection, crash data 

18. Distribution Statement 

No restrictions. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

89 

22. Price 

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

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

ii
 
PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

TABLE OF CONTENTS
 

LIST OF TABLES ....................................................................................... V
 

LIST OF FIGURES..................................................................................... VI
 

ACRONYMS .............................................................................................VII
 

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

CHAPTER 1—INTRODUCTION .............................................................. 1
 

BACKGROUND............................................................................................................1
 

Legislation and Safety Programs ..............................................................................2
 

OVERVIEW OF GUIDE ...............................................................................................3
 

Scope............................................................................................................................3
 

OBJECTIVE....................................................................................................................5
 

AUDIENCE ....................................................................................................................5
 

CHAPTER 2—INTRODUCTION TO MIRE............................................. 6
 

MIRE ...............................................................................................................................6
 

MIRE FDE.......................................................................................................................6
 

MIRE-RELATED GUIDES AND REPORTS ...............................................................8
 

CHAPTER 3—ANALYSIS METHODS, APPLICATIONS, AND DATA
 

REQUIREMENTS ........................................................................................ 9
 

NETWORK-LEVEL SAFETY ANALYSIS ................................................................10
 

Planning and Performance Measurement .............................................................10
 

Crash-Based Approach to Roadway Safety Management....................................16
 

Systemic Approach to Roadway Safety Management ..........................................23
 

PROJECT-LEVEL SAFETY ANALYSIS....................................................................29
 

IMPLEMENTING SAFETY ANALYSIS THROUGHOUT THE PROJECT
 

DEVELOPMENT PROCESS ......................................................................................35
 

CHAPTER 4—PRIORITIZING DATA ELEMENTS ...............................36
 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

IDENTIFYING DATA TO PRIORITIZE...................................................................36
 

Step 1: Define Business Needs ................................................................................37
 

Step 2: Identify Desired Analysis Capabilities and Data Requirements .............38
 

Step 3: Catalog Available Data ...............................................................................40
 

Step 4: Conduct Gap Analysis .................................................................................41
 

Step 5: Prioritize Data .............................................................................................41
 

PRIORITIZATION CONSIDERATIONS .................................................................42
 

Prioritizing Data by Analysis Type and Usage ......................................................43
 

Using Data-Related Costs to Prioritize Data ........................................................45
 

Using Existing Data and Management Needs to Prioritize Data........................48
 

Using Regional and Jurisdictional Data Needs to Prioritize Data.......................48
 

Data Variability Over Time.....................................................................................49
 

Using Data Sharing Considerations to Prioritize Data ........................................50
 

SUMMARY OF PRIORITIZATION...........................................................................50
 

CHAPTER 5—MOVING BEYOND PRIORITIZATION ........................52
 

DATA COLLECTION AND INTEGRATION .........................................................52
 

Developing a Data Collection Action Plan ............................................................53
 

Using and Maintaining a Data Collection Action Plan..........................................55
 

DATA GOVERNANCE AND PARTNERSHIPS......................................................55
 

Additional Resources................................................................................................56
 

CHAPTER 6—SUMMARY........................................................................59
 

APPENDIX A: EXAMPLE APPLICATIONS ..........................................61
 

Example 1: Local Data Priorities ............................................................................61
 

Example 2: State Data Priorities ............................................................................66
 

APPENDIX B: CASE STUDIES ...............................................................73
 

REFERENCES ............................................................................................77
 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

LIST OF TABLES
 

Table 1. Analysis capabilities and data requirements for SHSP analysis and
 

performance measurement............................................................................................13
 

Table 2. Most common crash types and contributing factors in SHSPs. ..................13
 

Table 3. Data used to calculate network screening performance measures............18
 

Table 4. Data to support the crash-based approach. ..................................................23
 

Table 5. Comparison of data collection methods........................................................47
 

Table 6. Action plan excerpt for Goal 1, Action 1. ......................................................53
 

Table 7. Action plan excerpt for Goal 1, Action 2. ......................................................54
 

Table 8. Example 1 Prioritization Results. ...................................................................65
 

Table 9. Number of sites in the State by maintenance and MIRE FDE category. ...66
 

Table 10. Available segment, intersection, and horizontal curve data coverage.....67
 

Table 11. MIRE FDE for local paved roads. ..................................................................68
 

Table 12. MIRE FDE for intersections with paved roads.............................................68
 

Table 13. MIRE data elements for non-local horizontal curves on paved roads. .....69
 

Table 14. Data-related cost priorities. ..........................................................................70
 

Table 15. Data collection sites by site category...........................................................70
 

Table 16. Prioritization results by site type and data collection method. ................71
 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

LIST OF FIGURES
 

Figure 1. Graphic. Data-driven safety analysis linking better data to safety
 

improvements....................................................................................................................1
 

Figure 7. Chart. Example intersection crash type distribution for a municipality.(34)
 

Figure 2. Graphic. SHSP and HSIP processes.(2) ............................................................4
 

Figure 3. Graphic. Example of a MIRE data element.(8).................................................7
 

Figure 4. Graphic. Roadway safety management process. .........................................16
 

Figure 5. Graphic. Systemic project selection process. (32) ..........................................24
 

Figure 6. Chart. Example crash tree diagram for a State.(32)......................................27
 

...........................................................................................................................................28
 

Figure 8. Graphic. Project development process.........................................................29
 

Figure 9. Flow chart. Data prioritization process. .......................................................36
 

Figure 10. Graphic. Simple gap analysis example. .......................................................41
 

Figure 11. Graphic. Main considerations in prioritization...........................................42
 

Figure 12. Graphic. Data life cycle.(45) ............................................................................52
 

Figure 13. Chart. Standard data governance model.(46) ..............................................56
 

Figure 14. Equation. Example prioritization ranking equation. .................................64
 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

ACRONYMS 

AADT Annual average daily traffic 

AASHTO American Association of State Highway and Transportation Officials 

B/C Benefit-cost 

CDIP Crash Data Improvement Program 

CMF Crash modification factor 

DDSA Data-Driven Safety Analysis 

DOT Department of Transportation 

EB Empirical Bayes 

EDC Every Day Counts 

EMS Emergency medical services 

FAST Act Fixing America’s Surface Transportation Act 

FHWA Federal Highway Administration 

GIS Geographic information system 

HPMS Highway Performance Monitoring System 

HSIP Highway Safety Improvement Program 

HSM Highway Safety Manual 

IHSDM Interactive Highway Safety Design Model 

ITS Intelligent transportation system 

LTAP Local Technical Assistance Program 

LRS Linear referencing system 

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

MIRE Model Inventory of Roadway Elements 

MIRE FDE Model Inventory of Roadway Elements Fundamental Data Elements 

MIS Management information system 

MPO Metropolitan Planning Organization 

NCHRP National Cooperative Highway Research Program 

NHTSA National Highway Traffic Safety Administration 

QC Quality control 

RDIP Roadway Data Improvement Program 

RPC Regional Planning Commission 

SHSP Strategic Highway Safety Plan 

SPF Safety performance function 

SSPST Systemic Safety Project Selection Tool 

TRCC Traffic Records Coordinating Committee 

TRA Traffic Records Assessment 

TTAP Tribal Technical Assistance Program 

VMT Vehicle miles traveled 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

EXECUTIVE SUMMARY
 

There is a growing awareness of the importance of data-driven safety analysis. By integrating 

roadway, traffic, and crash data, State and local highway agencies can implement and improve 

data-driven, performance-based safety analyses in planning, programming, and design processes. 

Data-driven safety analysis (DDSA) is the application of the latest software tools and methods 

for analyzing crash and roadway data. Data-driven analyses promote objective, informed 

decision making by quantifying the expected safety impact of each decision in the project 

development process. Performance-based analysis and practical design aims to compare 

conditions to a baseline threshold with the aim of determining optimal design criteria, 

considering safety and cost, while not over-engineering roadways. Advanced analysis methods 

enhance decision-making capabilities; improve productivity; allow agencies to plan, program, and 

develop projects that strategically address their missions and goals; lead to more effective and 

efficient stewardship of funds and programs; and help move toward zero fatalities and serious 

injuries on the Nation’s roadways. 

Data availability and quality are key aspects of a successful data-driven safety program; however, 

it can be daunting to figure out where to begin with data improvement. This Guide can help by 

providing agencies with a means to prioritize their safety data needs. The Guide builds upon the 

previous efforts of the Federal Highway Administration (FHWA) in safety analysis, data quality, 

and data integration. 

This Guide helps agencies understand their options and provides important considerations for 

prioritizing their safety data needs, building upon FHWA’s previous efforts in safety analysis, 

data quality, and data integration. Agencies can use this Guide alongside other FHWA reports 

and Guides to prioritize safety data collection and maintenance to allocate resources efficiently 

and support the most important safety analyses to the agency. 

The Guide promotes an all-public-roads approach to safety that addresses the need for local 

data and emphasizes the importance of partner agencies in contributing to overall data 

improvement. The Guide also provides safety engineers and analysts with information about 

data needs in planning, programming, and developing projects under all highway programs. 

Broad participation in the prioritization effort will help agencies find more opportunities to 

improve safety. 

viii 
 

 

 

PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

CHAPTER 1—INTRODUCTION 

BACKGROUND 

There is a growing awareness of the importance of data-driven safety analysis (DDSA). By 

integrating roadway, traffic, and crash data, State and local highway agencies can implement and 

improve performance-based safety analyses in planning, programming, and design processes by 

using advanced analysis methods. Advanced analysis methods provide the following benefits to 

agencies: 

•	 Enhance decision-making capabilities. 

•	 Improve productivity. 

•	 Allow agencies to meet their performance goals for reductions of fatal and serious 

injury crashes. 

•	 Lead to more effective and efficient stewardship of funds and programs. 

•	 Help move toward zero fatalities and serious injuries on the Nation’s roadways. 

Figure 1 shows the logical links between better data, better decisions, more effective resource 

allocation, and ultimate reductions in fatalities and serious injuries. 

Figure 1. Graphic. Data-driven safety analysis linking better data to safety 

improvements. 

This Guide is intended to help agencies prioritize data needs for safety planning and program 

management as well as in the broader consideration of safety in all highway programs and 

projects. The consideration of safety in all highway projects is essential to reach the goals of 

achieving a significant reduction in fatalities and serious injuries. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Legislation and Safety Programs 

Recent highway authorization bills have reinforced the importance of safety data and advanced 

safety analysis techniques as part of safety programs and public accountability for safety. The 

Moving Ahead for Progress in the 21st Century Act (MAP-21) required that each State have a 

safety data system in place to perform analyses for the Highway Safety Improvement Program 

(HSIP) supporting the strategic and performance-based goals in their Strategic Highway Safety 

Plan (SHSP) on all public roads. The legislation defines safety data as crash, roadway, and traffic 

data on a public road. 

An SHSP is a comprehensive plan for reducing fatalities and serious injuries on all public roads. 

States coordinate with Federal, Tribal, and local highway agencies, and other public and private 

partners, to develop and manage the SHSP. SHSPs guide investments in safety emphasis areas— 

representing significant contributors to fatal and serious injury crashes—through goals and 

strategies set for each area. Most SHSPs integrate strategies from engineering, education, 

enforcement, and emergency medical services (i.e., the 4E approach). Crash, roadway, and 

traffic safety data are necessary during SHSP planning and development through implementation 

and performance measurement. As a data-driven planning effort, the SHSP quantifies safety 

problems within each emphasis area and estimates the safety impacts of expected improvement 

strategies in the plan. Many States include data system and analysis capability improvement 

among their SHSP emphasis areas or strategies, which formalizes the need for improvement 

and allows them to spend HSIP funds on data improvement. The SHSP plays an important role 

in tracking a State’s performance and progress towards achieving their goals. 

The HSIP is a core Federal-aid, State-administered highway program with the purpose to 

achieve a significant reduction in fatalities and serious injuries. A State’s HSIP is the primary 

means for carrying out the engineering and infrastructure safety improvement strategies 

included in the SHSP. Agencies use safety management approaches to identify locations with 

potential for safety improvement and implement countermeasures to address the safety issues 

at those locations. Safety engineers, planners, and analysts use data at all steps in safety 

management process. Higher quality data are essential to make more reliable investments. 

MAP-21 also required the Secretary of Transportation to establish the minimum data elements, 

from the Model Inventory of Roadway Elements (MIRE), that analysts need to conduct systemwide network screening and improve investment decision making.

(1) This led to the 

development of the MIRE Fundamental Data Elements (MIRE FDE), a group of roadway data 

elements that support enhanced network-level safety analysis. The Fixing America’s Surface 

Transportation (FAST) Act continues the emphasis on safety data and the requirement to 

collect and maintain the MIRE FDE. States need to incorporate specific quantifiable and 

measurable anticipated improvements for collecting MIRE FDE into their State Traffic Records 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Strategic Plan by July 1, 2017, and have access to the MIRE FDE on all public roads by 

September 30, 2026. 

Safety research over the past two decades has considerably advanced the scientific basis for 

safety analysis methods applicable to most types of road facilities. However, while agencies have 

an increased awareness of the importance of safety, and agencies continue to aspire to improve 

their analysis capabilities with varying degrees of success, the state of the practice in safety 

analysis and evaluation lags behind the state of the science. This disparity is largely due to 

limitations in data quality and availability. Agencies can use this Guide to proceed with the next 

evolutionary steps toward institutionalization of modern, improved safety management 

practices. 

OVERVIEW OF GUIDE 

The Guide is organized into six chapters followed by appendices containing related material. 

The following chapters present a reference for data elements to prioritize, an overview of 

analysis-related data uses, a walkthrough of prioritization considerations, and steps to take after 

prioritizing data. 

The Guide first concentrates on the data that States need to support Federal safety programs. 

The HSIP and SHSP are familiar to State and local safety staff. States are already making changes 

in response to legislative requirements, and FHWA policy, for data-driven decision making in 

these programs. The Guide does not stop with required implementations based on policy and 

law. The scope includes encouraging broad consideration of safety in planning, programming, 

and developing projects under all highway programs. 

Additionally, the Guide promotes an all-public-roads approach to safety and thus addresses the 

need for local data and the involvement of Metropolitan Planning Organizations (MPOs), 

Regional Planning Commissions (RPCs), Local Technical Assistance Programs (LTAPs), Tribal 

Technical Assistance Programs (TTAPs), and the local and tribal agencies that can contribute to 

overall data improvement. 

Scope 

Chapter 2 summarizes MIRE as a reference of roadway data elements and attributes that an 

agency could prioritize. The chapter discusses MIRE FDE, a subset of critically required data 

elements, and MIRE Management Information System (MIS), a system to organize and maintain 

data. 

Chapter 3 discusses analysis applications and technical data considerations. The chapter has two 

sections: network-level analyses, which require a minimum level of data for all sites, and 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

project-level analyses, which require detailed data for a few sites of interest. Figure 2 depicts 

the SHSP and HSIP processes and the relationship between the SHSP and HSIP. 

Figure 2. Graphic. SHSP and HSIP processes.(2) 

Chapter 4 presents a process that agencies can use to prioritize roadway safety data collection. 

The process helps users determine the data elements and attributes of interest by identifying 

business needs, determining desired analysis capabilities, reviewing available data, and 

conducting a gap analysis. The Guide then provides details on considerations such as data usage 

in analyses, data-related costs, and system priorities to develop a relative priority among the 

selected data elements. Appendix A presents example applications of the prioritization method. 

Chapter 5 discusses steps after prioritization including data management, data governance, and 

developing data business plans 

Chapter 6 concludes with a summary of the Guide and directs readers to safety data resources. 

Case studies and noteworthy practices appear throughout the Guide. Appendix B provides 

short summaries and links to all case studies featured in this Guide. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

OBJECTIVE 

Data availability and quality are key aspects of a successful data-driven safety program; however, 

it can be daunting to figure out where to begin with data improvement. The purpose of this 

guide is to provide agencies with a means to prioritize roadway safety data needs. The Guide 

builds upon FHWA’s previous efforts in safety analysis, data quality, and data integration. 

States can use this Guide as they develop strategic plans for safety data improvement. It aids 

with prioritizing data improvement projects incorporated into the State’s Traffic Records 

Strategic Plan and SHSP. Those plans help make the case for funding and resources devoted to 

data improvement. Data improvement is a means to the end of more effective investment 

decision- making and ultimately, fewer fatalities and serious injuries (see Figure 1). 

This Guide also helps agencies make the case for funding safety data improvement and safety 

analysis in the planning, programming, design, and maintenance phases of project development. 

AUDIENCE 

This Guide serves State and local agency staff involved with roadway and traffic data and 

agencies of all capability levels in safety management. Varying levels of capability are usually 

determined by the amount of data and statistical rigor that agencies use in analysis. 

The target audience includes the following: 

• State, Tribal, and local safety and traffic engineers, planners, and analysts. 

• Data managers and IT staff. 

• Project managers. 

• Traffic Records Coordinating Committee (TRCC) members 

Additional audiences include consultants, LTAPs, and TTAPs who serve the safety data 

collection, management, and analysis needs of State and local agencies. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

CHAPTER 2—INTRODUCTION TO MIRE
 

This chapter provides an overview of MIRE, the MIRE FDE, and MIRE Management Information 

System (MIS) as resources for States considering improvements to their roadway inventory 

data. In October 2010, FHWA released a report entitled Model Inventory of Roadway Elements— 

MIRE Version 1.0. MIRE Version 1.0 provides a comprehensive listing of roadway and traffic 

elements for a robust data inventory. In 2016, FHWA finalized and released the MIRE FDE in 

three roadway categories: Non-Local Paved Roads, Local Paved Roads, and Unpaved Roads. 

The Guide references the MIRE and MIRE FDE data definitions in the data requirements listed 

in later chapters. This chapter provides information so that readers can make efficient use of 

the MIRE resources as they work to prioritize specific data elements.(3,4,5,6) 

MIRE 

MIRE provides a recommended listing of over 200 data elements and a data dictionary of 

roadway and traffic data elements that support State and local DOTs’ safety management 

efforts. MIRE provides a basis for a standard data inventory. Adopting the MIRE data elements 

can help agencies develop performance measures, improve data quality, and track safety 

outcomes. The 2017 MIRE Version 2.0 has some additional elements from MIRE Version 1.0, as 

well as some changes to the categories and data attributes. 

MIRE provides definitions and recommended attributes for each element, as well as a crosswalk 

to other Federal data sets and systems, shown in Figure 3. Readers are advised to check the 

MIRE website for additional, up-to-date information and resources.

(3) 

MIRE FDE 

The MIRE FDE is a subset of 37 roadway and traffic elements that are required to conduct 

enhanced network-level safety analysis. The HSIP requires States to collect the MIRE FDE on all 

public roads, as listed below. Title 23 CFR, Section 924, provides the updated listing of the 

MIRE FDE and calculates the associated costs and benefits of collecting the MIRE FDE.(7) The 

MIRE FDE include elements that are critical for States to conduct enhanced network-level 

safety analyses to support their HSIP. Some MIRE FDE and Highway Performance Monitoring 

System (HPMS) full extent elements that States submit for Federal-aid highways are equivalent. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Figure 3. Graphic. Example of a MIRE data element.

(8) 

The three categories are based on the functional classification and surface type of the roadway 

and include: 

• Non-local paved roadways (37 data elements). 

• Local paved roadways (9 data elements). 

• Unpaved roadways (5 data elements). 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Non-local paved roadways subcategories are: roadway segments, intersections, and 

interchange/ramps. Additional information on the MIRE FDE is available in FHWA’s Guidance 

for State Safety Data Systems.(9) 

MIRE-RELATED GUIDES AND REPORTS 

There are several products related to MIRE that can accompany this Guide to inform roadway 

safety data priorities to help State and local agencies improve the collection, maintenance, 

linkage, and use of their safety data. 

The MIRE Fundamental Data Elements Cost-Benefit Estimation report discusses the economic 

effectiveness of MIRE FDE data collection.(10) The Guide can assist agencies in justifying data 

collection of MIRE FDEs for all public roads. 

The MIRE Data Collection Guidebook provides State and local agencies practical 

recommendations and methods for MIRE data collection.(11) The guidebook describes existing 

and emerging methodologies to collect and process roadway inventory data. For each MIRE 

element, the guidebook provides specific guidance on how the element may be collected. 

Further, the guidance includes recommended accuracy and any special considerations regarding 

the definition including if it is one of the MIRE FDEs. MIRE Element Collection Mechanisms and 

Gap Analysis is another source of information about the data collection process.(12) The MIRE 

MIS Lead Agency Data Collection Report documents different methods used by lead agencies to 

develop an intersection inventory.

(13) It includes examining existing roadway data within an 

agency, collecting additional MIRE data, developing data collection tools, and documenting 

challenges faced and lessons learned. 

The Development of a Structure for a MIRE Management Information System report summarizes 

the efforts of gathering MIRE data into an MIS.(14) The report discusses how to develop a 

conceptual structure for a MIRE MIS. The suggested MIRE MIS is a relational database that 

could tie into a GIS to aid in data entry, querying, and spatial analysis. Users can link the MIRE 

MIS with the various roadway-related databases that DOTs currently use without interfering 

with operational uses of these databases. For example, agencies could gather data from and 

interface between intersection data, bridge inspection and appraisal databases, traffic data, and 

so forth. The report also identifies several scenarios for implementing a safety MIS based on 

testing the framework using New Hampshire and Washington State safety data. 

The Performance Measures for Roadway Inventory Data guide reviews each of the data quality 

performance measures defined by National Highway Traffic Safety Administration (NHTSA): 

timeliness, accuracy, completeness, uniformity, integration, and accessibility.(15) The guide also 

recommends modifications and additions to the existing NHTSA performance measures specific 

to the MIRE data elements. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

CHAPTER 3—ANALYSIS METHODS, APPLICATIONS, AND DATA
 

REQUIREMENTS 

Transportation agencies use safety data to inform decision-making and measure safety 

performance. Other business areas within these agencies focus on planning, alternatives 

analysis, design, construction, operations, maintenance, or other functions. Safety data can 

support decision making in each of these areas, as safety is a factor in nearly all projects. This 

chapter focuses on the importance of these analytic uses when prioritizing data needs and 

presents a spectrum of applications for using safety data in various safety management 

approaches and safety analysis throughout the project development process. 

The two categories in this chapter are: network-level analyses, which require a minimum level 

of data for all sites, and project-level analyses, which require detailed data for specific sites of 

interest. Network-level analysis is a planning function that agencies conduct during safety 

planning (including the SHSP), the crash-based roadway safety management process, and the 

systemic approach. 

Throughout Chapter 3, the Guide summarizes various capability levels in analysis: 

•	 Basic methods evaluate observed crashes and use Crash Modification Factors (CMFs) 

related to the observed crashes. The basic methods introduced in this Guide include 

assessing historical crash data, applying CMFs to observed crash frequency, and 

comparing relative CMF effectiveness. Note that while there may be even more “basic” 

methods that do not take advantage of CMFs, the decision to characterize this as a basic 

level is deliberate. Safety analyses that consider only crash frequency or severity will 

allow agencies to identify locations with relatively high crash counts. Agencies will select 

countermeasures to address the elevated crash frequencies. The CMF-based approach 

to countermeasure selection does not require any further information. Thus, this Guide 

assumes that the basic level of safety decision making can take CMFs into account so 

long as location-specific crash frequency is available. This reasoning applies equally well 

in cases where only the most serious crashes are counted (e.g., just fatal crashes, or fatal 

plus serious injury crashes). Agencies using a crash-frequency-only safety decisionmaking method are encouraged to take advantage of the CMF Clearinghouse to predict 

the effectiveness of safety improvement countermeasures. 

•	 Intermediate safety analysis methods include the basic use of SPFs and result in the 

more statistically reliable predicted average number of crashes. Examples of 

intermediate methods and applications include SPFs based only on annual average daily 

traffic (AADT) or using SPFs with countermeasure CMF adjustments. This is an 

improvement on the basic methods because it allows decision makers to compare the 

site’s crash experience to a performance threshold. As with the basic method, this 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Guide assumes that CMFs can be used to quantify the impacts of specific
 

countermeasures and design alternatives.
 

•	 Advanced safety analysis methods result in the most statistically reliable expected 

average number of crashes. Advanced methods include the use of SPFs weighted with 

observed crashes in the empirical Bayes (EB) or full Bayes methodologies. Analysts can 

use the long-term expected or excess expected crash frequency for each facility type 

when judging if a site holds promise for safety improvement. Again, CMFs can estimate 

the effects of treatment alternatives. The difference between advanced and intermediate 

methods is that the advanced methods control for regression to the mean and other 

factors. This allows decision-makers to select sites, or systemic improvements, that 

promise the greatest crash reductions possible given the resources available. 

Agencies can use this chapter as a resource for identifying their desired analysis capabilities and 

related considerations when prioritizing data needs. Agencies selection of analysis types is 

critical in determining the corresponding data requirements and their specific data needs. That 

list of data needs forms the basis for prioritization decisions. 

NETWORK-LEVEL SAFETY ANALYSIS 

Transportation agencies oversee a network of roadways and are responsible for providing safe 

and efficient mobility to their users. By conducting network-level safety analysis, agencies 

develop strategic safety improvement initiatives and plan infrastructure improvements on roads 

and at intersections. Network-level analysis requires data across the entire jurisdiction—a 

complete crash database along with geographic, geometric, and operational information on their 

roads and intersections. Depending on the quality and availability of data, agencies may select 

different capability levels of analysis—each of which has its own set of data requirements. The 

following sections categorize network-level analysis by SHSP and performance management, 

crash-based safety management, and systemic safety management approaches. 

Planning and Performance Measurement 

High-level planning analyses include those that support SHSPs, performance measurement, and 

policy development. The goal of these analyses is to identify, track, or evaluate network-wide 

safety issues and emerging problems. Agencies need a network-wide crash database to conduct 

these analyses, and should maintain at least a minimum level of roadway and traffic data for all 

sites to better understand the role of facility types and roadway characteristics and their 

relationship to crash risk. The MIRE FDE and supplemental data sets support the analyses 

described here. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Strategic Highway Safety Plan Development and Performance Measurement 

A State’s SHSP serves as a comprehensive transportation safety plan that provides strategic 

direction for highway safety advancement and improvement efforts. The SHSP also serves as a 

master resource document for other State and local safety plans. Multidisciplinary teams of 

subject matter experts develop SHSPs to include strategies and goals regarding engineering, 

enforcement, education, and emergency medical services (EMS). States then establish 

comprehensive performance-based highway safety programs with consistent safety goals and 

objectives that span many public and private agencies. FHWA’s SHSP website presents 

additional useful information and guidance.(16) 

Developing and implementing the SHSP is a data-driven process. Most States develop their 

SHSP using a process similar to the following: 

1.	 Use statewide crash data to examine crash types and contributing factors that are highly 

represented in fatal and serious injury crashes. 

2.	 Set SHSP emphasis areas based on the highest priority areas for the State. 

3.	 Form multidisciplinary stakeholder groups to identify specific, achievable improvement 

strategies within each emphasis area for the five-year duration of the plan. 

4.	 Set goals and performance targets within each emphasis area or for each strategy. 

State, regional, local, and tribal safety data are essential to identifying and prioritizing the plan’s 

emphasis areas (e.g., the list of safety issues where the State sees the greatest need for 

improvement). Analysts use experience and data to identify opportunities for improvements 

within each emphasis area. Using more data allows agencies to develop more targeted 

strategies for specific types of roads, populations, cultures, or regions. 

Continuous monitoring of implementation progress through data-driven performance measures 

and target setting helps managers evaluate progress overall and within each emphasis area. 

Performance measurement also assists stakeholders in prioritizing countermeasures and future 

directions. The process of recurring data analysis helps States allocate resources to safety 

projects and initiatives that have the greatest potential for reducing fatalities and serious 

injuries. 

Performance measures and targets help stakeholder agencies and champions effectively 

measure progress toward achieving the goals of the SHSP, HSIP, and other safety programs and 

initiatives. As required by the FAST Act and FHWA rulemaking, safety performance measures 

and targets should be aligned among stakeholder agencies’ safety plans. Tracking performance 

and setting targets for each emphasis area also allows leaders, partners, and the public to 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

understand the magnitude of the problem, the importance of implementing effective 

countermeasures, and stakeholder commitment. Realistic targets should be established based 

on the countermeasures and resources available to stakeholder agencies. The FHWA Safety 

Target Setting Final Report provides detailed guidance on establishing evidence-based targets to 

help States develop and update fatality targets.(17) 

Collaboration is essential to the SHSP development process. The SHSP collaborative process is 

a potential starting point when gathering stakeholders for setting data priorities. The 

multidisciplinary nature of SHSP development, and the involvement of State, tribal, and local 

agencies, means that the selected emphasis areas and specific strategies reflect the varied 

concerns of all stakeholders. The prioritization process should account for the needs of all 

stakeholders as the team determines which data improvements to pursue and in what order. 

Data to Support Strategic Highway Safety Plans 

Stakeholders need data to analyze network crash trends and patterns when developing and 

implementing an SHSP, but the approaches to developing SHSPs vary across States. Crash, 

injury surveillance, roadway and traffic, vehicle, driver, enforcement, and behavioral data are 

pertinent to developing the SHSP. Data needs depend on data availability and the level of 

analysis that the agency plans to use in developing the SHSP. 

The minimum data requirement for a basic analysis capability level is a network-wide crash 

database, which allows analysts to identify the types of crashes and contributing factors in fatal 

and serious injury crashes. Intermediate analysis uses roadway data to investigate what facility 

types or roadway characteristics are overrepresented within each emphasis area and target 

specific strategies on those facilities. Advanced analysis incorporates supplemental datasets, 

such as a horizontal curve inventory or citation database, to further diagnose and understand 

system-wide issues and then develop tailored strategies to address those problems. Table 1 

presents a summary of the data needs for SHSP analyses. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Table 1. Analysis capabilities and data requirements for SHSP analysis and 

performance measurement. 

Analysis Level 

Crash 

Location and 

Severity 

MIRE FDE Supplemental 

Data 

Basic (e.g., proportions of 

statewide crash types and 

contributing factors) 

X 

Intermediate (e.g., consider 

roadway facility types where 

crashes are overrepresented) 

X X 

Advanced (e.g., incorporate 

supplemental datasets to identify 

targeted strategies) 

X X X 

Basic analysis explores and summarizes the crash inventory to rank high proportions of crash 

types and contributing factors across the State or within regions and other jurisdictions. Crash 

reports capture crash-related data used in these analyses. Analysts summarize the data across 

the State or regions. Depending on the crash data availability and quality, the data should allow 

States and local agencies to set data-driven emphasis areas, such as intersection crashes or 

pedestrian crashes. Table 2, based on a review of all State SHSPs, lists the most common crash 

types and contributing factors in SHSPs, as of 2016.

(18) 

Table 2. Most common crash types and contributing factors in SHSPs. 

SHSP Crash Types and Contributing Factors 

Roadway or Lane Departure 

Intersections 

Occupant Protection 

Impaired Driving 

Teen or Young Drivers 

Speeding or Aggressive Driving 

Pedestrians 

Motorcycles 

Bicycles 

Data improvement is another common emphasis area in SHSPs. Establishing data improvement 

as an emphasis area in SHSPs formally documents the need for improvement and allows HSIP 

spending on data improvement projects. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Analysts should be aware of the limitations and boundaries of their crash database while 

interpreting trends to support safety decisions. For example, if the crash database was 

incomplete in certain years or certain data attributes tend to be inaccurate, analyses should try 

to account for those limitations in the analysis, if possible. Note each crash may include 

contributing factors related to multiple emphasis areas, such as an intersection crash involving 

an impaired driver that was not wearing a safety belt. 

After selecting emphasis areas, stakeholders develop strategies to address and improve safety 

related to each area. The results of a basic analysis generally do not provide more information 

about where, when, or how the issue is prevalent. Stakeholders rely on their experience, other 

plans, and anecdotal evidence to select applicable strategies that target the overall problem. 

Stakeholders also decide how to measure their progress in each area or per strategy, which 

determines the data needs for calculating those performance measures. 

Intermediate methods integrate roadway inventory data in the analyses to develop appropriate 

and achievable strategies by conducting analyses that identify the facility types, roadway 

characteristics, populations, or areas that are overrepresented within the overall emphasis area 

problem. For example, if pedestrian fatalities and serious injuries are concentrated on urban 

arterials, an agency could focus specific strategies on improvements for those facilities that may 

be more effective than generic strategies applicable to all roads. It is important to understand 

the magnitude of the problem, where the safety issues are occurring, and how to guide 

investment of resources. The MIRE FDEs include basic geometric data, geographic information, 

and traffic volume to assess crash trends by roadway type rather than for the network as a 

whole. Conventional network screening analyses with the MIRE FDE can also supplement the 

SHSP analyses. 

The most advanced analyses involve additional supplemental data sets to provide the capability 

for more in-depth system diagnosis. Based on Table 2, agencies may find different data sets 

useful, including intersection inventories, horizontal curve inventories, pedestrian and bicycle 

facility inventories, citation and adjudication records, and transportation systems management 

and operations data. Several States are integrating multiple datasets into the State databases to 

bolster existing analysis. Appendix B, which provides links and information about the case 

studies in this Guide, contains several case studies that describe the ways that States and local 

agencies have developed the data resources needed for intermediate and advanced safety 

analysis. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Crash Characteristic Data Map Book – Arizona 

In preparation for their SHSP updates, Arizona developed a detailed crash characteristic 

data map book. The book presents a high-level overview of crash types associated with 

characteristics. It also presents guidance on where efforts can be implemented to reduce 

crashes.(A) 

The NCHRP Report 500, Vol 21: Safety Data and Analysis in Developing Emphasis Area Plans 

provides procedures for basic and higher-order safety planning and analysis used to develop 

emphasis areas.(19) FHWA’s SHSP Community of Practice and Noteworthy Practices websites 

are valuable resources for developing and improving SHSPs.(18,20) 

Data to Support Performance Measurement 

Federal rules require some agencies to develop performance measures for various programs. 

The FAST Act requires States to establish safety performance targets for the following five 

safety performance measures: 

• Number of fatalities. 

• Number of serious injuries. 

• Rate of fatalities per hundred million vehicle miles travelled (VMT). 

• Rate of serious injuries per hundred million VMT. 

• Number of non-motorized fatalities and severe injuries. 

FHWA has published guidance for State and local agencies developing safety performance 

targets, stemming from the FHWA HSIP Performance Measure Rulemaking.(21) Most agencies 

supplement these measures with others that pertain to their safety improvement initiatives and 

emphasis areas. Agencies may also track each of these across regions or various types of roads 

in addition to statewide measures. 

Data for safety performance measurement is essentially the same as the data needed for 

developing emphasis areas and strategies. At a basic level, tracking progress in reducing systemwide target crashes can provide a basic indication of success. However, it may be difficult to 

track performance on specific strategies when only working with a crash database. 

Incorporating the MIRE FDE and facility types allows for an increased level of performance 

tracking, where an agency could measure progress on meeting goals across each facility type or 

within the facility type that they have targeted with recent improvements. Additional data sets 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

further enhance a State’s ability to consider interactions, variations, and context within a sample 

of crashes that they could not assess with only a crash inventory. 

A major consideration in safety performance measurement is integrating data across agencies 

so everyone within a jurisdiction can track the same measures and see the same trends and 

progress. 

Crash-Based Approach to Roadway Safety Management 

The crash-based approach to roadway safety management focuses on identifying the sites 

across a network with the highest potential for safety improvement, diagnosing the concerns at 

those sites, and implementing strategies to target those issues. 

Overview of the Roadway Safety Management Process 

The crash-based approach is well-defined in the FHWA HSIP Manual and AASHTO HSM Part B 

as the roadway safety management process and is incorporated in several software packages 

such as AASHTOWare Safety AnalystTM, AgileAssets Safety Analyst, and Vision Zero Suite. 

Figure 4 depicts the roadway safety management process as outlined in the HSM. 

Figure 4. Graphic. Roadway safety management process. 

Agencies conduct network screening to identify sites across a State or local network with 

potential for safety improvement. Analysts then conduct further investigation at the highest 

ranked sites to identify sites with potential for projects and diagnose the issues at those sites 

using crash summaries, road safety audits, or other similar tools. Project managers and 

stakeholders select the most applicable countermeasures and conduct an economic analysis of 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

the alternatives. The agency then implements the countermeasure(s) and evaluates the 

effectiveness of those improvements post-construction. The outcome of the crash-based 

approach is projects that are well suited to address or mitigate specific safety problems at sites 

with a demonstrated poor safety performance. 

Data to Support the Crash-Based Approach to Roadway Safety Management 

Safety management involves planning-level and preliminary design-level analyses and decisionmaking. The goal of network screening (Step 1 in Figure 4) is to identify the sites that warrant 

further investigation. It does not require very detailed or precise data for every site. The 

subsequent steps in the process analyze the top sites from the network screening to plan and 

begin designing justifiable and effective projects. After projects are constructed, agencies 

evaluate their effectiveness (Step 6) to inform future planning and project development. Steps 2 

through 6 are also network-level analyses; however, these analyses require additional data only 

for selected sites. It is more important to have a minimum level of relatively basic data about 

each site at the start of the process, and seek out additional data for the sites that are selected 

as candidates for further investigation and potential project implementation. Agencies should 

consider how to best retain additional information collected for analyzing potential project 

locations. The data requirements for each step assume the data for the previous steps are 

available. For example, in Step 2 the Guide lists the extra data needed beyond those data used 

in Step 1 for the same capability level. 

Crash-Based Safety Management Approach Case Study – Ohio 

The Ohio DOT has embraced the opportunity to integrate AASHTOWare Safety 

AnalystTM within its existing safety management system. The software implements analysis 

methods from the HSM roadway safety management process. Ohio DOT had previously 

made efforts to collect and maintain required data, but the Department undertook several 

additional data gathering efforts to improve the quality and quantity of the available data. 

Ohio DOT also developed tools to automate the transformation of data into the required 

import data set. By improving the inputs and streamlining the data translation process, Ohio 

DOT benefits from analysis outputs that help direct funding to areas where improvements 

will have the greatest impacts.(C) 

Network Screening 

Network screening produces a list of sites with potential for safety improvement. Agencies 

rank sites by a measure of safety performance to develop a manageable list of sites for further 

investigation. The screening performance measure can be as basic as a count of total crashes. 

Advanced methods use crash, roadway inventory, and traffic volume data and compare each 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

site’s safety performance to a predicted performance threshold for that type of site. 

Intermediate and advanced methods use safety performance functions (SPF) as a performance 

threshold. SPFs are functions that help analysts predict the crash frequency of a site based on 

traffic volumes and roadway characteristics. 

Table 3 presents the data sets that analysts use in calculating common screening performance 

measures, listed from most basic at the top to most advanced at the bottom. The advanced 

methods have the advantage of controlling for bias in crash data and the selection process. 

Readers should bear in mind that each measure could examine overall crash risk or represent a 

targeted crash type or severity. 

Table 3. Data used to calculate network screening performance measures. 

Performance Measure Crash Roadway Traffic Other 

Average Crash Frequency X X 

Crash Rate X X X 

Equivalent Property Damage Only Average 

Crash Frequency X X Crash Costs 

Level of Service of Safety X X X SPF 

Excess Predicted Average Crash Frequency 

Using SPFs X X X SPF 

Probability of Specific Crash Types Exceeding 

Threshold Proportion X X 

Excess Proportions of Specific Crash Types X X 

Expected Average Crash Frequency with EB 

Adjustments X X X SPF 

Equivalent Property Damage Only Average 

Crash Frequency with EB Adjustment X X X SPF 

Excess Expected Average Crash Frequency 

with EB Adjustments X X X SPF 

A State’s safety data system should assign location codes for all public roadways and use those 

same location codes for crashes, roadway information, and traffic volumes.(9) The minimum 

requirement for basic network screening is crashes locatable on the roadway network. 

Integrating crash and roadway data is accomplished using geolocated crash data and roadway 

data on a shared linear referencing system. Integrated crash and roadway data allows an analyst 

to identify locations with high concentrations of crashes using some form of linear or geospatial 

analysis. 

An intermediate-level network screening analysis would include at least the MIRE FDE to allow 

screening by facility types. The MIRE FDE is basic site-level data and information about the site’s 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

facility type. The MIRE FDE is required for network screening using SPFs for analysis, which is 

critical to implementing a predictive analysis method. Intermediate-level analysis also should 

factor in crash severity to focus the analysis on safety performance measures. 

Advanced network screening analysis uses expected crash frequency to measure performance, 

and may include roadway inventory elements beyond the MIRE FDE, such as a horizontal curve 

inventory or pedestrian facility database, to focus the screening even more on the crash types, 

facility types, and sites that are a high priority. Reliability of Safety Management Methods: Network 

Screening presents a justification for using advanced network screening methods and examines 

the worthiness of several network screening performance measures.

(22) 

Diagnosis 

Diagnosis is the process of further investigating the sites identified in network screening to 

identify specific safety concerns and contributing factors at potential project sites. Basic 

diagnosis involves a traditional engineering study, including an office-based review of crash data, 

such as crash report documents, summary statistics, and collision diagrams. Intermediate 

diagnosis capability builds on basic analysis with a multidisciplinary team field study and road 

safety audit to observe site conditions and road user behaviors to identify underlying 

contributing factors that may not be obvious from the crash data alone. Incorporating human 

factors considerations with guidelines from NCHRP Report 600: Human Factors Guidelines for 

Road Systems, Second Edition and similar resources can further improve diagnostic capabilities.(23) 

Advanced-level diagnosis incorporates statistical methods to focus the diagnosis. For example, 

analysts can use crash data and MIRE FDE to develop statewide or regional average proportions 

of certain crash characteristics by facility type to compare the candidate site to baseline 

thresholds and identify safety problems or target crash types. The Reliability of Safety 

Management Methods: Diagnosis information guide describes advanced methods to support 

diagnosis.(24) 

Countermeasure Selection 

Following diagnosis, agencies select potential treatments (engineering-related or other) for each 

site of interest. Appropriate countermeasures target the underlying contributing factors 

identified in the diagnosis. Data used in countermeasure selection include crash data, budget 

and other program data, stakeholder input, and project-related factors (e.g., costs and 

environmental and right-of-way needs). Intermediate practice involves a list of common or 

preferred countermeasures by facility type along with preselected CMFs for those treatments. 

A standard countermeasure or CMF list promotes consistency and saves time in analysis. The 

Developing a State CMF List flyer provides guidance in developing lists of standard CMFs for an 

agency.(25) The CMF Clearinghouse is the most comprehensive source of CMFs and CMFrelated knowledge. Although not current practice, recent research shows that States may need 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

to calibrate CMFs to improve their accuracy for local application; however, several States have 

explored developing their own CMFs, rather than calibrating the published values.(26) Developing 

State-specific CMFs or calibrating national CMFs defines an advanced capability level. 

The data needs for countermeasure selection focus on countermeasure feasibility (e.g., what 

improvements would work well at the site) and countermeasure effectiveness (i.e., inputs for 

specific CMFs). For example, if the diagnosis for an intersection showed a high proportion of 

left-turn crashes, the countermeasure selection stage might require data to conduct a signal 

warrant analysis. It is most cost-effective for agencies to collect this type of data on a projectby-project basis rather than for the entire network of sites. The Reliability of Safety Management 

Methods: Countermeasure Selection information guide describes various methods to support 

countermeasure selection.(27) 

Economic Appraisal 

Economic appraisal compares the relative benefits and costs of proposed alternatives or 

treatments. Analysts usually express benefits as the dollar value of the estimated lives saved as 

well as other injuries and property damage avoided (e.g., based on a CMF). Costs may include 

construction costs, maintenance costs, right-of-way acquisitions, and other costs incurred due 

to implementing the treatment. Common economic measures include benefit-cost (B/C) ratio, 

net present value, and cost-effectiveness. Economic analysis also allows project managers and 

designers to develop project budgets to maintain economic justification for safety improvement 

with known countermeasures and desired B/C ratio. 

Data required to support economic appraisal include an estimate of the change in crashes by 

crash frequency and severity (converted to costs), projected construction and maintenance 

costs, and countermeasure service life. Agencies can compile lists of typical countermeasure 

costs, service life, and construction duration to standardize this step across their jurisdiction. 

Project costs should include the initial construction cost as well as costs for additional 

maintenance and rehabilitation activities incurred as a result of the project over the service life 

or analysis period. Benefits are the monetized crash reduction resulting from a countermeasure 

installation, usually with CMFs, using standard crash costs. 

Economic appraisal is useful for project comparison and prioritization. Some potential 

countermeasures may not be justified from a safety perspective, nor is it always feasible, 

practical, or desirable to implement all economically justified countermeasures at a site. It is 

necessary to estimate the cost and expected benefits of each potential countermeasure and 

project to determine whether each strategy is justifiable. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Project Prioritization 

Project prioritization results in an optimized portfolio of projects for a given budget. Agencies 

have different processes for prioritizing projects. Even within safety programs, safety benefits 

are usually not the only decision factor. Rankings based in part on project costs, safety benefits, 

project risk, project readiness, agency goals and performance targets, public perception and 

acceptance, and political influence, among others, can maximize the B/C ratio of the overall 

program for a given available budget. 

Safety Effectiveness Evaluation 

Agencies conduct safety effectiveness evaluation to quantify the actual safety benefits achieved 

from project implementation (e.g. a reduction in fatal and serious injury left-turn crashes). The 

evaluation provides critical feedback for future planning. Agencies can and should conduct 

evaluations at the project, countermeasure, and program levels. 

The objective of project-level evaluation is to quantify the safety effectiveness of individual 

projects. For example, a project-level evaluation could analyze the safety impacts of a 

roundabout implementation project. Countermeasure-level evaluations focus on many 

installations of the same countermeasure across multiple sites to better estimate the 

effectiveness of installing that treatment in the future. For instance, a countermeasure-level 

analysis could determine the average safety effect of all roundabout installations across a State. 

These types of evaluations are useful in CMF development. 

Project- and countermeasure-level evaluations have similar data requirements and capability 

levels. Basic analyses include simple before-after comparisons of observed crashes with no 

control for traffic volume, changes over time, or regression to the mean. Intermediate analyses 

incorporate comparison groups that begin to account for traffic volume and general changes 

over time, but would still not control for regression to the mean. Intermediate analyses may 

also look at changes in target crash types rather than the effect on total crashes. Advanced 

analyses fully account for traffic volume, changes over time, and regression to the mean as well 

as changes in target crash types. Examples of advanced safety effectiveness evaluation methods 

include empirical or full Bayesian methods. 

Roadway data requirements for all evaluations include construction dates, implemented 

countermeasure locations, actual project costs, and estimated service life. Agencies also need 

crash frequency of targeted crash types before and after the countermeasure implementation. 

Intermediate and advanced analyses require information about other similar sites with and 

without the selected countermeasure. Advanced analyses require SPFs. 

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Empirical Bayes Analysis – Wisconsin 

Wisconsin DOT developed a project evaluation process incorporating EB analysis into all 

HSIP project evaluations using the network-level SPFs from the AASHTOWare Safety 

AnalystTM software. Results from implementing the EB method for B/C analysis showed how 

a simple before-after evaluation can overestimate the safety benefits of a project.(B) 

The objective of program-level evaluation is to determine the overall benefit or effectiveness of 

an entire safety program or a group of projects sharing a common focus or emphasis. Program 

evaluation could cover all similar projects within a program such as those focusing on 

intersections, roadway departure, or pedestrian safety. The goal of these analyses is to quantify 

the impact of the program on performance measures and the overall return on investment in 

projects. Additionally, an agency may use program-level evaluation to assess progress toward 

meeting specific performance goals in safety emphasis areas. For example, it may be appropriate 

to compare the number and cost of safety improvement projects targeting roadway departure 

to the number and trend in roadway departure crashes, injuries, and fatalities. Agencies can use 

this type of information in performance measurement and target setting. 

Basic evaluations of an entire program can be somewhat simpler and compare all expenditures 

and benefits. The primary requirement is a list of the projects, costs (preferably by year), and 

benefits. Advanced program analyses use expected benefits calculated with more statistically 

rigorous methods. Evaluations may also focus on subsets of projects within a program that 

address each emphasis area, which requires agencies to designate each project with one or 

more program emphasis areas. 

The HSIP Evaluation Guide and Reliability of Safety Management Methods: Safety Effectiveness 

Evaluation informational guide provide more detailed information about evaluation 

methodologies.(28,29) The Recommended Protocols for Developing Crash Modification Factors guide 

and Guide to Developing Quality Crash Modification Factors are useful references for 

countermeasure evaluations and CMF development.(30,31) 

Table 4 summarizes the data needed to support most applications of the crash-based approach. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Table 4. Data to support the crash-based approach. 

Component Crash Exposure Roadway Other 

Area type 

Network 

Screening 

Crash counts 

by severity at 

the site level 

(intersections 

and segments) 

Traffic volume 

and segment 

length 

(rural or 

urban), number 

of lanes, 

median type, 

intersection 

control, and 

Safety 

performance 

functions (SPFs) 

or other 

thresholds by 

facility type 

number of legs 

Diagnosis 

Three to five 

years of 

police crash 

reports and 

details for 

each location 

Traffic volume 

and turning 

movement 

counts 

Traffic 

operations, 

roadway 

design, and 

roadside 

design features 

Adjacent land 

use, road user 

behavior, and 

road user 

demographics 

Countermeasure 

Selection 

Three to five 

years of 

police crash 

reports and 

details for 

each location 

Traffic volume 

and turning 

movement 

counts 

Traffic 

operations, 

roadway 

design, and 

roadside 

design features 

List of crash 

contributing 

factors and 

countermeasure 

details 

Economic 

Appraisal 

Expected 

change in 

crashes due 

to treatment 

Current and 

future traffic 

volume 

Site 

characteristics 

to identify 

suitable crash 

modification 

factors (CMFs) 

Applicable 

CMFs, average 

crash costs, and 

service life of 

treatment 

Site 

Safety 

Effectiveness 

Evaluation 

Crash counts 

by severity 

before and 

after 

treatment for 

each site 

Traffic volume 

before and 

after treatment 

for each site 

characteristics 

to define a 

suitable 

reference 

group or 

comparison 

Treatment 

details, including 

location and 

implementation 

date; SPFs 

group 

Systemic Approach to Roadway Safety Management 

The systemic approach targets system-wide safety problems and aims to implement 

countermeasures across the network to address those specific concerns. FHWA’s Systemic 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Safety Project Selection Tool (SSPST) presents one version of systemic safety analysis.

(32) Other 

data-driven systemic safety analysis methods are implemented within software tools such as 

usRAP and AASHTOWare Safety AnalystTM. Figure 5 shows the systemic process employed in 

the SSPST. 

Figure 5. Graphic. Systemic project selection process. (32) 

Practitioners have mostly followed the SSPST methodology shown in Figure 5 when 

implementing systemic safety improvement projects. The systemic project selection process 

follows a similar flow to the crash-based safety management process. An agency identifies focus 

crash types or common geometric and operational characteristics associated with higher 

crashes across the network, screens and selects candidate locations, and selects 

countermeasures to address roads with those characteristics. Alternatively, agencies may start 

with a countermeasure they want to implement (to address a specific crash type), and then 

determine the most appropriate locations to apply that countermeasure. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Agencies often use the systemic approach to address rural safety concerns where fatal and 

serious injury crashes are sparsely located or when crash and traffic volume data are not 

available. However, analysts may build upon this approach by incorporating site-based crash or 

exposure thresholds when the data is available to enhance the systemic analysis. Incorporating 

crash and exposure data reduces project risk and focuses investments on sites with the highest 

potential for crash reduction. 

Funding Systemic Improvements – New Mexico 

New Mexico identified proven countermeasures to streamline the wide implementation of 

cost-effective safety improvements on rural roads. Implementing this approach, the State 

applied low-cost systematic improvements to locations that might not otherwise have 

received funding in a crash-based approach due to their crash history.(D) 

All systemic analyses begin with a system diagnosis in some form or another. Often, analysts 

look at system-wide crashes (or for a network or corridor) to identify focus crash types. Other 

times the process may start with selecting a countermeasure, and in this case it is likely that an 

agency already performed a system diagnosis (e.g., for the SHSP) or the countermeasure 

addresses a known priority safety issue such as roadway departure, intersection safety, or 

safety for pedestrians and bicyclists. 

As with the crash-based approach, agencies should evaluate the safety and economic 

effectiveness of systemic projects post-construction. Systemic projects require data about more 

locations due to the wider implementation, so the evaluation can be more complex. However, 

the data requirements for the evaluations are the same for all projects. 

Ultimately, the systemic approach results in projects well suited to improve high priority 

emphasis area safety problems across the network. The Reliability of Safety Management Methods: 

Systemic Safety Programs guide provides more details about systemic analysis approaches and 

suggests methods for balancing crash-based and systemic projects within improvement 

(33) programs.

Data to Support the Systemic Approach to Roadway Safety Management 

The data requirements for systemic safety analyses vary depending on the level of sophistication 

a State or local agency adopts in their approach to analysis and decision-making, although some 

steps are common to all methods. The following discussion focuses on the analysis that is 

unique to systemic analysis. 

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System Safety Project Selection Tool – New York 

The New York State DOT used the SSPST to identify locations that would benefit from 

deploying low-cost countermeasures. New York State DOT combined crash data with 

roadway inventory data to develop a crash tree diagram assessment of rural vs. urban, 

number of lanes, a divided or undivided indicator, and posted speed limit, which resulted in 

a focus facility type for systemic improvements. The crash and roadway inventory data was 

then used to select risk factors for the selected facility type.(E) 

The data requirements for system diagnosis and selecting focus crash types are comparable to 

those for the SHSP emphasis areas analyses. Analysts should consider crash types that 

constitute a high proportion of fatal and serious injury crashes across the system or network. 

Tools such as crash summaries, crash tree diagrams (like the one shown in Figure 6), and crash 

distributions identify crash types that have disproportionately high number of crashes across 

the network or for a single facility type of interest. High-quality detailed site-level data is not 

required for this step. Incorporating the MIRE FDE and other supplemental datasets can help to 

hone not only the focus crash types and characteristics of the problem but also the best 

locations to implement the improvements. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Figure 6. Chart. Example crash tree diagram for a State.

(32) 

Countermeasure selection in the systemic approach is based on system needs rather than a 

site-specific diagnosis, although some site-level review is necessary to ensure the 

countermeasures are appropriate. Figure 7 shows an example crash type distribution of 1200 

crashes across intersections within a municipality. By identifying that turning maneuvers and 

rear end account for most crashes, an agency can select appropriate countermeasures to 

address those crashes. Typically, agencies favor lower unit cost countermeasures in the 

systemic approach so the agency can install the treatments across many sites. Selecting 

implementation sites with the highest B/C ratio can result in the highest economic effectiveness 

for the available budget. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Left Turn 

624 

(52%) 

Rear End 

384 

(32%) 

Angle 

108 

(9%) 

Sideswipe 

72 

(6%) 

Fixed Object 

12 

(1%) 

Figure 7. Chart. Example intersection crash type distribution for a municipality.

(34) 

Systemic evaluations abide by the same economic analysis, prioritization, and effectiveness 

evaluation methods and data requirements as site-specific projects. Agencies should try to 

collect implemented project and countermeasure data during project development and 

construction. In contrast to crash-based projects where it is clear what site the project was at, 

it is more important for agencies to track the locations of systemic projects during project 

development and implementation because it is more difficult to identify potentially hundreds or 

thousands of implementation locations throughout the State across multiple years. The State 

must have information on previously implemented improvement sites when deciding where to 

go next. 

Data to Support Network-Level SPF Development and Calibration 

Agencies typically develop network-level SPFs to model safety performance of facilities at a 

planning-level for network screening or to answer a specific question regarding policy or crash 

occurrence. Agencies characteristically develop network-level SPFs, using length and exposure 

as independent variables, for each facility type. That is, States most often develop separate SPFs 

for two-lane rural roads, urban interstates, and so on. The MIRE FDE represent the roadway 

data needs for developing network-level SPFs, including traffic volumes, that accompany a crash 

database with crash location information. Analysts develop SPFs using as many sites as possible 

to assure statistically significant results across all roadways and characteristics that are present 

within the network. Agencies may choose to develop their own SPFs or to calibrate previously 

published SPFs (e.g., from national resources). SPF development and calibration have the same 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

data requirements; however, the data sample necessary for calibration is smaller than that used 

to develop SPFs from scratch. Agencies can use The Calibrator spreadsheet tool to calibrate SPFs 

and assess SPF compatibility and applicability.(35) 

Summary of Network-level Safety Analysis 

Network-level analysis is enabled by the breadth of data available about all sites across a 

network. Analysts need to be able to understand the crash types most prevalent across a 

network and to compare potential project locations to other similar sites within a network to 

identify the relative potential for improvement. As agencies conduct site-level diagnosis and 

begin to develop projects, their analyses require more data specific to the candidate site that 

are not needed for planning-level network screening. Ideally, as agencies collect site-level data 

they should save that data within a database for future evaluation of the implemented 

improvement. Agencies could use such a database to capture detailed site characteristics that 

they could use later in project-level analysis, as well as field notes and other observations that 

may be useful later. 

Generally, for States with an all-public roads common highway base map, the basic networklevel analyses require a crash data set. Intermediate-level analyses incorporate roadway and 

traffic data included in the MIRE FDE. Advanced analyses allow more flexibility and complexity 

in the analysis by factoring in other supplemental data sets such as a broader list of roadway 

inventory elements and other data. 

PROJECT-LEVEL SAFETY ANALYSIS 

The objective of project-level safety analyses is to examine the safety performance of site or 

project conditions and to optimize the safety of roadway designs. As an agency considers 

constructing a project, designing a project, or evaluating the effects of projects, project-level 

safety analyses help inform their decision-making and ultimately allow agencies to deliver better, 

more defensible, projects. Figure 8 depicts the project development process divided into 

sections that relate to various types of analyses. 

Figure 8. Graphic. Project development process. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Project-level analysis applications typically consist of predicting the safety performance in 

planning, alternatives analysis, design, and through construction, operations, and maintenance. 

Agencies interested in quantifying safety performance at an existing or proposed site can use 

SPFs and CMFs to calculate the predicted or expected safety performance for the geometric 

and operational characteristics of that site. Project-level SPFs are more complex than networklevel SPFs, allowing for more comprehensive insight into the factors affecting safety. This helps 

designers understand the implications of design decisions and project managers to best 

communicate project effectiveness to stakeholders. Project-level analysis can also compare 

multiple alternatives to understand the tradeoffs in proposed designs. Finally, designers can use 

project-level analysis tools to optimize the design with quantitative means. Integration of Safety in 

the Project Development Process and Beyond: A Context Sensitive Approach and Integrating the HSM 

into the Highway Project Development Process provide many additional insights for agencies 

interested in safety analysis in the project development process.(35,37) The Scale and Scope of 

Safety Assessment Methods in the Project Development Process informational guide suggests safety 

assessment methods that may be suitable for answering safety performance-related questions 

that typically during each phase of the development process and for projects of various types.

(38) 

Several tools are available to analysts working with project-level safety analyses. HSM Part C is 

a resource for understanding the predictive method and provides SPFs and CMF adjustment 

factors for analyzing rural two-lane roads, rural multilane roads, urban and suburban arterials, 

freeways, and interchange ramps. HSM spreadsheets implement the SPF equations and process 

to organize and simplify the calculations for Chapters 10-12 and 18-19 of the HSM. The 

enhanced Interchange Safety Analysis Tool is a spreadsheet tool that implements the freeways 

and interchange ramp analysis methods. The IHSDM is a software application that faithfully 

implements the HSM Part C methods along with other design tools related to geometrics and 

operations. Other research through the National Cooperative Highway Research Program, 

State DOT research offices, and universities has developed SPFs and related analysis tools that 

are available when limitations in the HSM preclude conducting analysis. States have the option 

to develop their own SPFs and CMFs if published ones from other jurisdictions are not suitable. 

The Summary of State SPF Calibration and Development Efforts spreadsheet lists State-developed 

SPFs and States’ SPF calibration factors.

(39) 

Sometimes agencies use only CMFs when complex analyses are not appropriate or other tools 

are not available. HSM Part D and the CMF Clearinghouse provide a wealth of CMF information 

that analysts may use for estimating changes in safety performance. 

Project-level safety analysis requires data in two ways. SPF development and calibration 

procedures require detailed geometric and operational data for a sample of sites. Applying the 

predictive method in project development to assess the safety performance of standalone 

designs or to compare sites or designs of different facility types requires calibrated SPFs and 

detailed data inputs only for the project locations. Some analyses do not require calibrated 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

SPFs. Given these requirements, it is not necessary or prudent to collect this data for all sites 

across a network. Planners, designers, and safety analysts should compile data as projects enter 

the project development process, and agencies should build SPF data as the need arises and 

calibrate SPFs regularly. 

Data to Support Project-Level SPF Development and Calibration 

SPFs are an essential tool for project-level safety analyses. Most SPF development research uses 

data from a handful of States for a certain period, and there are issues introduced when 

transferring these equations to other States or jurisdictions. Calibration allows analysts to apply 

SPFs to jurisdictions and time periods not reflected in the modeling data set. Rather than 

calibrating SPFs from other jurisdictions, some agencies elect to develop their own SPFs with 

data from their jurisdiction. 

A common question is whether agencies should calibrate or develop SPFs. In a way, this 

question is misleading. Agencies must calibrate SPFs regardless of whether the agency uses SPFs 

from other jurisdictions or they develop their own. The important consideration is whether 

SPFs from other areas are acceptable or if developing SPFs is necessary. There is more 

discussion on this topic in the Safety Performance Function Decision Guide: SPF Calibration vs SPF 

Development.

(40) 

The SPF equations serve as a baseline estimate of safety performance against which analysts may 

compare specific project sites. Developing and calibrating SPFs is the only function within 

project-level analysis that requires data for more sites than the candidate project location does, 

and therefore requires a substantial investment to collect, manage, and analyze that data. This is 

the largest need for data collection within project-level safety analysis. The data requirements 

to apply these tools are specific to the project, and the data are often already readily available 

or easy to collect as part of normal design practices. As with network-level analysis, agencies 

only need data to develop or calibrate SPFs for the facility types on which they plan to apply 

project-level SPFs. 

Calibration adjusts the SPF to account for differences in several factors including crash 

experience, driver population, climate, culture, and crash reporting thresholds. The HSM 

recommends calibrating SPFs at least every two to three years. The calibration procedure 

requires detailed SPF data inputs for a sample of sites to calculate the predicted crashes at each 

site, as well as the historical crash data for the same sites. Each SPF uses a unique set of data 

elements. Agencies collect the necessary data for a random sample of sites to support 

calibration, and should maintain that data set in subsequent years. The only new data needed 

for future recalibration is the updated crash history and any revisions to the SPF data inputs 

(e.g., because of new construction) for the sample sites. More information regarding calibration 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

is provided in the User's Guide to Develop Highway Safety Manual Safety Performance Function 

(41) Calibration Factors.

SPF development is a more flexible and exploratory process. Analysts develop SPFs for several 

reasons. When generally modeling safety performance of some facility type, analysts use data 

elements that they expect are related to the safety of the facility. In other cases, they may 

develop SPFs to better answer a specific question, such as determining an optimal roadway 

cross section for preventing roadway departure crashes of a given type of road, and limit the 

variables included in the model to those that help answer that question. More information is 

provided in the Safety Performance Function Development Guide: Developing Jurisdiction-specific 

SPFs.

(42) 

Data for Applying Project-Level Analysis 

Data requirements for applying predictive methods in project-level analyses range from 

relatively basic geometric and operational data to finite details of project designs. However, the 

data are only required for each specific project location. As planners, designers, and analysts 

work on a project, they already collect and compile much of the needed data inputs through 

their normal processes (or the data needed to calculate the required inputs). The other data 

inputs may require special data collection. It is not necessary to undertake a region-wide data 

collection effort for this amount of data for each site across the network when agencies analyze 

so few sites with project-level safety analysis. IHSDM allow users to import much of the data 

for a project from civil design software. 

The following subsections present some of the common analysis types and the data-related 

considerations for each. 

Planning and Scoping 

Analysts can apply project-level safety analysis tools during planning and scoping to identify 

which roadways aren’t performing as they should, determine the scope and need of potential 

projects, and prioritize them. Project-level safety analysis can improve decision making for all 

highway projects early on in project development. 

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Interactive Highway Safety Manual Software – Idaho 

Idaho used the IHSDM software’s Crash Prediction and Policy Review modules to evaluate 

the safety and operational effects of existing traffic and roadway geometry on an 11-mile 

section of a corridor. The analysis resulted in a list of geometric deficiencies and specific 

locations needing further improvement. The project also resulted in a Corridor Plan Report 

to be included in the Statewide Transportation Improvement Program for future 

implementation.(F) 

Alternatives Analysis 

Nearly every project has multiple alternatives that planners or designers consider at an early 

stage in the project development process. Project-level analysis provides an opportunity for 

agencies to compare design alternatives from a safety perspective in a quantitative manner. 

Agencies can predict the number and severity of crashes for multiple design options at once, 

and then compare them side-by-side. 

Highway Safety Manual Predictive Method – Florida 

Florida used the HSM predictive method to analyze alternatives for a corridor widening 

project on an urban arterial in the Tampa area. Based on the analysis results, Florida was 

able to determine that a four-lane divided alternative was predicted to have a crash cost 

savings of approximately $4.2 million, in comparison to an alternative with five-lanes and a 

two-way left-turn lane. This further justified additional costs associated with right of way for 

the project.(G) 

Design 

Agencies can use project-level safety analysis to determine optimal design criteria, considering 

safety and cost. Applications in design phase can include evaluating design exceptions and 

incorporating performance-based practical design into traditional design analysis. For example, 

project-level analysis could indicate the optimal cross section when repainting centerlines and 

edgelines on rural roads by using SPFs to minimize the crash frequency on an average section. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Many agencies have seen value in using project-level safety analysis for justifying design 

exceptions. Design exceptions are situations where an agency seeks to build a roadway in a way 

that differs from existing design standards. Analysts can assess specific design parameters to 

evaluate their impacts on the proposed design. Rather than standards-based or subjective 

means of justifying design exceptions, project-level safety analyses provide a more quantifiable, 

defensible, and reliable means of evaluating design solutions. Agencies have demonstrated 

significant cost savings as a result of these practices. 

Safety Analysis in Project Development – Louisiana 

The Louisiana Department of Transportation and Development implemented safety 

practices into the project development process, resulting in a safety focus from the 

beginning of a project’s life. Louisiana’s designers and engineers use crash data, the CMF 

Clearinghouse, and B/C analysis to select the alternative with the highest return on 

investment. An alternatives analysis with the IHSDM software led to design exception that 

justified a low-cost countermeasure instead of a costly curve straightening project on 

Interstate 10. They’ve also integrated safety into maintenance and work zone safety.(H) 

Construction, Operations, and Maintenance 

Agencies can also apply project-level safety analysis later in the project development process. 

Detailed analyses can help maximize the safety performance of traffic control plans during 

construction or work zone designs and operations. After construction, agencies can use safety 

analysis tools to help monitor how the project is operating, comparing safety performance to 

what was predicted and then refining the tools for future use. Agencies can also use safety 

analysis tools to identify future maintenance needs. Information collected during maintenance 

efforts can benefit future project-level safety analysis for other projects. 

Maintenance Teams Support HSIP – Arkansas and Arizona 

Arkansas DOT recognized the extensive knowledge maintenance staff has of the roadways 

and now uses these crews to analyze crash sites and recommend next steps for addressing 

related safety issues. Similarly, Arizona DOT Engineering districts use maintenance records 

to identify potential safety concerns if specific sites are repeatedly being reported for 

maintenance.(I) 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

IMPLEMENTING SAFETY ANALYSIS THROUGHOUT THE PROJECT 

DEVELOPMENT PROCESS 

The preceding sections of this chapter address the situation where an agency develops safety 

improvement projects that they select and plan with safety analyses. The same analyses are 

applicable to other types of projects, such as those for condition improvement (i.e., pavement 

rehabilitation), capacity and operational improvements, and economic development. 

Agencies make safety decisions during planning and design, although the safety implications of 

the choices may not be explicitly recognized. Quantitative safety analyses allow planners and 

designers to use data to drive safety decisions. Regardless of the project’s purpose, agencies can 

predict the anticipated number and severity of crashes based on the SPF specific to that facility 

type and apply any relevant CMFs to raise or lower the expected crash counts accordingly. 

Agencies can analyze projects to distinguish which produce a project with better anticipated 

safety performance, in the same way that agencies currently quantitatively analyze the impacts 

of cost, environmental factors, right-of-way, asset condition and maintenance, operations, and 

other considerations. These analyses can occur in planning to weigh safety in programming 

decisions for all projects or later in project development to drive more defensible design 

decisions. 

The application of project-level safety analysis to justifying design exceptions on all types of 

projects is also valuable to the credibility of the process. Agencies can use safety analysis to 

optimize the design, considering both safety and cost. Planners, project managers, and 

maintainers can also better consider the complete life cycle costs and benefits of all projects to 

improve their stewardship of public resources and funding. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

CHAPTER 4—PRIORITIZING DATA ELEMENTS
 

This Guide will help agencies prioritize data needs for safety planning and program management 

as well as in the broader consideration of safety in all highway programs and projects. While 

safety improvement programs may be a more familiar domain for State and local safety staff, 

projects in the HSIP are not the only projects that address or influence safety. There is a much 

greater level of investment in projects under other programs. The consideration of safety in all 

highway projects is essential to reach the goals of achieving a significant reduction in fatalities 

and serious injuries. 

The overall prioritization process begins with identifying business needs and a gap analysis of 

available and required data elements. Users will prioritize the subset of target data elements 

based on many factors and preferences including data requirements for desired analyses, 

funding considerations, regional and jurisdictional considerations, and potential for data sharing. 

Agencies can set priorities for data collection efforts as well as data elements that they should 

maintain at a high quality. These considerations fit within a data governance framework in which 

subject matter experts, data providers, data managers, and IT staff work together to define data 

needs, set data standards, and, ultimately, establish data priorities. 

Agencies should consider all information available to them when prioritizing data needs. 

Appendix A presents two example applications of the prioritization method presented below 

with varying levels of existing data, desired capabilities, and complexity. 

IDENTIFYING DATA TO PRIORITIZE 

The beginning steps of the prioritization process formulate the subset of data sets or elements 

that support the agency’s business needs and desired analysis capabilities. It is not necessary to 

prioritize all potential data; rather, it is best to determine realistic priorities commensurate with 

an agency’s goals and resource constraints. MIRE is a helpful resource for agencies in identifying 

potentially useful data elements and their importance in analysis, as well as developing their own 

dictionary of roadway elements. 

Figure 9 presents the prioritization process. The first four steps prepare for prioritization in the 

fifth step. 

Figure 9. Flow chart. Data prioritization process. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Step 1: Define Business Needs 

The first step of the process is to define the agency or organization’s current and future 

business needs. A business needs analysis should document and map out business functions in 

terms of users and services they provide. The later steps in the prioritization process will 

benefit from a complete business needs analysis of safety, asset management, planning, design, 

operations, and other areas that would potentially use safety data (although focusing solely on 

safety program needs may be appropriate for some agencies). 

Agencies can define business needs in several ways. A straightforward method is to first list the 

business areas and types of data users across the organization. Agencies can define business 

areas by the various bureaus or sections within their agency, or by the needs of each highway 

program that the agency supports. Then, work with each business area or user group to 

identify how they use (or potentially could use) safety data to perform the job functions they 

are already doing, as well as what safety data they would need to better incorporate safety into 

their business practices. 

Agencies can use the following process to document future business needs: 

1.	 Determine why a change to existing processes is necessary. 

2.	 Explain the problems that changing existing practices will solve. 

3.	 Define the goals for implementing the change. 

4.	 Outline the expected outcomes and benefits to all users from the change. 

By working through these steps, managers should have a complete understanding of the 

business needs and justification for improving data to support a change in business processes. 

From a safety perspective, agencies’ existing business needs revolve around implementing SHSP 

emphasis area strategies and developing and implementation of the HSIP. Many agencies also 

have other ongoing safety initiatives within the organization. Existing safety programs are an 

excellent springboard for improving data and prioritizing data needs. Future business needs may 

represent improvements to the delivery of these programs. For example, an agency may decide 

to collect new data that allows them to plan more specific safety improvement strategies within 

an SHSP emphasis area or implement advanced project selection methods that allow an agency 

to spend HSIP resources more wisely. 

The following are examples of safety-related business needs: 

•	 A State is preparing for a major update to their SHSP. The State is interested in 

collecting additional data that may help to more effectively analyze crash patterns and 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

trends for selecting emphasis areas, develop related strategies and goals, set realistic 

performance targets, and measure performance. 

•	 A State’s SHSP identifies roadway departure, intersections, and pedestrians as emphasis 

areas that collectively contribute to a majority of fatalities and serious injuries. A local 

agency is interested in collecting additional data to advance their capabilities to analyze 

these safety problems and more effectively develop safety improvement projects. 

•	 A transportation agency responsible for the design of highway facilities has identified a 

strategic goal to integrate quantitative safety analysis into the design process and 

improve the expected safety performance of projects. The agency is interested in 

collecting and maintaining data to support the use of safety performance functions and 

crash modification factors in the project-level planning, alternatives analysis, and 

preliminary and final design phases of project development. 

Generally, agencies have safety data needs in other business areas. Agencies that own roads or 

are responsible for highway design and maintenance have roadway safety data needs throughout 

planning, programming, and the project development process. Safety stakeholders and agencies 

responsible for non-engineering initiatives have different needs that focus on crash events and 

driver data. It is helpful to formalize these needs and document them to guide the rest of the 

process. This first step—defining the business needs—lays the groundwork for identifying a 

subset of data elements to prioritize in later steps. 

In most States, roadway departure and intersection crashes are major roadway-related crash 

concerns and play a role in data prioritization. For example, in the peer exchanges following the 

United States Roadway Safety Data Capabilities Assessment in 2012, States ranked intersections 

and horizontal curves as the data inventories they would most like to develop.(43) 

Step 2: Identify Desired Analysis Capabilities and Data Requirements 

Next, given formalized business needs, stakeholders should build on the previous step by 

identifying as specifically as possible any analyses that an agency would like to start doing or the 

analyses they would like to conduct at a more advanced level. Whereas the prior step aimed at 

identifying general business needs and priority areas for safety data, this step should start to 

focus those in terms of analysis capabilities that an agency will need safety data to support or 

develop. 

SHSPs, data plans, or program assessments often identify areas for data or analysis 

improvement. However, if an agency has not defined specific areas for improvement, it may 

help to start this step by reviewing their current analysis practices and trying to identify where 

there are realistic opportunities to improve. Chapter 3 presents various analysis types and 

levels of capability within each analysis as considerations for working through this step. 

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A formal, agency-wide data governance process is an ideal way to develop all the necessary 

documentation describing existing data assets. For agencies already engaged in formal data 

governance, inventories already exist or are in the planning stage. For agencies where this 

would be a new project starting from scratch, it may be possible to use safety data as the test 

case to pilot or accelerate the process. It is also possible, although not ideal, for safety 

practitioners to conduct the review themselves without the benefit of a data governance group 

or formal process. The agency may wish to seek technical assistance and involve its many 

stakeholders through the State’s Traffic Records Coordinating Committee (TRCC), the SHSP 

planning group, or other existing working group dealing with safety data and decision making. 

The list of desired data elements should be comprehensive and not omit data that the agency 

has already. Users should not leave elements off the list if a business unit is not currently 

prepared to make use of the information, as future needs are also important to consider. Later 

steps will help prioritize between maintaining existing data and collecting new elements. 

In this step of identifying desired analysis capabilities and data requirements, the following are 

special considerations when developing new data inventories or collecting data to support the 

implementation of software analysis tools: 

•	 Developing roadway data and asset inventories: Each type of site, asset, or data 

set has different data elements that pertain to it. Pedestrian and bicycle facilities, 

intelligent transportation systems (ITS), roadside hardware, road signs, and other assets 

have unique subsets of data that describe those features. 

Agencies should identify the data sets and inventories that they are interested in 

developing to guide the selection of elements to make up those data sets. At this stage, 

users should list all potential data elements that could be included in these inventories 

and then prioritize the data elements in a later step. 

Horizontal Curve Database – Tennessee 

The Tennessee DOT created a horizontal curve database with approximately 40,000 miles 

of roadways with curves. Tennessee DOT uses the horizontal curve database to provide 

quick, data-derived answers to public concerns, make data-driven decisions, quantify 

potential benefits from both systemic and spot improvements, and compare curves with 

similar characteristics to help prioritize projects.(J) 

•	 Developing data to support software analysis tools: Agencies may need additional 

data to develop, implement, or expand implementation of software analysis tools. Every 

software package or tool has unique data requirements and data dictionary. Agencies 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

implementing software tools should review the software documentation to determine 

the required data, optional data, data definitions, network requirements, and other data 

considerations such as storage, maintenance, and integration. Paying attention to this 

information will save time and help organize data needs. If the software implementation 

represents the desired analysis capabilities for the agencies, then the agency will only 

need to collect the data required or used by the software. 

Many software tools have modules or separate functions that require different sets of 

data. In some cases, agencies may decide to use only some capabilities of a software tool 

or start with some and develop more of the capabilities over time. The list of desired 

data elements should encompass those elements needed to support the desired analysis 

capabilities. 

Step 3: Catalog Available Data 

The next step is to identify and catalog currently available data, where the data is stored, the 

responsible group or person within the organization, and the data quality. Data can be stored in 

many forms such as database tables, static computer files, pictures, video, or physical 

documents. Each of these has a varying level of accessibility and timeliness. Agencies should 

conduct a review of data quality, as accessible data formats are not always more timely, 

complete, or accurate. 

Users should contact data owners to discuss data quality and accessibility. In some cases, users 

may not have access to the data or the data may require a usage agreement. These are 

important factors in prioritizing data collection and maintenance efforts. 

Users may find it helpful to first look within their agency to determine the data maintained 

within the organization and then look outward to partners and other stakeholders to 

investigate their available data. All State DOTs have some existing databases including a road 

inventory, HPMS database, and traffic count database. State DOTs are also developing or have 

submitted to FHWA a linear referencing system (LRS) for the All Road Network of Linear 

Referenced Data. These are good places to start cataloging safety data resources. Ideally, the 

data catalog will expand to include information about systems that support business functions 

like asset management, planning, design, operations, maintenance, and other units outside of the 

safety office. These groups have data that are needed for some safety analyses. They also may 

need some of the safety data if they are going to help the agency meet a goal of considering 

safety throughout these multiple business processes. A common point among all these units is 

geographic location of sites—spatial data describing each roadway segment, intersection, and 

interchange. Enterprise-wide GIS with other spatial tools and databases can form the backbone 

for all location-based analyses and decision-making. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Step 4: Conduct Gap Analysis 

Agencies should next conduct a gap analysis between the desired and available data elements to 

determine the subset of data elements that the agency needs to prioritize. The gap analysis will 

help an agency determine the elements that they need to collect and the currently available 

elements needing improvement. Figure 10 illustrates what a simple gap analysis may look like 

for basic intersection data. The intent is to map between available and desired data to identify 

any differences, or gaps. The gaps in this example are highlighted in Figure 10. 

Figure 10. Graphic. Simple gap analysis example. 

After determining the gaps, it is important for users to note whether each element is required, 

desired, optional, or otherwise potentially useful to the desired analyses. Users should also 

explore the optional ways to obtain the data (e.g., data collection or extraction methods) in this 

step. Finally, the gap analysis should note where existing data needs improvement in accuracy, 

timeliness, completeness, uniformity, accessibility, or integration to meet users’ needs (i.e., 

rather than focusing solely on collecting new data). 

The gap analysis results in a subset of data to prioritize. 

Step 5: Prioritize Data 

The final step in the prioritization process is to prioritize the data that the agency needs to 

collect and maintain. Agencies may decide to use numerical or relative priority ranking systems 

to assist in the process. Appendix A provides example prioritization scenarios that demonstrate 

the prioritization process and considerations that agencies may have. The following section 

discusses the six major considerations that affect data priority. 

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PRIORITIZATION CONSIDERATIONS 

After an agency identifies the desired data elements, they must prioritize them. Every agency 

faces resource constraints and a tradeoff between using resources to improve data or support 

construction projects, maintenance, or other safety initiatives. Agencies can use data priorities 

to allocate resources on the most pressing needs. Along with data collection and maintenance 

costs, considerations such as data uses and potential collaboration with other agencies play a 

role in establishing priority. Figure 11 shows six main considerations in data priority. Depending 

on the agency and its specific goals and needs, some of these considerations may play a larger 

role in the prioritization process. The following subsections walk through these considerations 

and provide case studies and example scenarios to show how agencies can use these 

considerations to help make decisions. 

Users may find it useful to prioritize based on some relative priority system, such as a five-point 

scale where a low priority is 1 and high priority is 5 (see Appendix A, Example 1). It is helpful to 

understand what elements have a similar priority and show differences in priority between 

other elements. An advantage to a simple scoring scheme is that it lends itself to gathering 

prioritization inputs from a broad spectrum of stakeholders. One possibility is that the 

stakeholders could meet to discuss the data needs and each person scores each candidate data 

element based on their individual priorities. A composite score for each data element results in 

a value representing the relative priority set by each group. Joint review of the outcome can 

resolve any concerns, such as a low score given to a business-critical data element for one 

business unit. 

Figure 11. Graphic. Main considerations in prioritization. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

The “prioritization prompts” at the beginning of each of the following subsections provide 

sample questions related to that prioritization consideration. Answering these questions should 

help users prioritize their data needs. 

Prioritizing Data by Analysis Type and Usage 

Prioritization prompts: 

•	 What data are required for the type of analysis we want to perform? 

•	 What data are not required but could improve our analyses? 

•	 What is the purpose of collecting and using the data? 

•	 What is the minimum level of data needed to describe an asset and its location? 

•	 What additional elements (i.e., beyond the minimum level of data) may be useful in 

other applications or for partner agencies? 

•	 What elements are more (or less) important in design and operational calculations 

and considerations? 

The simplest and most straightforward factor in determining priority is what the desired 

analysis is and how the analysis uses the data elements. Agencies may value certain types of 

analyses more than others, and the data requirements for those analyses would be higher 

priority for them. Each element has varying importance within a data set, and nearly every data 

element is important for some analysis function. The relative importance of each element 

depends on what the element describes, how the analysis uses the data element, and the goals 

of each agency using the data. These distinctions play a large role in the priority of data 

elements. Each agency will need to decide what is important to them and prioritize data 

accordingly. 

For example, one agency’s strategic goals may reflect a need to begin conducting higher-level 

network screening and evaluations, while another may lean toward integrating project-level 

analysis into design. In either case, the agency should prioritize the data requirements for those 

analyses above other data so that the agency can begin conducting these analyses sooner with 

adequate data and tools. Other factors that may influence whether an agency prioritizes certain 

types of analysis more than others are training, willingness, availability of the analysts or 

designers to implement new analyses, and how much data the agency needs to collect to 

support those analyses (i.e., are there only a few missing elements or is a large collection effort 

required). 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Agencies can think about the relative importance of data in many ways. Most analysis manuals 

and guides have a list of required, desired, and optional data inputs, along with whether an 

analyst needs actual measured data or if it is acceptable to use default values. In the absence of 

such guidance, agencies can consider whether there is an equation or other tool that uses the 

data element or attribute as an input. If not, there may not be a reason to collect the data 

unless there is a plan to analyze the data in another way (e.g., within data summaries, 

performance measures, or developing new analyses). 

Data elements that are required for analyses are the highest collection priority, regardless of 

cost, to achieve a desired capability level. Agencies may determine some data elements are 

desirable—not required, but have moderate priority as they may provide some helpful analysis 

capabilities and insights. Lower priority optional data have varying importance and may only 

provide minor enhancements or make tools easier to use without improving analyses. 

Each agency will have a different priority for the elements needed for their desired analyses. It 

may be necessary to look into more detail about their impact on predicting safety performance 

and risk. For example, most data required in project-level analyses are used in calculating CMFs. 

Each CMF affects the analysis results differently, and those with a wider range of values have a 

greater potential to impact the analysis results and ability to describe the risk of a site. Agencies 

may feel more comfortable making some assumptions about data with CMFs that have a smaller 

variability and lesser impact on analyses. 

MIRE also lists a relative priority rating for each data element. “Critical” elements are necessary 

for agencies to conduct basic safety management analyses. Elements ranked as “value added” 

are those elements that would otherwise be beneficial, provide helpful analysis capabilities, or 

important elements that safety analysis tools will likely use in the future but are not crucial to 

using current versions of such tools. 

When developing new data sets and inventories that very few, if any, available tools are able to 

consume—such as a horizontal curve inventory—the decision of which data elements are 

needed is more subjective. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Using Data-Related Costs to Prioritize Data 

Prioritization prompts: 

•	 How will the data collection method affect the costs for each element? 

•	 How will data maintenance and management requirements affect the cost? 

•	 Is it feasible and necessary to spread data costs across multiple years to lessen the 

budgetary impacts? 

•	 Is there an opportunity to share data collection costs with partners? 

•	 How can we reduce costs by aligning data collection with other activities (e.g. new 

construction or hardware upgrades)? 

One of the most important factors that agencies should consider is the costs and estimated 

level of effort for data collection and management. Resource constraints are the most common 

reason that agencies do not collect more data. Between data collection and data maintenance, 

collection usually takes the most resources and is therefore the primary cost consideration in 

prioritization. 

Data collection includes collecting new elements, collecting new attributes for existing 

elements, and updating old data sets. Data collection costs are largely dependent on the data 

collection method and network coverage (i.e., the number of sites that need data). 

The MIRE Data Collection Guidebook presents the following data collection methods: 

•	 Manual Collection (“foot on ground”). 

•	 Ground-based Imaging (with or without LiDAR). 

•	 Aerial Imaging (with or without LiDAR). 

•	 Data Mining. 

Manual collection involves methods where data collectors visit each site to collect data. 

Manual collection is time consuming, susceptible to poor weather conditions, and exposes 

collection staff to traffic or other potentially harmful conditions, making this method relatively 

inefficient with higher labor costs. However, manual data collection is also highly accurate, 

especially for data elements requiring visual inspection or simple measurements, and typically 

has low costs for equipment and data reduction. 

Ground-based imaging is generated from an instrumented vehicle, and data collectors use 

the imagery to collect various data. This method is moderately efficient, requiring skilled crews 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

to drive the roads to get the imagery as well as a substantial data reduction and post-processing 

effort. Collecting ground-based imagery is inefficient in poor traffic conditions and unfavorable 

weather. However, data collection with this method can result in high data accuracy, especially 

for data elements needing visual inspection of feature presence and type (e.g., number of lanes, 

signs, roadside hardware and conditions, traffic control, non-motorized facilities, etc.). Groundbased imagery (e.g., Google Street View) is free or low-cost from several vendors for higher 

order roadways and more commonly on local roads. There is a high capital cost to obtain or 

contract a vehicle to collect the imagery when it is not available. Regardless of the cost of the 

imagery, data collection with ground-based imagery has moderately high data reduction costs. 

Comprehensive Roadway Data – Utah 

Utah DOT completed the first-of-its-kind data collection of the entire State roadway 

network using LiDAR, which created three-dimensional models of the roadways. Utah 

DOT used these data to develop asset inventories and as a basis for safety analyses. 

Ultimately, Utah DOT was able to improve data management of roadway assets and 

provide comprehensive data to many types of analysts.

(K) 

Aerial imaging methods involve gathering data from aerial photogrammetry. Data collectors 

work in the office, and data collection from aerial imagery can be highly efficient. Aerial imagery 

is free from a number of vendors, but has moderate to high costs when such imagery is not 

available or the quality is poor and the agency needs to buy imagery or hire a vendor to collect 

the imagery. Readily available aerial imagery can have tree cover, discoloration, or other issues 

that limit accuracy or prohibit collection. Higher level of accuracy in aerial imaging typically 

carries higher costs. Data collection with aerial imagery is preferable for collecting geographic 

location information or plan view dimensions, such as cross section widths and length of 

auxiliary lanes. 

Data mining is the process of using readily available data to compute or extract other data 

elements or attributes. Highway agencies can derive new safety data from existing asset 

inventories, operational data, as-built plans, records, pictures, video, and other information. The 

level of effort and usefulness of this method depends on the existing data quality and type. 

Table 5 presents a summary comparison of the various data collection methods. 

These data collection methods are not mutually exclusive. In many cases, supplementing one 

method with another as part of a data collection program or project provides a higher level of 

efficiency and lower cost. Additionally, an agency could collect data quickly with one method at 

lower accuracy and correct errors over time with a higher accuracy method to balance costs 

and not wait for the highest accuracy data before conducting analyses. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Table 5. Comparison of data collection methods. 

Collection Method Efficiency Accuracy Capital 

Costs 

Labor 

Costs 

Risk to 

Collection 

Staff 

Manual Methods Low High Low High High 

Ground-Based Imaging Moderate High Varies Moderate Moderate 

Aerial Imaging High Moderate Varies Moderate Low 

Data Mining High Moderate Low Varies Low 

Agencies can consider whether it is reasonable to use data mining along with various 

assumptions about certain elements to generate a baseline of data, which future data collection 

could improve upon. For example, determining intersection traffic control is difficult without 

ground-based imagery or foot on ground data collection. If an agency has an inventory of traffic 

signals or controller hardware with traffic signal locations, it may be reasonable to assume the 

majority of the remaining intersections have stop control on the minor road as a starting point, 

especially for State routes. An agency could apply this assumption and have reasonable 

estimates of traffic control for intersections almost immediately. Further data collection efforts 

could correct the attributes as needed. 

Another example is related to the collection of AADT. Traffic volume is costly to measure; 

however, regression analyses, travel demand models, ITS infrastructure, and other methods for 

estimating traffic volume have shown promising results and may be worth considering as an 

alternative to a relatively costly short count collection program for all roads. FHWA’s Traffic 

Monitoring Guide has more information about collecting traffic volume data.(44) 

Finally, collection costs vary by the number of sites. For instance, some elements are only 

relevant in urban areas or for freeways. Others are costly to collect for the whole system, so 

the agency may elect to collect the data only for State routes or across certain regions. The 

MIRE Fundamental Data Elements Cost-Benefit Estimation guide discusses specific costs and a cost 

estimation methodology that agencies can apply to other data.(10) 

Overall data collection costs are related to the collection method and the desired coverage and 

accuracy of the data. Once an agency collects data, data owners should maintain the data so it 

remains timely and accurate. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Using Existing Data and Management Needs to Prioritize Data 

Prioritization prompts: 

•	 Is more investment needed to update existing systems prior to investing in new 

data? 

•	 Is it more effective to improve existing data or data management processes than to 

collect new data? 

•	 How should we balance resource allocation for new data collection and continued 

data maintenance? 

Most State and local roadway inventories and other databases were not originally developed to 

support safety analyses, and do not always cover all important data elements and attributes. 

However, all agencies should have at least some basic roadway data elements already available 

to them, such as geographic location information, surface type, and cross section data. Agencies 

may prefer to prioritize improvement and maintenance of data elements collected or partially 

collected before collecting new data. Often there is potential to make available data more 

accurate, complete, and timely. Spending resources on new data adds to the resources needed 

to maintain all data, and the level of investment in data maintenance may not have been 

sufficient in the first place if available data are not satisfactory for analysis. Consider that 

developing a system for effective data management and maintenance before collecting new data 

may be beneficial. 

Using Regional and Jurisdictional Data Needs to Prioritize Data 

Prioritization prompts: 

• Do certain regions or jurisdictions have a greater need for safety improvements? 

• Is it important to prioritize building upon data for the State system, or to collect 

baseline data for all sites in regions with more limited data (e.g., local roads)? 

Sites within certain regions or jurisdictions may have varying priorities for safety and data 

depending on the extent of the system, strategic goals, and distribution of crashes across sites. 

One aspect of this is the number of sites for which the agency needs the data. For example, a 

rural State may have relatively few intersections with traffic signals compared to stop control. It 

may be easier and cheaper to collect data for the traffic signals before launching a large-scale 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

data collection effort for the stop-controlled intersections, resulting in a higher priority for data 

needs at signals. 

The priority in this area is unique to each agency. The following examples further demonstrate 

hypothetical situations affecting data priority. 

1.	 If 70 percent of serious crashes in a State occur on local roads, local data collection and 

maintenance could take on higher priority for that State. 

2.	 A State needs to collect some additional data to increase their network screening 

capability to advanced level. The agency could prioritize collecting data for roads on the 

State system because they likely have LRS and HPMS coverage already and those roads 

can serve as a good starting point for a complete data set. Over time, the agency could 

collect data on local roads where data needs and costs are higher. 

3.	 States that have established regional, county, and local safety plans may place a higher 

emphasis on collecting data elements outside of the State system. This often leads to 

greater interagency cooperation and data sharing between local agencies and the State. 

County Safety Plans – Minnesota 

Minnesota has developed individual safety plans for each to the State’s 87 counties. This
 

process helped identify curves with 500 to 1,200 foot radii to consider for systemic
 

improvements.(L)
 

Data Variability Over Time 

Prioritization prompts: 

•	 How do data elements vary over time? 

•	 Do available data sources provide accurate representations of past and current 

roadway conditions? 

•	 Should collection of geometric or operational roadway data elements be higher 

priority? 

Another factor that agencies should consider is the variability of data over time. Geometric 

data are inherently more static, often representing unchanging conditions over many years. On 

the other hand, operational characteristics are constantly changing. While a single data point for 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

the number of through lanes on a road may be accurate for several past decades, the AADT for 

the same roadway changes annually. 

Safety analysis is most accurate when a database contains a history of roadway conditions that 

allows analysts to understand the roadway characteristics present at the time of each crash. 

Agencies may derive some benefit from collecting operational data sooner and more regularly 

to develop a record of more data points. In contrast, an agency might collect geometric data 

once and only update the data as construction or other changes occur. 

Using Data Sharing Considerations to Prioritize Data 

Prioritization prompts: 

• Do other agencies or business areas have a need for any data? 

• Would other agencies lead or support data collection and maintenance efforts? 

Finally, it is important for agencies to consider intra-agency and inter-agency uses for data. 

Other groups or partner agencies may have some use for the same data that the lead agency 

needs. The following is a list of groups and applications that share some data needs with safety 

analysis: 

• Asset management and maintenance. 

• Planning. 

• Traffic engineering. 

• Transportation systems management and operations and ITS. 

• Environmental and right-of-way. 

These other uses can bring additional returns on the investment in data collection and 

maintenance, and thus an agency could decide that the elements that have other potential uses 

have a higher priority. This is especially true when the other groups or partner agencies can 

help provide resources to support or lead the data collection and maintenance efforts. 

Conversely, data elements that only one agency will use may be lower priority. 

SUMMARY OF PRIORITIZATION 

As described in this chapter, the data prioritization process involves several steps with many 

considerations for States. Throughout the process, States should be deliberate with all 

stakeholders when making decisions. The initial step is developing a subset of elements to 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

prioritize by answering questions about the agency’s business and data needs. This may include 

identifying the intention, use, minimum level needed for further analysis, additional elements to 

collect, and information the State needs to answer those questions. States should also consult 

the myriad of available resources—such as the MIRE Data Collection Guidebook—for more 

information during each step of the process.(11) 

Once States have identified the data elements of interest, they should work with partner 

agencies to prioritize the needed elements. Considerations in prioritization include the cost to 

collect, the use of the elements, and the potential for collaboration with partner agencies. This 

process often requires financial and staffing investments—both at the beginning of the process 

and throughout its lifespan for ongoing data collection and maintenance. Some States may view 

data prioritization as a long or overwhelming process, but investing resources and time on the 

front end will lay the groundwork for developing useful, sustainable data systems for the future 

to support safety analyses. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

CHAPTER 5—MOVING BEYOND PRIORITIZATION
 

Data prioritization, as discussed in Chapter 4, is one component of a larger data management 

life cycle and governance framework. When prioritizing data, agencies should work with interagency and intra-agency partners and stakeholders who could help develop data priorities and 

provide additional insights from other areas of the agency to inform the process. Enterpriselevel data governance, data management structure, and resource constraints influence safety 

data priorities. After developing data priorities, the agency should move to collection and 

maintenance of the data. The Guide for State DOT Safety Data Business Planning presents the data 

life cycle in Figure 12. This data life cycle is guided by formal data governance and planning. 

Collect 

Analyze 

Deliver Process 

Use 

Figure 12. Graphic. Data life cycle.(45) 

Data prioritization is most likely a precursor to data collection efforts. Chapter 4 highlights the 

need to consider factors relating to all steps of the data life cycle in prioritization including 

collection costs, analysis usage, processing and data mining, and collaboration. This chapter 

discusses steps after prioritization including data governance and developing data business plans. 

The Guide for State DOT Safety Data Business Planning provides more information on these 

topics.(45) 

DATA COLLECTION AND INTEGRATION 

Following data prioritization, agencies plan future data collection and maintenance efforts for 

the newly prioritized data. Agencies should have some estimate of data collection and 

maintenance costs from the prioritization process, and can compare those to their budget to 

determine how much of the data they can afford to collect and determine a schedule for data 

collection efforts. 

Agencies can collect various data elements all at once or collect elements over time to build a 

dataset. Budget and staff limitations are the major limitations for data collection activities. Given 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

the necessary resources and a greater urgency, a single data collection effort to gather all data 

elements of interest may be more desirable. Alternatively, an agency may decide to balance data 

collection costs over multiple years by collecting the higher priority and cost-effective data 

elements earlier (or data for higher priority networks or regions). This lets the agency begin 

conducting the more important analyses sooner, leaving the lower priority data collection for 

later years. 

Agencies should explore the tradeoffs of how to schedule and plan data collection efforts. 

Generally, if resources are limited, it will be most cost effective for agencies to limit or split 

data collection efforts by the number of sites included in the effort, rather than the number of 

elements. In other words, if an agency undertakes a data collection effort using aerial imagery, 

they should collect all desired data elements that can be collected with aerial imagery for each 

site that data collectors visit, rather than collecting only some elements for each site and 

revisiting the same sites later to get the rest of the elements. 

Similarly, agencies should explore the similar elements that can be collected together, or those 

that can be collected once and then used in or translated to multiple data sets. Agencies should 

decide whether to build each dataset independently or concurrently. 

The next step for most agencies is to draft a Data Collection Action Plan to document the 

results of the prioritization, lay out specific action items and a timeline for data projects, and 

garner buy-in as future efforts proceed. 

Developing a Data Collection Action Plan 

Data Collection Action Plans are the roadmap for an agency to go from their current status to 

their final, desired status in terms of data. The agencies participating in data activities should 

decide what specific contents and level of detail go into the Data Collection Action Plan. 

Table 6 and Table 7 are excerpts of an example Data Collection Action Plan for data collection 

and enhanced analysis for a State DOT. These tables represent the specific steps toward 

reaching the goal of collecting MIRE FDE on all public roads (Goal 1). General actions are 

categorized in two steps: contract services and database preparation (Action 1) and implement 

MIRE FDE with Statewide accessibility (Action 2). 

53
 
Table 6. Action plan excerpt for Goal 1, Action 1. 

Step # Description Preceding 

Tasks 

Following 

Tasks 

Who? Start 

Date 

End 

Date 

Status 

1.1.1 Develop collection None 1.1.2 DOT 1/17 3/17 Done 

contract 

1.1.2 Hire vendor 1.1.1 1.1.3 DOT 3/17 4/17 Done 

1.1.3 Approve initial test 

data 

1.1.2 1.1.4, .6 DOT 4/17 5/17 Started 

1.1.4 Establish database 1.1.3 1.1.4 DOT 6/17 8/17 Not 

started 

1.1.5 Establish quality 

control (QC) Plan 

1.1.4 Action 2 DOT 8/17 10/17 Not 

started 

1.1.6 Train DOT Staff` 1.1.3 1.2.4 DOT 7/17 8/17 Not 

started 

1.1.7 Train selected local 

staff 

1.1.6 1.2.5 Local 9/17 10/17 Not 

started 

 

 

 

PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Table 7. Action plan excerpt for Goal 1, Action 2. 

Step # Description Preceding Following Who? Start End Status 

Tasks Tasks Date Date 

1.2.1 Collect MIRE FDE on 

all roads 

1.1.5 1.2.2 DOT 10/17 12/18 Not 

started 

1.2.2 Complete QC 1.2.1 1.2.3 QC 

Group 

11/17 03/19 Not 

started 

1.2.3 Implement GIS 1.2.2, .3 Action 3 GIS 

Group 

11/17 04/19 Not 

started 

This example Data Collection Action Plan excerpt shows each goal and its associated actions to 

achieve the goal. For each goal and action, the plan shows a series of steps with a brief 

description of what is to take place. The steps follow a logical sequence shown in the list of 

preceding and following tasks at each step. This shows the critical path for the data collection 

process. The responsible party or parties are listed in the “Who?” column. This identifies the 

responsible agency and any needed partners. Start and end dates show the planned schedule of 

steps. They also help to show which tasks can run in parallel. A status indicator shows how 

much of the project is completed, and gives managers an easy way to identify when steps are in 

danger of not being completed on time. 

This example is not prescriptive. It resembles an example State’s plans to improve roadway and 

crash data over a period. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Using and Maintaining a Data Collection Action Plan 

A Data Collection Action Plan should include the agency’s goals, their corresponding actions, 

and the steps to complete each action. Agencies should update and modify the plan as projects 

progress and as the agency’s goals change over time. Agencies should designate a champion to 

compile relevant information from stakeholders and to continually maintain the plan, beyond 

the simple tracking shown in Table 6. The champion should be able to, at any time, provide 

managers with an update on overall progress and specific information about any steps in the 

plan. With this structure, managers should know if the goals are met, if projects are completed 

on time, and if there are problems that their staff need to overcome to facilitate progress. 

Project management techniques and tools are beyond the scope of this Guide; however, 

agencies are familiar with standard management methods and should consider managing the 

Data Collection Action Plan the same way they would any project management task. The 

example in Table 6 has the necessary information to implement a critical path method, Gantt 

charts, or other project management methods. Agencies should modify the Data Collection 

Action Plan contents to suit their own project management methods. 

The Data Collection Action Plan could easily feed into a larger Data Business Plan that 

documents enterprise data vision, goals, objectives, and actions; guides data management 

practices; and identifies opportunities to use data to improve business operations. The two 

plans together address the State’s need for improved data and improved analyses to support 

safety decision making. 

DATA GOVERNANCE AND PARTNERSHIPS 

Data governance represents a formal structure of authority over the processes of data 

collection, storage, security, inventory, analysis, quality control, reporting, and visualization. 

These processes have a potential influence on data priority in a limited resource environment. 

Formalizing data priorities, data governance, and data activities in a Data Business Plan is a 

practice that encourages successful implementation and maintenance. The agency’s Data 

Business Plan should include their safety data priorities. This will support future data collection 

and updates, agency performance management, and target setting. 

Safety data stakeholders should collaborate with those involved in data governance at the 

agency to promote integration of safety data with other data sets. Numerous partners, from 

within the agency and with external partners, are involved in successful data systems. Data 

governance provides a necessary framework for these diverse partners to work together. In 

many States, the TRCC fosters this collaborative structure and articulates the data governance. 

Figure 13, from National Cooperative Highway Research Program (NCHRP) Report 666: TargetSetting Methods and Data Management to Support Performance-Based Resource Allocation by 

Transportation Agencies (Figure 2.4), below, presents an example data governance structure.

(46) 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Figure 13. Chart. Standard data governance model.(46) 

Incorporating safety data as part of an agency’s enterprise-level data efforts provides 

opportunities to leverage the resources in other business areas and improve data accessibility 

throughout the organization. 

Data Governance Development – Utah 

The Utah DOT is one of many agencies using innovative tools to manage its data. Utah 

DOT shares and manages data using its centralized data portal, UGate. The data on UGate 

is downloadable and accessible; most is available to the public. Utah DOT engineers use 

UGate data in their safety analysis tools. As the agency develops UGate 2.0, the expansion 

of technology combined with the visibility of the data means that any data quality or data 

management issues are increasingly perceptible. Therefore, data governance has become a 

high priority for Utah DOT.(M) 

Additional Resources 

There are several resources that agencies can use when considering data integration as a 

strategic initiative. The following resources provide guidance, assistance, and needs assessments: 

• Roadway Data Improvement Program (RDIP): 

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

The RDIP provides assessment and technical assistance on roadway data collection and 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

standards. The program addresses data sharing, management, governance, and 

integration. It also covers analytic tools and their use in safety management. 

• Crash Data Improvement Program (CDIP): 

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

The CDIP provides assessment and technical assistance on crash data collection and 

standards. The program addresses data management, data quality, performance 

measurement, and data integration. 

• Every Day Counts Round 4 (EDC-4): Data-Driven Safety Analysis (DDSA): 

https://www.fhwa.dot.gov/innovation/everydaycounts/edc_4/ddsa.cfm 

EDC-4 DDSA employs state-of-the-practice, evidence-based methods and analytic tools 

to provide agencies with the means to quantify safety impacts of agency projects and 

decisions. It includes predictive and systemic analyses to identify sites with the best 

potential for safety improvement and roadway features that correlate with specific crash 

types. The DDSA model leads to informed decision-making, targeted investment, and 

improved safety. 

• State Safety Data Capabilities Assessments: 

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

https://trid.trb.org/view.aspx?id=1301939 

The Safety Data Capabilities Assessments are now entering the second round. In the 

first round (see links above), the States, plus District of Columbia and Puerto Rico, 

participated in assessments of their roadway inventory data collection, data analysis 

tools and uses, data management, and data interoperability and expandability (including 

data integration). The second-round capabilities assessment will update the information 

from the first round, and add a new assessment of safety performance management. 

• Traffic Records Assessments: 

https://www.transportation.gov/government/traffic-records/traffic-records-assessments 

Traffic Records Assessments (TRAs) are designed to follow the Traffic Records Program 

Assessment Advisory (DOT HS 811 644). TRAs cover the six core traffic records systems 

(crash, roadway, driver, vehicle, citation and adjudication, and injury surveillance), plus 

data management, traffic records coordination, and data integration. States are assessed 

on a five-year cycle using a standardized process and a team of subject matter experts 

to develop a final status report with a list of improvement opportunities. 

• Performance Measures for Roadway Inventory Data: 

The Performance Measures for Roadway Inventory Data guide reviews each of the data 

quality performance measures defined by NHTSA: timeliness, accuracy, completeness, 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

uniformity, integration, and accessibility.(15) The guide also recommends modifications 

and additions to the existing NHTSA performance measures specific to the MIRE data 

elements. Furthermore, the guide enumerates a series of suggestions for data-related 

business practices that can lead to the successful implementation of data quality 

performance measures and improved roadway data. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

CHAPTER 6—SUMMARY
 

The Priorities in Roadway Safety Data Guide is the capstone of more than five years of related 

projects aimed at helping States, Tribes, and local agencies develop complete data to support 

advanced safety analysis and decision-making. This Guide follows and promotes the data driven 

approach to safety decision making as described in Chapter 1. The philosophy is simple: better 

data leads to more informed decision-making, leading to efficient use of resources, which 

results in reduced fatalities and injuries. Chapter 1 also describes the legislative and policy 

context for States working with local agencies to develop all-public-roads data to support 

decision-making. This includes, but is not necessarily limited to, the core safety data sets of 

roadway inventory, traffic volume, and crash data. This Guide is not prescriptive with respect 

to which agencies lead the projects, how the partnering agencies work together to develop and 

achieve a set of shared goals, or how the agencies implement the desired data and analyses. The 

Guide includes examples chosen deliberately to illustrate multiple paths to success. 

Chapter 2 describes the MIRE and MIRE FDE, which are great starting points for agencies 

beginning to prioritize data needs. Collaborating agencies should consider MIRE and related 

products as guides in conducting a data gaps analysis and when determining the data needs for 

specific analyses and analytic tools. 

Chapter 3 explores the wide variety of analyses that decision-makers may rely on to allocate 

safety resources at the State, Tribal, and local levels. This chapter is also not prescriptive. It 

offers Guidance on how to implement advanced safety analyses with specific focus on the data 

needed to support those analyses and tools. Project-level and system-wide safety approaches 

are included so that partner agencies can identify data needs for any of the advanced analytic 

approaches recommended by FHWA, AASHTO, and others in already-published resources. 

Chapter 4 describes methods for prioritizing data elements for collection and maintenance. The 

model is of an inclusive process addressing needs throughout safety, engineering, planning, 

design, operations, and maintenance. Partner agencies, and offices within those agencies, should 

work together to identify, prioritize, and obtain the data needed for all their operations. The 

Guide promotes safety as a consideration throughout all processes that affect roadways. 

Chapter 4 explains how to conduct a data gap analysis among all relevant stakeholders and 

arrive at a logical set of data priorities that can be reflected in a Data Collection Action Plan 

and an enterprise Data Business Plan. 

Chapter 5 describes Action Plans as the basis for achieving a set of defined data collection and 

management goals. The Action Plan is a companion to existing strategic planning efforts (such as 

the SHSP, the HSP, and the Traffic Records Strategic Plan). The Action Plan focuses entirely on 

implementation. It shows what the steps are to achieve the data collection and analysis goals 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

established by the partner agencies. The Action Plan is a dynamic document subject to updates 

as projects progress and situations change the list of desired activities. 

Finally, this Guide is a practical resource. Partners among States, Tribes, and local agencies can 

use the Guide to establish their own safety data business plans, conduct their own gap analyses, 

identify their own training needs, and develop their own paths to data integration and 

availability. The examples provided in this Guide are all drawn from existing projects in State, 

Tribal, or local agencies. The Noteworthy Practices described in the Guide are posted on the 

RSDP website.

(47) The site links to additional resources, such as full project reports and other 

guides on related topics such as data business planning and data integration. This Guide also 

includes a list of references that partner agencies can use to find out more information. The 

intention was not to duplicate the existing guides but to provide a handy reference and 

sufficient explanatory material to help agencies implement data prioritization. 

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APPENDIX A: EXAMPLE APPLICATIONS
 

This Appendix presents two hypothetical examples to illustrate potential applications of the 

Guide. The first example represents a relatively simple application where a city is prioritizing 

general data collection efforts with limited resources to expand their analysis capabilities. The 

second example describes a more complex prioritization of data elements within an 

intersection inventory. 

While these examples portray simplistic applications of the Guide for hypothetical situations, 

users can identify similar types of considerations when prioritizing data among their partner 

agencies. 

Example 1: Local Data Priorities 

Background 

A State maintains a complete inventory of State-maintained roads, with minimal data on local 

roads. The State has no intersection inventory and sparse data on pedestrian facilities from 

maintenance records. All safety analysis tools and data sets developed by the State DOT are 

available to local agencies within the State. The State has not developed or calibrated any SPFs. 

The State offers some financial assistance to municipalities for collecting data on their public 

roadways to integrate into the statewide road inventory. 

The State’s SHSP identifies roadway departure, intersection, and pedestrian crashes as 

infrastructure-related emphasis areas and highlights local data collection as an important 

strategy across multiple emphasis areas. The other SHSP emphasis areas are related to driver 

behaviors and enforcement. 

The fastest-growing city in the State hears recurring concerns from residents and business 

owners that the transportation system is lacking connectivity between residential, commercial, 

and recreational areas. The city expects retail development to rise over the next couple of 

years to accommodate the growing population within the town and the surrounding areas. 

Residents have expressed the desire to expand non-motorized facilities. There is a need to 

understand the safety impacts of the city’s growth. 

Given the situation, the city identified an opportunity to increase its safety analysis capabilities 

through local data collection efforts. The city has limited resources for safety analysis, but has 

the technical capabilities to collect and manage data. The city hopes to improve its safety 

analysis capabilities to understand the safety impacts of the new infrastructure and maintain a 

high level of safety for its residents and visitors throughout a period of economic growth. Since 

the city does not have a large budget for making safety improvements other than maintenance 

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of existing facilities, targeting the right projects is crucial. The city recognized that the data it 

collects will improve the State’s analysis capabilities to identify potential improvements in the 

jurisdiction. 

Identifying Data to Prioritize 

The city decides to use the prioritization process in this Guide to prioritize databases that will 

support safety analyses. The first four steps determine the data needs that the city will 

prioritize. 

1.	 Define Business Needs: The city currently focuses its safety-related efforts on 

maintaining existing infrastructure and works with the State when the State identifies 

opportunities for safety projects in the city. The city has identified a need to understand 

the impacts of new infrastructure construction and growth. The city would also like 

help improve the State’s capability to identify safety concerns. 

2.	 Identify Desired Analysis Capabilities and Data Requirements: The city 

identified project-level safety analysis as the driving reason for collecting new data and 

expanding analysis capabilities. This level of analysis requires SPFs and detailed data for 

project sites. The city also has secondary goals of collecting basic roadway inventory 

data across its network and analyzing the safety impacts of non-motorized facilities. 

3.	 Catalog Available Data: The State has a complete road inventory for Statemaintained roads that contains sparse local data, no intersections, and some pedestrian 

facility data that is not readily accessible. The city currently does not maintain roadway 

data other than maintenance records and some maps. SPFs are not available. 

4.	 Conduct Gap Analysis: The city needs calibrated SPFs to conduct project-level 

analyses. The city also needs roadway and intersection data for most of its network and 

an inventory of pedestrian facilities to support its secondary safety analysis goals. 

5.	 Prioritize Data: See the Prioritization Considerations section, below. 

The city identified a goal of conducting project-level safety analysis for new developments to 

understand the impacts of growth, particularly the result of higher traffic volumes. Advancing 

project-level analysis capabilities would allow the city to respond to the public’s safety concerns 

and would support the city’s planning efforts and policy development. The city would also like 

to increase its capabilities in identifying safety issues, developing safety improvements projects 

to mitigate safety issues, and prioritizing projects if the budget can support that. 

City transportation staff listed the following actions needed to support their future goals: 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

• Collect roadway segment data to fill in gaps in the State inventory. 

• Collect local intersection inventory. 

• Collect local pedestrian facility inventory. 

• Collect local bicyclist facility inventory. 

• Calibrate urban SPFs from the HSM. 

• Digitize maintenance records of guardrail, road sign, and pavement marking locations. 

Prioritization Considerations 

The next step in the process is to prioritize the city’s data needs. The Guide lists the following 

six prioritization considerations. 

• Analysis type and usage. 

• Data-related costs. 

• Existing data and management. 

• Regional and jurisdictional data. 

• Data variability over time. 

• Data sharing. 

In this situation, the city is most concerned about the costs and data usage in analysis (i.e., the 

importance of the analyses that the data feed into). Data sharing with the State is an important 

secondary consideration. City staff determined the other factors are not as important at this 

time. The following sections capture the city’s prioritization considerations for each data need. 

Roadway Segment Inventory Data Collection 

The State’s roadway segment inventory is sparse on local roads, and this data collection would 

enable the city and State to identify and plan safety improvements across the city. However, the 

city’s ability to construct new projects is severely limited by its budget. This information is also 

costly to collect and maintain, but the structure of the State’s inventory provides a starting 

point for this data collection effort. 

Intersection Inventory Data Collection 

Like the roadway segment inventory, an intersection inventory is costly and has limited 

potential utility for the city. Unlike the segment inventory, the State does not have an 

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intersection inventory to serve as a starting point for the city, and the potential for data sharing 

is lower than with the segment data. 

Pedestrian and Bicycle Inventory Data Collection 

Pedestrian and bicycle facility information will assist the city in analyzing the safety impacts and 

connectivity for non-motorized transportation. The city is interested in the facilities that 

intersect or are adjacent to the roadway, and would focus first on the location and type of 

those facilities. These data cost less to collect than segment and intersection inventory data 

because there are fewer facilities, but the process still requires manual data collection and 

entry. These data may have some use to other agencies. 

Calibrated SPFs 

Calibrating SPFs for all facility types is an intensive effort that carries a relatively high cost. The 

city does not have the capability with in-house staff to calibrate SPFs; however, the SPFs are 

required for their highest priority goal of analyzing impacts of growth and new development. 

Some data sharing is possible, but locally calibrated SPFs are not useful outside of the city or 

region for which they are calibrated. 

Digitized Maintenance Data 

The city has information about the location of guardrails, road signs, and pavement markings in 

its paper maintenance records. This information is not readily accessible for analyses and 

requires manual interpretation and data entry to digitize the location and condition of assets. 

This information can be used for future maintenance and safety considerations during safety 

analysis. Although other agencies would not use the data, the city’s primary responsibility in 

highway safety is maintenance of existing facilities and a digitized maintenance database would 

improve its ability to do that. 

Prioritization Results 

The city used a tabular rating system to apply priorities to each data need. The city staff ranked 

each data need on a scale of 1-5, with 5 representing the highest priority, most important, 

lowest cost data needs. As shown in Figure 14, the overall score is based on the weighted sum 

of the individual rankings, with data costs and analysis usage weighted twice as much as data 

sharing. 

Overall Score = 2 x (Data Cost Rank) + 2 x (Analysis Usage Rank) + (Data Sharing Rank) 

Figure 14. Equation. Example prioritization ranking equation. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Table 8 shows the results of the data prioritization. 

Table 8. Example 1 Prioritization Results. 

Data Need Data 

Costs 

Analysis 

Usage 

Data 

Sharing 

Overall 

Score 

Segment Inventory 2 3 5 15 

Intersection Inventory 1 2 4 10 

Pedestrian/Bicycle Data 3 1 3 11 

Calibrated SPFs 4 5 2 20 

Digitized Maintenance 

Records 5 4 1 19 

Based on the results in Table 8, calibrated SPFs are the highest priority for the city, followed by 

digitized maintenance records. Calibrated SPFs are needed for the most important analyses to 

the city, and are estimated to cost more than digitizing existing maintenance records but less 

than collecting roadway inventory data. Digitizing maintenance records requires the least 

funding but is less important and has the least potential for data sharing. The segment inventory 

has the second highest cost and is less important to the city, but has the highest potential for 

sharing with the State. Non-motorized facility and intersection inventories are the lowest 

priorities due to their cost and lesser importance for analysis. 

Conclusion 

The city decides to move forward with developing calibrated SPFs and digitizing maintenance 

records. They also decide to start the process of expanding their segment inventory; their 

short-term efforts will focus on filling gaps in the State inventory to support SPF calibration. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Example 2: State Data Priorities 

Background 

A State has identified roadway departure safety, intersection safety, and data improvement as 

emphasis areas in its SHSP. The State has elected to prioritize data collection efforts to improve 

safety analysis capabilities regarding roadway departure and intersection safety. The State has 

also identified a need to collect the MIRE FDE in accordance with the FAST Act. 

The State has a linear referencing system (LRS) base map of all public roads that meets the 

requirements of the HPMS and the All Road Network of Linear Referenced Data (ARNOLD). 

A State maintains a roadway segment inventory for non-local (i.e., by functional class) roadway 

segments and all ramps. Segment inventory data for local roads is limited. The State does not 

currently have an intersection inventory or a horizontal curve inventory. The State maintains 

approximately 20 percent of sites. Table 9 presents the number of sites in the State, 

categorized by maintenance type and MIRE FDE category. 

Table 9. Number of sites in the State by maintenance and MIRE FDE category. 

Maintenance 

Type 

Non Local 

Paved 

Segments 

Local Paved 

Segments 

Non Local 

Intersections 

Local 

Intersections 

Non Local 

Horizontal 

Curves 

State 

maintenance 9,000 27,500 4,000 12,000 2,000 

Tribal or local 

maintenance 36,000 110,000 16,000 48,000 8,000 

The State would like to conduct system-wide network screening to identify locations for 

potential for improvement in roadway departure and intersection safety. The agency has 

analysts and analysis tools capable of conducting network screening analysis with SPFs for paved 

roads and curves, but not unpaved roads. The State has decided to improve its safety 

management processes by collecting and maintaining the MIRE FDE on all public roads, as well 

as collecting a basic inventory of horizontal curves. 

Identifying Data to Prioritize 

The State decides to use the prioritization process in this Guide to prioritize their data 

collection efforts. The first four steps determine the data needs that the State will prioritize. 

1.	 Define Business Needs: The State has a desire to provide higher levels of roadway 

departure and intersection safety on all public roads. The State would like to collect the 

MIRE FDE and a horizontal curve inventory to support system-wide network screening. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

2.	 Identify Desired Analysis Capabilities and Data Requirements: The State 

already conducts some advanced network screening analysis for non-local paved roads. 

However, the lack of local road data, a complete intersection inventory, and a 

horizontal curve inventory limits their capability to make more informed project 

decisions and target investments for most sites across their network. 

3.	 Catalog Available Data: The State maintains an incomplete road inventory and has
 

the MIRE FDEs for all interchange ramps. The State has a video log of non-local roads
 

and aerial imagery of all public roads available to them.
 

4.	 Conduct Gap Analysis: The State conducted a gap analysis between available and 

desired data. Table 10 summarizes the State’s existing roadway segment, intersection, 

and horizontal curve data coverage across sites (i.e., 100% indicates all roads have the 

desired data elements, 0% indicates no roads have data) in comparison to desired data 

elements by maintenance type and MIRE FDE category. For this example, assume that 

each site either has all data elements or no data elements. 

Table 10. Available segment, intersection, and horizontal curve data coverage. 

Maintenance - - -

Type 

Non Local 

Paved 

Segments 

Local Paved 

Segments 

Non Local 

Intersections 

Local 

Intersections 

Non Local 

Horizontal 

Curves 

State 

maintenance 100% 70% 0% 0% 0% 

Tribal or local 

maintenance 100% 20% 0% 0% 0% 

From the gap analysis, State staff listed the following actions that they need to take to 

support their future goals. 

•	 Collect MIRE FDE for local paved roads. 

•	 Collect MIRE FDE for non-local intersections and local intersections. 

•	 Collect MIRE data elements for horizontal curves on non-local paved roads. 

5.	 Prioritize Data: See the Prioritization Considerations section, below. 

The State identified a goal of conducting network-level safety analysis for all paved public 

roadway segments, intersections, and curves. Advancing network-level analysis capabilities will 

allow the State to improve decision-making during planning and scoping, resulting in improved 

delivery of roadway departure and intersection improvement projects to support the SHSP. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

In preparation for prioritization, the State compiled Table 10 through Table 12, representing 

the data elements and preferred data collection method for local paved road data elements, 

intersection data elements, and horizontal curve data elements, respectively. “Assign” indicates 

that the State can assign the data attributes directly; no data collection is needed. 

Table 11. MIRE FDE for local paved roads. 

Mire FDE (MIRE 1.0 Number) Collection Method 

Type of Governmental Ownership (4) Assign 

Begin Point Segment Descriptor (10) Assign 

End Point Segment Descriptor (11) Assign 

Segment Identifier (12) Assign 

Functional Class (19) Assign 

Rural/Urban Designation (20) Data Mining 

Surface Type (23) Aerial Imaging 

Number of Through Lanes (31) Aerial Imaging 

AADT (79) Field Collection 

Table 12. MIRE FDE for intersections with paved roads. 

Mire FDE (MIRE 1.0 Number) Collection Method 

AADT (79) [For Each Intersecting Road] Field Collection 

AADT Year (80) [For Each Intersecting Road] Assign 

Unique Junction Identifier (120) Assign 

Location Identifier for Road 1 Crossing Point (122) Data Mining 

Location Identifier for Road 2 Crossing Point (123) Data Mining 

Intersection/Junction Geometry (126) Aerial Imaging 

Intersection/Junction Traffic Control (131) Aerial Imaging 

Unique Approach Identifier (139) Data Mining 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Table 13. MIRE data elements for non-local horizontal curves on paved roads. 

Curve Elements (MIRE 1.0 Number) Collection Method 

Curve identifiers and Linkage Elements (107) Data Mining 

Curve Feature Type (108) Aerial Imaging 

Horizontal Curve Degree or Radius (109) Aerial Imaging 

Horizontal Curve Length (110) Aerial Imaging 

Horizontal Curve Direction (114) Aerial Imaging 

Prioritization Considerations 

The next step in the process is to prioritize the State’s data needs. The Guide lists the following 

six prioritization considerations. 

• Analysis type and usage. 

• Data-related costs. 

• Existing data and management. 

• Regional and jurisdictional data. 

• Data variability over time. 

• Data sharing. 

For this example, the State is most concerned about the analysis type and usage as well as datarelated costs. Data sharing and data variability over time are secondary considerations. 

Jurisdictional considerations and existing data improvement are not important to this 

prioritization effort. The following sections capture the State’s prioritization considerations 

related to their identified data needs. 

The State aims to prioritize data collection efforts on local paved roads, intersections, and nonlocal curves. 

Analysis Type and Usage 

All the State’s data needs represent the minimum data to conduct enhanced network screening 

for each site. The analyses require each element before analysis is possible. However, there is 

some difference in priority related to site types. The State wants to prioritize roadway segment 

collection over intersection and curve elements to complete the segment inventory for all 

public roads. The agency is also able use local roadway segment data subsequently in data 

mining efforts for intersections. Intersection data are the next priority due to their inclusion in 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

the MIRE FDE requirements. Therefore, horizontal curve data are the lowest priority relative 

to segment and intersection data elements. 

Data-Related Costs 

Data collection costs are dependent on the collection method and number of sites. Table 11 

through Table 13 indicates the need for Data Mining, Aerial Imaging, and some Field Collection 

of AADT data. Data mining is based on existing data and the LRS base map. Aerial imagery for 

all public roads is available. Using the summary in Table 5, the State identified the priorities in 

Table 14. 

Table 14. Data-related cost priorities. 

Data Collection Method Relative Cost Relative Priority 

Data Mining Low High 

Aerial Imaging Medium Medium 

Field Collection High Low 

Table 15 lists the number of sites for which the State must collect data in each category using 

the information from Table 9 and Table 10. 

Table 15. Data collection sites by site category. 

Site Category Number of Sites 

Local paved roads 96,250 

Non-local intersections 20,000 

Local intersections 60,000 

Non-local horizontal curves 10,000 

Data Variability Over Time 

The State decides that operational data is a relatively higher priority than geometric data to 

start building a history of data points in AADT. 

Data Sharing 

There is potential to share segment, intersection, and curve data with local agencies for the 

roads those agencies maintain. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Prioritization Results 

Rather than using a scoring system to apply priorities to each data element, State staff got 

together to discuss a plan and schedule for data collection efforts based on the prioritization 

considerations. Table 15 presents the results by site type and data collection method. 

Table 16. Prioritization results by site type and data collection method. 

Site Type Data Mining Aerial Imaging Field Collection 

Local paved roads High Medium High 

Non-local intersections Medium Medium N/A 

Local intersections Medium Medium Low 

Non-local horizontal curves Low Low N/A 

The State aims to use these results to plan and schedule data collection efforts. The data 

collection effort is expected to take several years due to resource constraints. The State 

considered coordinating simultaneous collection with aerial imaging for segments and 

intersections. However, due to funding limitations, the State decided to collect local road data 

prior to intersection data since the efforts will span several years regardless. 

The following presents the tentative schedule for data collection efforts. Some efforts may span 

multiple years. 

1. Begin planning and conducting an AADT data collection program for local roads. 

2. Collect local paved road elements with data mining. 

3. Collect local paved road elements with aerial imaging. 

4. Collect non-local and local intersection data with data mining. 

5. Collect non-local intersection data with aerial imaging. 

6. Collect local intersection data with aerial imaging. 

7. Collect horizontal curve data with data mining. 

8. Collect horizontal curve data with aerial imaging. 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Conclusion 

The State has decided to move forward with prioritizing data that can be collected with the 

same method. The State set general data priorities by site type and data collection method, 

leading to a plan for data collection efforts to improve their roadway departure and 

intersection safety analysis capabilities. 

72
 
 

Arizona Strategic Highway Safety Plan. Appendix C: Crash Characteristic Data 

Map Book. https://azdot.gov/docs/default-source/about/az-shsp-appendix-c-crashcharacteristic-data-map-book.pdf?sfvrsn=2 

Arizona DOT developed a crash characteristics data map book to present a high-level 

overview of crash types associated with characteristics. The data map book also presents 

guidance on where efforts can be implemented to reduce crashes. 

Wisconsin Empirical Bayes Analysis. “University Conducting HSIP Project Evaluations 

Using Empirical Bayes” from HSIP Noteworthy Practice Series: HSIP Project Evaluation 

FHWA-SA-11-02. 

https://rspcb.safety.fhwa.dot.gov/noteworthy/pdf/FHWASA1102_hsip_proj_eval.pdf 

Wisconsin DOT developed a project evaluation process incorporating EB analysis into all 

HSIP project evaluations using the network-level SPFs contained in the AASHTOWare 

Safety Analyst software. Results from implementing the EB method for B/C analysis 

showed how a simple before-after evaluation can overestimate the safety benefits of a 

project. 

Ohio Safety AnalystTM: Implementing a New Roadway Safety Management Process with 

Safety AnalystTM – OH 2010. http://safety.fhwa.dot.gov/hsm/casestudies/oh_cstd.pdf 

The Ohio DOT has embraced the opportunity to integrate Safety AnalystTM software into 

its existing safety analysis system to increase the quality of its network-level data analysis. 

In order to take advantage of the benefits of Safety AnalystTM, Ohio DOT improved its data 

collection and management processes. The immediate advantage of Safety AnalystTM is the 

ability to apply the HSM screening methods, analyze specific subtypes of locations, and 

compare multiple screening methods. 

Funding Systemic Improvements – New Mexico: Developing Methodologies for the 

Prioritization of Systemic Safety Improvements – NY 2015. 

https://rspcb.safety.fhwa.dot.gov/noteworthy/html/hsip_in_nm.aspx?id=179 

New Mexico identified proven countermeasures that did not require a crash history for 

justification to streamline the wide implementation of cost-effective safety improvements 

on rural roads. Implementing this approach, the State applied low-cost systematic 

improvements to locations that might not otherwise have received funding due to their 

low crash histories. 

PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

APPENDIX B: CASE STUDIES
 

73
 
Systemic Planning Process – New York: Applying Systemic Planning Process to Lane 

Departure Crashes on State Highway System – NY 2013. 

http://safety.fhwa.dot.gov/systemic/pdf/sfty_ny.pdf 

The New York State DOT used the Systemic Safety Project Selection Tool to identify sites 

where safety could be improved by deploying low-cost countermeasures on roadways with 

a high crash risk rather than just focusing on specific sites with a history of severe crashes. 

The Tool is beneficial because it provides a process to identify locations that would benefit 

from safety-related improvements that would not otherwise be identified through the 

traditional site-specific analysis process. 

Interactive Highway Safety Design Model - Idaho: Highway Safety Manual Case 

Study 1: Using Predictive Methods for a Corridor Study in Idaho – ID 2011. 

https://rspcb.safety.fhwa.dot.gov/noteworthy/html/datacollect_id.aspx?id=82 

Idaho used the IHSDM software to conduct a detailed project-level corridor study of an 

11-mile section. The analysis resulted in a list of geometric deficiencies and specific 

locations needing further improvement. 

Highway Safety Manual Predictive Method – Florida: Florida Uses Predictive 

Methods found in the Highway Safety Manual (HSM) for Alternative Selection in Florida – 

FL 2012. https://rspcb.safety.fhwa.dot.gov/noteworthy/html/datacollect_fl.aspx?id=88 

Florida used the HSM predictive method to analyze alternatives for a corridor widening 

project on an urban arterial in the Tampa area. They were able to estimate the safety and 

economic benefits of each alternative. 

Safety Analysis in Project Development – Louisiana: 2014 Safety Analysis in Project 

Development – LA 2014. 

https://rspcb.safety.fhwa.dot.gov/noteworthy/html/datacollect_la2.aspx?id=136 

The Louisiana Department of Transportation and Development has implemented safety 

practices into the project development process, resulting in a safety focus from the 

beginning of a project’s life. Practices include incorporating low-cost countermeasures in 

the design stage and analyzing the Transportation Management Plan in the construction 

phase for potential countermeasures to increase work zone safety. 

Maintenance Crews Support HSIP – Arkansas and Arizona: Maintenance Crews 

Step in to Support the HSIP. Arkansas and Arizona – AR and AZ 2015. 

https://rspcb.safety.fhwa.dot.gov/noteworthy/html/safetyculture_ar_az.aspx?id=177 

PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

Arkansas DOT recognized the extensive knowledge maintenance staff have of the 

roadways and now uses these crews to analyze crash sites and recommend next steps for 

addressing related safety issues. Similarly, Arizona DOT Engineering districts use 

maintenance records to identify potential safety concerns if specific sites are repeatedly 

being reported for maintenance. 

Horizontal Curve Database – Tennessee: Tennessee’s Horizontal Curve Database – 

TN. http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16048.pdf 

As part of an extensive effort to complete its roadway network inventory, the Tennessee 

DOT recorded each horizontal curve on the system. Tennessee DOT compiled and stored 

the data in a horizontal curve database. This case study describes the challenges and 

successes in developing and using the horizontal curve database in Tennessee. 

Comprehensive Roadway Data – Utah: Collection and Use of Roadway Asset Data in 

Utah – UT 2014. http://safety.fhwa.dot.gov/rsdp/downloads/utahiidarcasestudy.pdf. 

This case study describes the successes and ongoing challenges related to statewide data 

collection to support asset management, safety, and planning activities in Utah. This report 

has relevance for State DOTs as an example using new technologies and enhanced data 

management practices to create a multipurpose resource. The example may be extended 

to local agency participation as well. The project proved effective for managing roadway 

assets for Utah DOT, which has improved data storage to allow all Utah DOT divisions to 

access the data and improved pavement and sign management. 

Systemic Safety Improvement Based on Risk Assessments at a County Level – 

Minnesota. http://safety.fhwa.dot.gov/systemic/pdf/SystemicinPractice_Minnesota.pdf 

Minnesota has taken a different approach through the development of safety plans for each 

of the State’s 87 counties. The safety plans disaggregate the severe crash types by each of 

the AASHTO SHSP emphasis areas to identify emphasis and target crash types at the 

regional level. Minnesota DOT has selected crash surrogates for various facility types to 

use in the risk assessment. Locations identified and included in the plans can be submitted 

for project funding through the HSIP. 

Safety Data Processes and Governance Practices – Utah. 

http://safety.fhwa.dot.gov/rsdp/downloads/utah_case_studyFinal.pdf 

This case study describes the Utah DOT’s experience with incorporating data governance 

into the development of its data systems. Utah DOT shares and manages data using its 

centralized data portal, UGate. As Utah DOT works to develop UGate 2.0, data 

governance has become a high priority for the agency. The UGate 2.0 structure resembles 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

a data governance framework, and Utah DOT is in the process of organizing a data 

governance board to review expenditures and data stewardship. 

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Agency Data Collection Report. Federal Highway Administration, Office of Safety, 

Washington, D.C., 2013. 

14. Altobello, M., M. Thurber, N. Lefler, H. McGee, and B. Hilger Delucia. Development of a 

Structure for a MIRE Management Information System. Federal Highway Administration 

Office of Safety, Washington, D.C., 2013. 

15. Council, F.M., J.P. Soika, N.X. Lefler, R.A. Pollack, and C.H. Tan. Performance Measures 

for Roadway Inventory Data. Federal Highway Administration Office of Safety, 

Washington, D.C., 2013. 

16. Federal Highway Administration. “Strategic Highway Safety Plan (SHSP).” Federal 

Highway Administration Office of Safety. http://safety.fhwa.dot.gov/hsip/shsp/ 

17. Wenmink, A., S. Herbel, H. Louch, and E. McKenzie. FHWA Safety Target Setting Final 

Report. FHWA-SA-14-009. Federal Highway Administration, Washington, D.C., 

December 2013. http://safety.fhwa.dot.gov/hsip/spm/docs/safetyfinalrpt.pdf 

18. Federal Highway Administration. “Strategic Highway Safety Plan Community of 

Practice.” Federal Highway Administration Office of Safety: Roadway Safety Professional 

Capacity Building. http://rspcb.safety.fhwa.dot.gov/shsp_cop.aspx 

19. Neuman, T.R., B. Hilger Delucia, J.L. Graham, R.C. Peck, I.B. Potts, D.W. Harwood, J.M. 

Hutton, F.M. Council, D.J. Torbic, and Transportation Research Board. NCHRP Report 

500, Vol. 21: Guidance for Implementation of the AASTHO Strategic Highway Safety Plan: 

Safety Data and Analysis in Developing Emphasis Area Plans. Transportation Research 

Board, Washington, D.C., 2008. DOI: 10.17226/14170 

20. Federal Highway Administration. “SHSP Noteworthy Practices.” Federal Highway 

Administration Office of Safety: Roadway Safety Professional Capacity Building. 

https://rspcb.safety.fhwa.dot.gov/shsp_noteworthy.aspx 

21. Federal Highway Administration. “Rulemaking.” Federal Highway Administration Office of 

Safety. 2016. http://safety.fhwa.dot.gov/hsip/rulemaking/ 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

22. Srinivasan, R., F. Gross, B. Lan, and G. Bahar. Reliability of Safety Management Methods: 

Network Screening. FHWA-SA-16-037, Federal Highway Administration Office of Safety, 

Washington, D.C., 2016. http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16037.pdf 

23. Campbell, J.L, M.G. Lichty, J.L. Brown, C.M. Richard, J.S. Graving, J. Graham. M. 

O’Laughlin, D. Torbic, and D. Harwood. NCHRP Report 600: Human Factors Guidelines for 

Road Systems, Second Edition. Transportation Research Board, Washington, D.C., 2012. 

24. Srinivasan, R., G. Bahar, and F. Gross. Reliability of Safety Management Methods: Diagnosis. 

FHWA-SA-16-038, Federal Highway Administration Office of Safety, Washington, D.C., 

2016. http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16038.pdf 

25. Ensuring Consistency in Decision Making: Why and How Your State Should Develop a State 

CMF List. FHWA-SA-17-023, Federal Highway Administration Office of Safety, 

Washington, D.C., 2016. 

http://www.cmfclearinghouse.org/collateral/DevelopingStateCMFList.pdf 

26. Carter, D., R. Srinivasan, B. Persaud, C. Lyon, F. Gross, T. Le, S. Himes, T. Harmon, and 

J. Bonneson. NCHRP Report 17-63: Guidance for the Development and Application of Crash 

Modification Factors. Manuscript in preparation. 

27. Bahar, G., R. Srinivasan, and F. Gross. Reliability of Safety Management Methods: 

Countermeasure Selection. FHWA-SA-16-039, Federal Highway Administration Office of 

Safety, Washington, D.C., 2016. 

http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16039.pdf 

28. VHB. “HSIP Evaluation Guide.” Federal Highway Administration Office of Safety, 

Washington, D.C., 2016. Manuscript in preparation. 

29. Srinivasan, R., F. Gross, and G. Behar. Reliability of Safety Management Methods: Safety 

Effectiveness Evaluation. FHWA-SA-16-040, Federal Highway Administration Office of 

Safety, Washington, D.C., 2016. 

http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16040.pdf 

30. Carter, D., R. Srinivasan, F. Gross, and F. Council. Recommended Protocols for Developing 

Crash Modification Factors. AASHTO Standing Committee on Highway Traffic Safety, 

2012. http://www.cmfclearinghouse.org/collateral/CMF_Protocols.pdf 

31. Gross F., B. Persaud, and C. Lyon. Guide to Developing Quality Crash Modification Factors. 

Federal Highway Administration, Washington, D.C., 2010. 

http://www.cmfclearinghouse.org/collateral/CMF_Guide.pdf 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

32. Preston, H., R. Storm, J. Dowds Bennett, B. Wemple. Systemic Safety Project Selection 

Tool. FHWA-SA-13-019, Federal Highway Administration Office of Safety, Washington, 

D.C., 2013. http://safety.fhwa.dot.gov/systemic/fhwasa13019/ 

33. Gross, F., T. Harmon, G. Behar, and K. Peach. Reliability of Safety Management Methods: 

Systemic Safety Programs. FHWA-SA-16-041, Federal Highway Administration Office of 

Safety, Washington, D.C., 2016. 

http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16041.pdf 

34. Herbel, S., L. Laing, and C. McGovern. Highway Safety Improvement Program (HSIP) 

Manual. FHWA-SA-09-029, Federal Highway Administration Office of Safety, 

Washington, D.C., 2010. 

http://safety.fhwa.dot.gov/hsip/resources/fhwasa09029/fhwasa09029.pdf 

35. Federal Highway Administration. “The Calibrator.” Federal Highway Administration Office 

of Safety: Roadway Safety Data Program Safety Data and Analysis Toolbox. 2016. 

http://safety.fhwa.dot.gov/rsdp/toolbox-content.aspx?toolid=150 

36. Coakley, R. Integration of Safety in the Project Development Process and Beyond: A Context 

Sensitive Approach. Institute of Transportation Engineers, Washington, D.C., 2015. 

http://library.ite.org/pub/e4edb88b-bafd-b6c9-6a19-22e98fedc8a9 

37. Van Schalkwyk, I., E.A. Wemple, and T.R. Neumann. Integrating the HSM into the Highway 

Project Development Process. FHWA-SA-11-50, Federal Highway Administration Office of 

Safety, 2012. http://safety.fhwa.dot.gov/hsm/hsm_integration/ 

38. Atkinson, J., K. Dixon, J. Jones, K. Donoughe-Palframan, M. Colety, and M.l Pratt. Scale 

and Scope of Safety Assessment Methods in the Project Development Process. FHWA-SA-16-

106, Federal Highway Administration Office of Safety, Washington, D.C., 2016. 

http://safety.fhwa.dot.gov/hsm/fhwasa16106/fhwasa16106.pdf 

39. Federal Highway Administration. “Summary of State SPF Calibration and Development 

Efforts.” Crash Modification Factors Clearinghouse. 

http://www.cmfclearinghouse.org/collateral/StateSPFCalibrationandDevelopmentEfforts_ 

07_2016.xlsx 

40. Srinivasan, R., D. Carter, and K. Bauer. Safety Performance Function Decision Guide: SPF 

Calibration vs SPF Development. FHWA-SA-14-004, Federal Highway Administration 

Office of Safety, Washington, D.C., 2013. 

http://safety.fhwa.dot.gov/rsdp/downloads/spf_decision_guide_final.pdf 

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PRIORITIES IN ROADWAY SAFETY DATA GUIDE 

41. Bahar, G., and E. Hauer. User's Guide to Develop Highway Safety Manual Safety Performance 

Function Calibration Factors. American Association of State Highway and Transportation 

Officials (AASHTO) Standing Committee on Highway Traffic Safety, 2014. 

http://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP20-07(332)_FinalGuide.pdf 

42. Srinivasan, R., and K. Bauer. Safety Performance Function Development Guide: Developing 

Jurisdiction-Specific SPFs. FHWA-SA-14-005, Federal Highway Administration Office of 

Safety, Washington, D.C., 2013. 

http://safety.fhwa.dot.gov/rsdp/downloads/spf_development_guide_final.pdf 

43. Sawyer, M., N. Lefler, J. Soika, D. Carter, R. Scopatz, F. Gross, H. Rothenberg, J. Miller, 

G. Bahar, and K. Eccles. United States Roadway Safety Data Capabilities Assessment. 

FHWA-SA-12-028, Federal Highway Administration Office of Safety, Washington, D.C., 

2012. http://safety.fhwa.dot.gov/rsdp/downloads/rsdp_usrsdca_final.pdf 

44. Federal Highway Administration. Traffic Monitoring Guide. Federal Highway 

Administration, Washington, D.C., 2016. 

45. Cambridge Systematics. “Guide for State DOT Safety Data Business Planning.” Federal 

Highway Administration Office of Safety, Washington, D.C., 2016. Manuscript in 

preparation. 

46. Transportation Research Board. NCHRP Report 666: Target-Setting Methods and Data 

Management to Support Performance-Based Resource Allocation by Transportation Agencies -

Volume II: Guide for Target Setting and Data Management. Transportation Research Board, 

Washington, D.C., 2010. http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_666.pdf 

47. Federal Highway Administration. “Roadway Safety Noteworthy Practices.” Federal 

Highway Administration Office of Safety: Roadway Safety Professional Capacity Building. 

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

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For More Information: 

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

FHWA, Office of Safety 

Robert Pollack 

robert.pollack@dot.gov 

202-366-5019 

FHWA-SA-17-032