Florida Department of Transportation’s 

Roadmap for Collecting

Model Inventory of 

Roadway Elements

SAFETY DATA CASE STUDY 

FHWA-SA-22-067 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp
ii SAFETY DATA CASE STUDY FHWA-SA-22-067

Notice 

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iii SAFETY DATA CASE STUDY FHWA-SA-22-067

1. Report No.

FHWA-SA-22-067

2. Government Accession

No.

3. Recipient's Catalog No.

4. Title and Subtitle

Florida Department of Transportation’s Roadmap for Collecting

Model Inventory of Roadway Elements

5. Report Date

November 2022

6. Performing Organization Code

7.Author(s)

Ian Hamilton

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Drive

Raleigh, NC 27606

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00052

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

Case Study, August 2019-August 2022

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Sarah Weissman Pascual. Jerry Roche and Joe Hausman provided editorial review

and support for technical content.

16. Abstract

The Federal Highway Administration (FHWA) originally published the Model Inventory of Roadway Elements – MIRE

1.0 guidance on a set of recommended safety data elements for State departments of transportation (DOTs) in 2010. These

elements could support a variety of network and site-specific safety analyses, as well as support the methods introduced in

the First Edition of the American Association of State Highway and Transportation Officials’ Highway Safety Manual. In

2017, FHWA updated and expanded the MIRE guidance and introduced the concept of MIRE Fundamental Data Elements

(FDEs). These MIRE FDEs included data elements for roadway segments, intersections, and interchange/ramps on nonlocal paved roads, as well as smaller subsets for local paved and unpaved roads. This case study presents an effort by the

Florida Department of Transportation (FDOT) as it navigated the process of capturing MIRE FDEs for all public roads.

The purpose is to provide a potential roadmap for other States as they prepare their safety data inventories to meet the 2026

deadline and support safety programs intended to reduce fatalities and serious injuries on all public roads. FDOT’s safety

data program is an example of how multiple data approaches and collaboration across DOT offices can be organized into a

comprehensive program.

17. Key Words:

MIRE, FDE, safety, data

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this

page) Unclassified

21. No. of Pages

16

22. Price

Technical Documentation Page
iv SAFETY DATA CASE STUDY FHWA-SA-22-067

Acronyms 

Acronym Description

AADT annual average daily traffic 

ARBM All Roads Base Map

DOT department of transportation

FDE Fundamental Data Elements

FDOT Florida Department of Transportation 

FHWA Federal Highway Administration 

FLARIS Florida All Roads Intersections and Streets

FSU Florida State University 

GIS geographic information system

HPMS Highway Performance Monitoring System 

HSIP Highway Safety Improvement Program

HSM Highway Safety Manual

IT Information Technology 

LRS linear referencing system 

MIRE Model Inventory of Roadway Elements

OEM original equipment manufacturers 

RCI Roadway Characteristics Inventory

SAFE System Analysis and Forecast Evaluation 

SPF safety performance function

SP&R Safety Planning and Research 

SSO State Safety Office 

STRIDES State Traffic Roadway and Intersection Data Evaluation System 

S2Z STRIDES 2 Zero 

TDA Transportation Data and Analytics 

TEOO Traffic Engineering and Operations Office

TSM&O Transportation Systems Management and Operations

VMT vehicle miles traveled 
v SAFETY DATA CASE STUDY FHWA-SA-22-067

Table of Contents

Introduction........................................................................................................................ 1

Background ........................................................................................................................ 3

Data Collection Methods .................................................................................................. 4

Data Update Cycles........................................................................................................... 5

Data Governance and Future Considerations................................................................ 7

Conclusions......................................................................................................................... 7

References .......................................................................................................................... 9

List of Figures

Figure 1. Chart. Nine-step process for safety data integration. .................................. 3

Figure 2. Graphic. Roadway safety management process. ........................................... 6

Figure 3. Graphic. FDOT’s Reliable, Organized, Accurate Data Sharing. ................. 7

List of Tables

Table 1. MIRE 2.0 FDEs..................................................................................................... 2
vi

SAFETY DATA CASE STUDY FHWA-SA-22-067

Executive Summary

The Federal Highway Administration (FHWA) originally published the Model Inventory of 

Roadway Elements – MIRE 1.0 guidance on a set of recommended safety data elements for State 

departments of transportation (DOTs) in 2010. These elements could support a variety of 

network and site-specific safety analyses, as well as support the methods introduced in the First 

Edition of the American Association of State Highway and Transportation Officials’ Highway 

Safety Manual. In 2017, FHWA updated and expanded the MIRE guidance and introduced the 

concept of MIRE Fundamental Data Elements (FDEs). These MIRE FDEs included data elements 

for roadway segments, intersections, and interchange/ramps on non-local paved roads, as well 

as smaller subsets for local paved and unpaved roads. This case study presents an effort by the 

Florida Department of Transportation (FDOT) as it navigated the process of capturing MIRE 

FDEs for all public roads. The purpose is to provide a potential roadmap for other States as 

they prepare their safety data inventories to meet the 2026 deadline and support safety 

programs intended to reduce fatalities and serious injuries on all public roads. FDOT’s safety 

data program is an example of how multiple data approaches and collaboration across DOT 

offices can be organized into a comprehensive program.
1 SAFETY DATA CASE STUDY FHWA-SA-22-067

Introduction 

The Federal Highway Administration (FHWA) originally published guidance on a set of 

recommended safety data elements for State departments of transportation (DOTs) in 2010. 

The Model Inventory of Roadway Elements (MIRE), Version 1.0 (Lefler et al., 2010) provided a list of 

202 potential data elements representing roadway, intersection, interchange, and traffic 

characteristics. These elements could support a variety of network and site-specific safety 

analyses, as well as support the methods introduced in the First Edition of the American 

Association of State Highway and Transportation Officials’ Highway Safety Manual (HSM; 2010).

In 2017, FHWA updated and expanded the MIRE guidance to include 205 data elements (Lefler 

et al., 2017). The MIRE 2.0 guidance also introduced the concept of MIRE Fundamental Data 

Elements (FDEs). These MIRE FDEs included 37 data elements for roadway segments, 

intersections, and interchange/ramps, primarily for non-local paved roads (table 1).

1 Local paved 

roads and unpaved roads require fewer MIRE FDEs (9 and 5 data elements, respectively), but 

MIRE requires annual average daily traffic (AADT) estimates for all public paved roads. Per 23 

CFR §924.11, “States shall have access to a complete collection of the MIRE FDEs on all public 

roads by September 30, 2026.” FHWA also requests States to report progress (i.e., percent of 

MIRE FDEs collected) annually as part of routine Highway Safety Improvement Program (HSIP) 

reporting. 

This case study presents an effort by the Florida Department of Transportation (FDOT) as it 

navigated the process of capturing MIRE FDEs for all public roads. The purpose is to provide a 

potential roadmap for other States as they prepare their safety data inventories to meet the 

2026 deadline and support safety programs intended to reduce fatalities and serious injuries on 

all public roads.

1 Smaller subsets of MIRE FDEs are required for paved local (based on functional classification) and unpaved roads.
2 SAFETY DATA CASE STUDY FHWA-SA-22-067

Table 1. MIRE 2.0 FDEs.

Data Element

(In MIRE 2.0 Order)

Non-Local 

Paved 

Roadway 

Segment

Non-Local 

Paved 

Intersection

Non-Local 

Paved 

Interchange/ 

Ramp

Local 

Paved 

Roads

Local 

Unpaved 

Roads

Type of Government Ownership X X X X

Route Number X

Route/Street Name X

Begin Point Segment Descriptor X X X

End Point Segment Descriptor X X X

Segment Identifier X X X

Segment Length X

Direction of Inventory X

Functional Class X X X X

Rural/Urban Designation X X

Federal Aid/Route Type X

Access Control X

Surface Type X X

Number of Through Lanes X X

Median Type X

AADT X X X

AADT Year X X

One/Two-Way Operations X

Unique Junction Identifier X

Location Identifier for Road 1 

Crossing Point X 

Location Identifier for Road 2 

Crossing Point X 

Intersection/Junction Geometry X

Intersection/Junction Traffic 

Control X 

Unique Approach Identifier X

Unique Interchange Identifier X

Interchange Type X

Ramp Length X

Ramp AADT X

Year of Ramp AADT X

Roadway Type at Beginning 

Ramp Terminal X 

Location Identifier for Roadway 

at Beginning Ramp Terminal X 

Roadway Type at Ending Ramp 

Terminal X 

Location Identifier for Roadway 

at Ending Ramp Terminal X 
3 SAFETY DATA CASE STUDY FHWA-SA-22-067

Background 

MIRE data collection can fall within the purview of several offices within a State DOT. Safety, 

traffic operations, geographic information systems (GIS), and information technology (IT) all 

have a role in developing processes necessary to collect and maintain MIRE data. Furthermore, 

many offices within the same agency may collect individual MIRE FDEs, but a lack of 

coordination between offices may make these data less accessible for integration or analysis. 

Thoughtfully beginning the process and conducting gap analyses are essential for agencies to 

collect comprehensive MIRE datasets (figure 1).

FDOT, has made significant strides in data integration and conflation in recent years

(Cambridge Systematics, Inc., 2017). This includes data integration among the State’s All Roads 

Base Map (ARBM), Florida All Roads Intersections and Streets (FLARIS), and the State’s linear 

referencing system (LRS). Furthermore, the LRS is the common linkage between the State’s 

Roadway Characteristic Inventory (RCI) and supplementary proprietary roadway data from 

HERETM. These datasets combine to generate the geospatial basemap of MIRE data. The 

Transportation Data and Analytics (TDA) office manages the LRS and, by extension, many of 

the associated MIRE elements on the State’s road network. It helps coordinate analysis of State

data across several FDOT offices along with the Traffic Engineering and Operations Office 

(TEOO), State Safety Office (SSO), Office of Maintenance, and Systems Implementation Office. 

Source: FHWA

Figure 1. Chart. Nine-step process for safety data integration (Scopatz et al., 2016).
4 SAFETY DATA CASE STUDY FHWA-SA-22-067

FDOT began its MIRE data review by mapping existing datasets across different offices within 

the agency and identifying existing data gaps. The gap analysis crosswalk compared the RCI and 

State traffic signal inventory with MIRE requirements (23 CFR § 924.11). FDOT’s MIRE data gap 

analysis crosswalk revealed several existing data elements collected by the agency. These 

include lane counts, lane widths, shoulder widths, auxiliary lanes, and posted speed limits, 

among others. FDOT’s SSO currently integrates these data into the ARBM-FLARIS geospatial 

dataset to produce a basemap of all public roads with MIRE data associated with appropriate 

segments. To help streamline integration, FDOT plans to combine these data into a future 

comprehensive all roads basemap with other (non-safety) data elements in the near future (see 

Data Governance and Future Considerations).

The TEOO, TDA, SSO, and associated offices established an existing baseline of safety data 

elements on Florida’s public roads, and this allowed FDOT to develop a suite of potential 

methods for collecting the remaining MIRE data elements.

Data Collection Methods

FDOT’s approach to collecting MIRE FDEs considers several different potential data sources 

and partners. This includes internal agency collaboration, partnerships with State universities, 

and third-party technology providers. 

Signalized Intersection Data 

FDOT recently began a major initiative to expand its safety data capabilities, particularly with 

respect to implementing Part B of the HSM (Hamilton, 2022). The State Traffic Roadway and 

Intersection Data Evaluation System (STRIDES) 2 Zero (S2Z) program and the System Analysis 

and Forecast Evaluation (SAFE) subprogram focus on data-driven network screening and safety 

decisions. 

Intersections and traffic control devices are foundational components of TEOO’s SAFE S2Z 

program. In order to access data for these facilities, TEOO and TDA coordinate with local 

agencies through FDOT district offices to provide data for these locations on State roads 

(including State-to-local road intersections) as a condition for traffic signal maintenance funding. 

These data include intersection road names, location along the LRS, configurations, and 

associated traffic signal information. This information includes treatments at intersections 

(e.g., flashing yellow arrow and pedestrian signals) and midblock locations (e.g., pedestrian 

hybrid beacons and rectangular rapid flashing beacons). By collaborating with district staff, 

FDOT is able to access new information as projects are completed. This approach has 

produced a measured increase in the rate of updated data becoming available to Highway 

Performance Monitoring System (HPMS) and other programs.
5 SAFETY DATA CASE STUDY FHWA-SA-22-067

Machine Learning and Imagery

FDOT collects high-resolution aerial imagery on a three-year cycle. The latest collection effort 

occurred in 2021. In 2022, FDOT’s TEOO began a collaboration with Florida State University 

(FSU) to develop machine learning algorithms to extract crosswalk and pavement marking 

locations for on-system roads from the imagery. As part of this effort, FSU will match 

potentially extractable data and MIRE data elements. In particular, FDOT and TEOO plan to 

use these data to gather approach-level MIRE data elements at intersections. FSU will conduct a 

2022 pilot effort in Orange County (the Orlando area) before applying the refined methodology 

statewide. 

Local Counts, Probe Data, Connected Vehicles, and Traffic on All Public 

Roads

AADT is a required MIRE FDE for all public roads (23 CFR § 924.11). Obtaining these data we 

is a substantial undertaking for Florida with its roughly 125,000 centerline miles of public roads. 

TDA coordinates with county agencies to obtain local counts, and the office plans to implement 

a local vehicle miles traveled (VMT) development process that resulted from a 2022 State 

Planning and Research (SP&R) project.

FDOT is considering opportunities for partnering with connected vehicle and probe data 

providers to efficiently address this need for all roads, particularly where local roads intersect 

with State roads. FDOT plans to obtain data dating back to the three most recent years 

available (at the time of the future analysis) depending on the individual road. 

Connected vehicle technologies and data provided by original equipment manufacturers (OEMs) 

allow for coverage in areas previously unavailable in probe datasets. Although this only 

represents a percentage of total vehicles on the road, these counts can be extrapolated to 

develop a realistic estimate of total traffic. FDOT plans to calibrate local street traffic volumes 

through sampling and count ground-truthing.

Data Update Cycles

Data maintenance and updates are important components of a safety data program. Although 

certain data elements change slowly and lend themselves to periodic updates (e.g., roadway 

configuration), others may change rapidly and could be updated annually (e.g., traffic volumes). 

FDOT’s approach to data updates mixes more infrequent updates with methods that will 

collect changes more frequently. 

TDA requires districts to update or maintain RCI roadway section data every five years, as well 

as HPMS sample data every three years. The exact year and schedule may vary based on the

date FDOT established the roadway section or sample. An update could be required every year 

for a portion of a district’s network as a part of routine data management. However, new 
6 SAFETY DATA CASE STUDY FHWA-SA-22-067

construction could prompt an expedited update within 90 days. FDOT often prioritizes the 

State-system changes over off-system changes (and FDOT may not receive construction 

notifications for off-system roads), but all sections are updated as part of the routine 5-year 

cycle. The SSO is refining the ARBM-FLARIS development process to achieve an annual update 

cycle with roadway elements. 

The State’s emphasis on HSM Part B methods means that annual snapshots of the physical road 

network are sufficient. Changes (i.e., completed construction) to roadway configuration on 

State roads typically experience a delay of roughly 90 days before being tracked in the State’s 

database, but this delay is not critical for the purposes of network screening and applying safety 

performance functions (SPFs). Furthermore, traffic signal and other intersection data are 

collected on a rolling annual basis as a result of TEOO’s partnership with local agencies through 

district offices. Both approaches 

satisfy FDOT’s needs for conducting 

the roadway safety management 

process (figure 2). 

AADT is reported on an annual 

basis for State roads. However, this 

cycle is not necessarily practical for 

the remaining local public roads in 

the State (over 100,000 centerline 

miles). In addition to the local VMT 

estimation model, FDOT is 

interested in obtaining connected 

vehicle and probe data. These data

have the potential to help FDOT 

manage these large datasets 

efficiently. 

Funding

FDOT uses a mixture of State and Federal sources to collect and maintain the State’s safety

data. This includes the use of HSIP, SP&R, and district funds. FDOT estimates that annual 

maintenance costs for both roadway data on State roads amounted to roughly $600,000-

800,000 per year in 2022.

Figure 2. Graphic. Roadway safety management process 

(FHWA, 2013). 

Source: FHWA
7 SAFETY DATA CASE STUDY FHWA-SA-22-067

Data Governance and Future Considerations

FDOT recently embarked on a 

substantial data governance initiative 

within the agency, and this approach 

influences how the DOT is using safety 

data (figure 3; Christian, 2020). Several 

offices within FDOT (e.g., TDA, TEOO, 

and SSO) plan to substantially integrate 

these datasets into a comprehensive 

basemap over the next decade. This 

includes a conversion of the RCI and 

State LRS to the a single road basemap. 

This long-term project will integrate all 

MIRE data for segment-level analysis. 

Although FDOT has access to most 

segment and intersection MIRE FDEs 

(or will before the 2026 deadline), 

FDOT’s long-range vision includes a 

platform that will support all 205 MIRE 

data elements.

In addition to data integration, a goal 

for FDOT’s expanded data capabilities 

is the development and application of HSM Part C SPFs and analysis methods. FDOT collected 

statewide sample data in 2012 to calibrate HSM models to Florida’s conditions. Since 2020, 

TEOO has conducted annual network screening of signalized intersections on State roads only. 

FDOT developed these SPFs by using three recent years of major and minor road AADT. More 

comprehensive data collection and integration will allow FDOT to expand its analytical capacity 

into project-level analysis.

As FDOT explores new sources for obtaining MIRE data on public roads, several offices and 

teams within the agency are exploring new use cases for the data. For instance, TEOO is using 

enhanced exposure data to explore real-time safety metrics and safety surrogates, and the

office’s Transportation Systems Management and Operations (TSM&O) Program is exploring its 

applicability to work zone safety.

Conclusions

FDOT’s safety data program is an example of how multiple data approaches can be organized 

into a comprehensive program. MIRE data are often associated with safety analysis and data 

programs, but these data support several units and offices within a State DOT. FDOT’s data 

Figure 3. Graphic. FDOT’s Reliable, Organized, 

Accurate Data Sharing (ROADS) initiative component 

model (Christian, 2020).

© FDOT
8 SAFETY DATA CASE STUDY FHWA-SA-22-067

gap analysis demonstrated that several MIRE data elements can be collected and captured as 

part of the routine procedures in a transportation agency. Funding data collection and 

aggregation efforts can occur through a mixture of Federal and State sources, as well as tying it 

to existing funding streams (e.g., maintenance) as part of routine programs.

Partnerships outside of the DOT can also be critical to success. This includes partnerships with 

universities, as well as third-party data providers. These relationships can provide an economy 

of scale that internal data collection processes alone may not be able to match. As emerging 

data sources become more readily available, FDOT is able to explore new use cases and 

expand the DOT’s safety analysis capabilities.

All of FDOT’s partnerships, both internal and external, exist within the overall framework of 

FDOT’s data governance program. Individual data and analytics components can be aggregated 

into a larger program that serves the needs of the entire agency. Integrating MIRE and other 

agency data through a common geospatial basemap provides a roadmap for the agency as it 

makes data more accessible and expands its analytical capabilities. This will allow practitioners 

to better target safety investments and ultimately reduce traffic fatalities and serious injuries.
9 

References

 American Association of State Highway and Transportation Officials. (2010). Highway 

Safety Manual: 1st Edition, Washington, D.C.

http://www.highwaysafetymanual.org/Pages/default.aspx. 

 Cambridge Systematics, Inc. (2017, April 7). Florida Traffic Safety Information System 

Strategic Plan. Florida Department of Transportation. 

http://www.fltrafficrecords.com/pdf/FY21%20Florida%20Traffic%20Safety%20Informatio

n%20System%20TRCC%20Strategic%20Plan%202017-2021.pdf. 

 Christian, C. (2020, June 1). FDOT Data Governance Initiative: Managing Vital Data 

Assets, Report No. Federal Highway Administration, FHWA-HIF-20-064, Washington, 

D.C. https://rosap.ntl.bts.gov/view/dot/50671.

 Federal Highway Administration. (2013). Quantifying Safety in the Roadway Safety 

Management Process, Report No. FHWA-SA-13-010, Washington, D.C. 

https://rosap.ntl.bts.gov/view/dot/49412/dot_49412_DS1.pdf?. 

 Hamilton, I. (2022). Data Governance to Data-Driven Safety Analysis: Florida’s SAFE 

STRIDES 2 Zero, Report No. FHWA-SA-22-004, Washington, D.C.

 Lefler, N., Council F., Harkey, D., Zhou, Y., Carter, D., McGee, H., and Daul M. (2010). 

Model Inventory of Roadway Elements – MIRE 1.0, Report No. FHWA-SA-10-018, 

Washington, D.C. https://rosap.ntl.bts.gov/view/dot/41410.

 Lefler, N., Zhou, Y., Carter, D., McGee, H., Harkey, D., and Council, F. (2017). Model 

Inventory of Roadway Elements – MIRE 2.0, Report No. FHWA-SA-17-048, Washington, 

D.C. https://rosap.ntl.bts.gov/view/dot/49568.

 Phone Interview on June 24, 2022, with Alan El-Urfali and Dibakar Saha. 

 Scopatz, R.A., Goughnour, E., Abbot, D., Tang, E., Carter, D., Smith, S., and Salzer, T. 

(2016). Informational Guide for State, Tribal, and Local Safety Data Integration, Federal 

Highway Administration, Report No. FHWA-SA-16-118, Washington, D.C. Available

online: https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16118.pdf. 

 23 CFR § 924.11 - Implementation. (n.d.). Code of Federal Regulations. 

https://www.ecfr.gov/current/title-23/chapter-I/subchapter-J/part-924/

section-924.11.
10 

Contact

Florida Department of Transportation

Alan El-Urfali, State Traffic Services Program Engineer

Alan.El-Urfali@dot.state.fl.us