District Department of Transportation, 

Washington D.C.

Deriving and 

Visualizing MIRE 

Data for Safety 

Analysis 

D.C.’s Cross-Sectional Data

SAFETY DATA CASE STUDY 

FHWA-SA-22-003 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 
i SAFETY DATA CASE STUDY FHWA-SA-22-003

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 

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ii SAFETY DATA CASE STUDY FHWA-SA-22-003

1. Report No.

FHWA-SA-22-003

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Deriving and Visualizing MIRE Data for Safety Analysis: D.C.’s Cross-Sectional 

Data

5. Report Date

March 2022

6. Performing Organization Code

7.Author(s) 

Ian Hamilton and Em Adler

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, January 2020-June 2022 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

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

technical content.

16. Abstract

The District Department of Transportation (DDOT) is a unique transportation agency. As an equivalent to a State department of

transportation (DOT), it has a regulatory requirement to collect and manage roadway data for all public roads within its 

jurisdiction. The Model Inventory of Roadway Elements (MIRE) Fundamental Data Elements (FDEs) are an example of these 

data needs, and MIRE FDEs are a critical component of data-driven safety analysis (DDSA). DDOT manages transportation for a 

highly urbanized community with an emphasis on providing safe and accessible transportation for users of all modes. This case 

study documents how DDOT takes a unique and innovative approach to roadway data collection and integration to address the 

Agency’s diverse needs. DDOT uses cross-sectional data to derive MIRE FDEs from existing data and reduce data collection 

costs. By collecting data to represent the full cross-section of the road, DDOT is able to conduct robust DDSA in support of the 

District’s multimodal safety initiatives, including Vision Zero and long-range planning. This case study also examines DDOT’s 

pilot initiatives to enhance its existing data with innovative data management and visualization approaches. 

17. Key Words: 

MIRE, FDE, cross-section, data, management, 

integration, visualization

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 15 

22. Price

Technical Documentation Page
iii SAFETY DATA CASE STUDY FHWA-SA-22-003

Acronyms 

Acronym Description

AASHTO American Association of State Highway and Transportation Officials

DDSA data-driven safety analysis 

DDOT District Department of Transportation

DOT department of transportation

ETL extract, transform, and load

FDE Fundamental Data Elements

FHWA Federal Highway Administration 

GIS geographic information system

HPMS Highway Performance Monitoring System

HSM Highway Safety Manual

JSON JavaScript Object Notation

LRS linear referencing system

MIRE Model Inventory of Roadway Elements

MPD Metropolitan Police Department

NACTO National Association of City Transportation Officials

NHTSA National Highway Traffic Safety Administration

NoSQL Not only SQL

PFI Pedestrian Friendliness Index

SQL Structured Query Language
iv SAFETY DATA CASE STUDY FHWA-SA-22-003

Table of Contents 

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

Safety Data in the District ................................................................................................ 2

Safety Data and moveDC ................................................................................................. 4

Pilot Efforts and Future Enhancements .......................................................................... 6

Challenges .......................................................................................................................... 8

Conclusions......................................................................................................................... 8

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

List of Figures

Figure 1. Graphic. Illustration of block and sub-block data formats............................ 3

Figure 2. Graphic. Example of diverse urban road cross-sections. .............................. 4

Figure 3. Graphic. moveDC’s framework for transportation needs. ............................ 5

Figure 4. Graphic. Example of DDOT’s data in a NoSQL configuration. ................... 7
v SAFETY DATA CASE STUDY FHWA-SA-22-003

Executive Summary

The District Department of Transportation (DDOT) is a unique transportation agency. As an 

equivalent to a State department of transportation (DOT), it has a regulatory requirement to 

collect and manage roadway data for all public roads within its jurisdiction. The Model 

Inventory of Roadway Elements (MIRE) Fundamental Data Elements (FDEs) are an example of 

these data needs, and MIRE FDEs are a critical component of data-driven safety analysis

(DDSA). DDOT manages transportation for a highly urbanized community with an emphasis on 

providing safe and accessible transportation for users of all modes. This case study documents 

how DDOT takes a unique and innovative approach to roadway data collection and integration 

to address the Agency’s diverse needs. DDOT uses cross-sectional data to derive MIRE FDEs 

from existing data and reduce data collection costs. By collecting data to represent the full

cross-section of the road, DDOT is able to conduct robust DDSA in support of the District’s 

multimodal safety initiatives, including Vision Zero and long-range planning. This case study also 

examines DDOT’s pilot initiatives to enhance its existing data with innovative data management 

and visualization approaches.
1 SAFETY DATA CASE STUDY FHWA-SA-22-003

Introduction 

In 2019, the Federal Highway Administration (FHWA) completed the second U.S. Roadway 

Safety Data Capabilities Assessment (FHWA, 2019). This nationwide survey documented the 

safety data processes, policies, and procedures of all 50 States plus Washington D.C. and 

Puerto Rico. This survey highlighted the current state of practice with respect to all phases of 

safety data collection, management, integration, and analysis. It also revealed that State 

departments of transportation (DOTs) were eager to improve their capacity for data 

management and integration.

The purpose of this case study is to highlight a noteworthy example of safety data management,

integration, and application that can apply to State, regional, and local transportation agencies. 

The District DOT (DDOT) in Washington D.C. is building its capacity to collect, derive, and 

use roadway data to support the Agency’s unique safety needs. These needs, as well as the

practical application of DDOT’s approach, are exemplified in the District’s long range

multimodal planning effort, moveDC. The 2021 update to the moveDC plan used recent

advancements in the District’s transportation data, particularly using geographic information 

systems (GIS) data, to address key safety needs and plan for a more inclusive transportation 

system. This case study also examines two DDOT pilot efforts to enhance the Agency’s 

innovative data systems. 

Purpose and Need

State, Tribal, regional, and municipal DOTs have an obligation to collect data for the purposes 

of data-driven analysis and performance monitoring. With respect to safety, this means 

collecting data elements prescribed in the Model Inventory of Roadway Elements (MIRE) 

Fundamental Data Elements (FDE) and applying data-driven safety analysis (DDSA) to prioritize

project and programmatic opportunities. However, agencies of differing administrative levels

have diverse and unique priorities based on their context (e.g., urban or rural settings), 

administrative structure (e.g., State or local authority), and resource availability (e.g., budgets 

and technical capacity). DDOT is unique in this respect. The Agency has many of the obligations 

and responsibilities of a State DOT while its jurisdiction is entirely comprised of a highly 

urbanized environment with no rural context.

In 2016, a National Highway Traffic Safety Administration (NHTSA) Traffic Records Assessment 

concluded that DDOT needed to improve integration between various safety datasets. 

Recommendations included improved access to MIRE attributes and linkages between these 

data and reported crashes (NHTSA, 2016). This led DDOT to a unique solution to meet its 

safety data needs. This solution and recent data developments in the District have allowed 

DDOT to explore further, and pilot new, innovative solutions to managing, accessing, and 

visualizing safety data. This case study documents how DDOT applies safety data to meet the 
2 SAFETY DATA CASE STUDY FHWA-SA-22-003

regulatory and programmatic needs that affect all transportation agencies, regardless of size, 

technical capability, and context.

Target Audience

The target audience for this case study is:

• State and Local Transportation Agencies

• Information Technology Staff

• Data Managers, Analysts, and Stewards

Safety Data in the District

DDOT uses Esri’s ArcGIS Roads and HighwaysTM to help manage the District’s linear 

referencing system (LRS). Like other State DOTs, the LRS is the foundation of DDOT’s 

roadway data program. This allows DDOT to track roadway characteristics and asset 

information that are required for Federal programs such as the Highway Performance 

Monitoring System (HPMS), as well as District-level initiatives. These data also link to other 

internal and external datasets to inform policy, planning, and engineering decisions. Per the 

recommendations of the 2016 Traffic Records Assessment, DDOT’s GIS section publishes and 

maintains a data wiki that supports stakeholder access to the District’s GIS documentation and 

data processes. 

DDOT uses Roads and Highways and its LRS to link the Metropolitan Police Department’s 

(MPD’s) crash data to the road network for preliminary safety evaluations. DDOT’s data 

storage and integration practices also allow the Agency to derive several MIRE FDEs from its 

existing roadway inventory (as opposed to conducting costlier collection efforts). This 

investment in the data, rather than solely on data collection, enables the Agency to conduct 

detailed DDSA for its urban, multimodal environment.

Integration with Law Enforcement

To support the District’s Vision Zero initiative, DDOT publishes the “Crashes in D.C.” 

application. This resource provides stakeholders access to locatable crashes provided by MPD. 

DDOT processes new crash report data overnight to provide a continuously updated and nearreal time reflection of safety records. Assuming the MPD crash data have sufficient information 

to be located (i.e., latitude/latitude coordinates or address information), these data can be 

readily incorporated in the DDOT dataset. Furthermore, spatially located crashes allow DDOT 

to link crash data with roadway information via the LRS. This serves two functions, as it allows 

DDOT to analyze crashes with respect to certain facility types and specific city blocks, and it 

reduces the likelihood of data entry issues as law enforcement officers are not required to 

record static roadway characteristics. Accurate data is achieved through linkage rather than 

duplicate data collection.
3 SAFETY DATA CASE STUDY FHWA-SA-22-003

MIRE Data

Federal regulations require State DOTs to collect the MIRE FDEs by 2026 (23 CFR § 924.17).

FHWA published a list of these data elements in the Model Inventory of Roadway Elements – MIRE 

2.0 guide (Lefler et al., 2017). As DDOT noted, MIRE data collection can be expensive and time 

consuming depending on the data element(s) being collected. To help meet this regulatory 

requirement and enhance the District’s safety analysis capabilities, DDOT uses an innovative 

approach to derive MIRE data elements from the Agency’s existing network information. For 

instance, LRS network connections and block-level information can help derive intersection 

location and geometry. As another example, roadway cross-section and traffic operations data 

can provide insights into intersection approaches and support detailed safety data analysis.

DDOT tracks progress toward the MIRE FDEs and the applicable datasets on the Agency’s 

wiki. 

Cross-Sectional Data and Analysis

In addition to supporting safety analysis methods described in the American Association of 

State Highway and Transportation Officials (AASHTO) Highway Safety Manual (HSM), MIRE 

FDEs allow agencies to understand the cross-section of roads within their jurisdiction. DDOT’s 

data philosophy stems from its dual nature as both an equivalent to a State DOT and a 

municipal DOT. As an equivalent agency to a State DOT, DDOT collects MIRE data to support 

DDSA. As a municipal transportation provider, DDOT aggregates these data at the block and 

sub-block levels to record major shifts in traffic and road conditions on the District’s network

and support Vision Zero (figure 1). 

Figure 1. Graphic. Illustration of block and sub-block data formats.

© DDOT
4 SAFETY DATA CASE STUDY FHWA-SA-22-003

Figure 2 illustrates a practical example. The roadway cross-section changes substantially as a 

user bikes, walks, or drives along the street; each block has its own unique safety-related

circumstances. The ability to model the entire roadway, including sidewalks, on-street parking, 

turn lanes, through lanes, and median presence, allows DDOT and the Agency’s stakeholders 

to perform detailed and comprehensive analysis in a highly urbanized context. The tangible 

results of this data intensive effort can be seen in the District’s long-range transportation plan, 

moveDC. 

Safety Data and moveDC

DDOT’s safety data, particularly the Agency’s GIS data, was an integral part of the District’s 

2021 moveDC update. Additionally, the data queries and linkages required to develop the 

moveDC plan provided the motivation for DDOT to develop a series of complex extract, 

transform, and load (ETL) processes that support detailed safety analysis. For instance, DDOT 

now has segment and intersection relational summaries that can be used for approach analysis, 

and the ETL process updates the Agency’s production database each night.

These cross-sectional data summaries formed the cornerstone of two key components of the 

moveDC plan: 1) identifying locations and communities with the greatest need for transportation 

investments (including safety), and 2) developing priority networks for future investments for 

pedestrians, bicyclists, transit users, and freight vehicles.

Figure 2. Graphic. Example of diverse urban road cross-sections. 

© 2021 Google® © 2021 Maxar Technologies. 
5 SAFETY DATA CASE STUDY FHWA-SA-22-003

Transportation Needs

With readily accessible data, DDOT’s planners, engineers, and stakeholders mapped areas with 

the greatest transportation needs based on a suite of criteria. This data-driven approach applied 

traditional safety data and planning-level data to identify communities that met the following 

criteria (figure 3): 

1. Access to transit: Areas within a predefined buffer of rail and bus transit stops.

2. Access to jobs and destinations: Areas within a predefined travel time (depending 

on mode choice) of major employment and community centers (e.g., grocery stores, 

clinics, senior centers, etc.).

3. Presence of safety risks: Presence of high stress cycling routes, sidewalk gaps, and 

presence of priority Vision Zero corridors.

DDOT’s LRS and cross-sectional data helped support all elements of the transportation needs 

analysis. It was especially critical for delineating safety risks, such as safety needs in 

disadvantaged communities. To address the needs of non-motorized users, this approach 

applies to network connectivity and accommodations. Sidewalk data can illustrate gaps, 

especially where these gaps align with Vision Zero needs. Cyclist stress and comfort on 

individual corridors is another metric that requires detailed cross-sectional data for 

comprehensive screening. This metric is a compilation of traffic volume, speed, travel lane 

configuration, and presence of a bicycle facility or roadside shoulder (Schultheiss et al., 2019). 

Figure 3. Graphic. moveDC’s framework for transportation needs. 

© DDOT
6 SAFETY DATA CASE STUDY FHWA-SA-22-003

DDOT’s documentation and access to detailed data enabled the moveDC planners to develop a 

comprehensive Transportation Needs Map that shows neighborhoods of greatest need for 

investments. 

Priority Networks

DDOT’s data supported the development of a series of priority networks for pedestrians, 

cyclists, and transit users. For pedestrians, DDOT published a working map of sidewalk gaps in 

the District and developed a Pedestrian Friendliness Index (PFI) to assess the broader needs of 

pedestrians. The PFI considers several traditional safety datasets (i.e., MIRE for segments and 

intersections) and non-traditional safety indicators (e.g., building setbacks and street 

connectivity) at the block-level to assess where pedestrian safety improvements are needed 

most. These data will support future pedestrian investments, including the installation of proven 

safety countermeasures at intersections and identification of road diet candidates. 

For cyclists, DDOT used the data to develop recommendations for several facility types based 

on FHWA and National Association of City Transportation Officials (NACTO) guidance 

(Schultheiss et al., 2019; NACTO, 2014). These include indices for user comfort, as well as 

facility types mapped by functional classification and traffic operation. Cross-sectional data 

further supports this approach, as planners are able to query for locations that meet suitable 

geometric and traffic criteria for each type of bikeway.

Pilot Efforts and Future Enhancements

DDOT’s data currently supports a wide range of safety planning and analysis needs for the 

District, including Vision Zero and moveDC. Beyond MIRE data derivation and cross-sectional 

analysis, DDOT conducted two initiatives that could enhance the performance and applicability 

of data to the safety planning process:

1. A pilot exploration of a Not only Structured Query Language (NoSQL) platform for

data management and query.

2. A future enhancement to develop cross-section visualization tools.

Pilot Effort: NoSQL 

Most State DOTs store roadway geometry and asset information using an LRS in a relational 

database. This approach has numerous benefits, particularly with respect to linking

transportation data across databases in a spatial format. However, with an increased emphasis 

on cross-sectional data, DDOT explored the applicability of a NoSQL approach to data 

management. Rather than querying on a fixed set of relationships, a NoSQL approach has the 

potential for significantly more flexibility with complex data queries. The number and 

complexity of relationships inherent to safety data make these datasets ideal candidates for

NoSQL exploration.
7 SAFETY DATA CASE STUDY FHWA-SA-22-003

Although NoSQL is widely used in other data analytics applications (e.g., social media network 

connections), it is not commonly used in transportation GIS applications. A NoSQL approach 

uses two elements, nodes and edges (i.e., relationships), to define the connections between 

individual links in a broader dataset. Figure 4 shows how DDOT can apply NoSQL principles to

link intersection data elements (nodes) through their relationships in segment data (edge 

documents). These relationships can be used to describe the full network of District roads. This

platform could potentially form the basis of an advanced MIRE enterprise data model where 

analysis queries can investigate new and heretofore unexplored relationships in the data.

DDOT used the ArrangoDB platform to conduct the pilot effort. ArrangoDB is capable of 

handling a few different types of database models, including JavaScript Object Notation (JSON), 

key value, and graph-based models. DDOT focused on the JSON-object model for its MIRE 

pilot. While the DDOT pilot did not produce a final proof of concept for implementation prior 

to publication of this case study, NoSQL still has potential to relate large amounts of road, 

crash, and traffic data for complex queries and applications. 

Future Enhancement: Cross-Sections and Visualization

In addition to piloting NoSQL models for cross-sectional data, DDOT is exploring crosssectional data viewers and emerging urban data standards (e.g., the Curb Data Specification) to 

make data more practical and accessible. DDOT hopes to create ground-level visualizations of 

segment and intersection approach cross-sections. In tandem with emerging query tools and 

data models, these visualizations will allow District planners, engineers, and stakeholders to 

efficiently access data, support plan development, and enhance project scoping. 

Figure 4. Graphic. Example of DDOT’s data in a NoSQL configuration. 

© DDOT
8 SAFETY DATA CASE STUDY FHWA-SA-22-003

Challenges

DDOT’s approach to data management is a unique way to integrate and visualize data. 

However, the Agency experiences some challenges common to most DOTs. For instance, not 

every crash report collected by MPD is spatially locatable (i.e., location information is missing 

from the original crash report). As a result, these would not be represented in the “Crashes in 

DC” data viewer. Furthermore, the cross-sectional nature of DDOT’s data provides additional 

planning and engineering benefits beyond those outlined in the HSM. Still, these methods, as 

well as the safety management tools that implement these methods, do not consider the 

multidimensional nature of the roadway. HSM methods predict crashes by facility type (e.g., 

multilane arterials), but the detailed configuration of these facilities is not necessary for core 

analysis. 

DDOT’s exploration of a NoSQL data model has the potential to unlock new insights into the 

safety and accessibility of the District’s roads, but as of this case study, more traditional data 

management tools have proven more effective. For instance, DDOT was able to use 

PostGreSQL (a relational database management system) to achieve higher performance and 

faster queries than the ArrangoDB platform. Based on its pilot effort, DDOT believes potential

NoSQL applications are more appropriate for highly detailed, internal, and complex uses, while 

traditional data models are adequate for simpler and public-facing applications. The applicability 

of a NoSQL approach to DDOT’s day to day needs may be limited, and it may be resource 

intensive to pursue. As of this case study’s publication, DDOT does not plan pursue a NoSQL 

approach for its enterprise data systems.

Conclusions

DDOT is a unique agency, but many of its regulatory and technical needs are common for 

agencies of all sizes and administrative levels. Like State DOTs, DDOT ingests crash data from 

external sources (e.g., MPD) and links these data with the Agency’s roadway, traffic, and asset 

inventories. Likewise, DDOT is obliged to collect MIRE FDEs on all public roads. DDOT’s use 

of cross-sectional data has allowed the Agency to meet these regulatory challenges and enhance 

their analytical capabilities. The detailed analyses produced to support the District’s Vision Zero 

program and the moveDC plan demonstrate how these data can be applied to regional or 

municipal needs. The highly urbanized character of the District requires a highly multimodal 

approach to safety, and DDOT uses MIRE data in innovative ways to identify transportation 

needs and prioritize locations for improvement. DDOT’s continued work in the field of data 

management and visualization will continue to improve the District’s technical capacity and data 

accessibility for key stakeholders, as well as support a safer and more equitable transportation 

system.
9 

References

 District Department of Transportation. (2021). “What is GIS?” (wiki). 

 District Department of Transportation. (2021). “moveDC: 2021 Update” (website). 

Available online: https://movedc-dcgis.hub.arcgis.com/. 

 Federal Highway Administration. (2019). Second U.S. Roadway Safety Data Capabilities 

Assessment, Report No. FHWA-SA-19-018, Washington, D.C. Available online: 

https://rosap.ntl.bts.gov/view/dot/55617. 

 James Graham and Osheen Safarian. (2021, April). Using a Graph-Based Database to

Improve Data Modelling and Analysis [Presentation].

 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. Available online: https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa17048.pdf.

 National Association of City Transportation Officials. (2014). Urban bikeway design guide. 

Available online: https://nacto.org/publication/urban-bikeway-design-guide/. 

 National Highway Traffic Safety Administration. (2016). Washington, D.C.: Traffic 

Records Assessment, Washington, D.C.

 Phone Interview on July 8, 2021, with James Graham and Osheen Safarian at DDOT.

 Schultheiss, B., Goodman, D., Blackburn, L., Wood, A., Reed, D., and Elbech, M. 

(2019). Bikeway Selection Guide, Report No. FHWA-SA-18-077, Washington, D.C. 

Available online: https://safety.fhwa.dot.gov/ped_bike/tools_solve/docs/fhwasa18077.pdf. 

Contact

District Department of Transportation

James K. Graham, GIS and Applications Manager 

james.graham2@dc.gov