What is MIRE?

The Model Inventory of Roadway Elements (MIRE) is a set of guidelines established by the US Department of Transportation Federal Highway Administration in 2007 to promote the collection and maintenance of high-quality data for advancing transportation safety. MIRE defines data elements that every state is recommended to produce, ultimately ensuring all transportation departments have a standardized database of critical information for safety analytics. 

Before this initial MIRE report, American transportation planning organizations largely relied on crash data for safety assessments. However, comparisons with other countries around the world revealed that the US lacked consistent infrastructure data to use in conjunction with crash information to better understand why transportation safety issues exist and how best to fix them. While initial guidelines in 2007 identified 180 data elements, this list was expanded to 202 in 2010 when MIRE 1.0 was created to further enhance data collection for transportation safety. 

MIRE 2.0

MIRE guidelines were once again updated in 2017, increasing the list of recommended data elements to 205 and also modernizing the ideal database structure to facilitate data sharing and collaboration. For example, MIRE 2.0 includes regulations established by the Highway Performance Monitoring System (HPMS) All Road Network of Linear Referenced Data (ARNOLD) requirement, which mandates each state include all public roads within their linear referencing system.

Although each edition of MIRE is essentially a list of guidelines for states to model their transportation data collection on, recent legislation has required that each state collect a subset of MIRE 2.0 fundamental data elements (FDEs) by September 30, 2026. While it is recommended that all states collect and maintain high-precision data for all relevant 205 data elements cited in MIRE 2.0, only 37 are classified as FDEs. 

Required fundamental data elements from MIRE 2.0

MIRE 2.0 FDEs required for each state by September 2026 can be divided into three categories: non-local paved roads, local paved roads, and unpaved roads. The number of FDEs required varies by category. Here is a quick breakdown of which geospatial features and attribution is required for each:

Non-local paved road FDEs

All 37 FDEs are required for non-local paved roads. These FDEs encompass road segments, intersections, interchanges, and ramps. 

Local paved road FDEs

For paved roads with a functional class of ‘local’, only 9 FDEs are required, and only for road segments.

Unpaved road FDEs

There are only five mandated FDEs for unpaved roads, again only for road segments.

More information about what is required for each field can be found in the MIRE 1.0 report.

Leveraging geospatial data from MIRE 2.0 to enhance transportation safety

The main goal of MIRE and the subsequent mandate for states to collect FDEs is to increase transportation safety across the US. With high-precision geospatial data representing transportation infrastructure, state DOTs and regional MPOs can analyze crash, traffic, demographic, and other datasets to better understand the causes of safety incidents and innovate solutions to mitigate them. However, some analyses require more than the 37 required FDEs, prompting many states to create full MIRE 2.0 databases.

Transportation safety analysis using FDEs

FDEs were established by the Federal Highway Administration to ensure each state has a database of transportation features to conduct basic safety analytics. These features are considered the minimum for what each state needs to understand important context for crashes and other traffic safety issues. 

For example, our recent analysis of 2023 Chicago crash data, demographics, and transportation infrastructure features, including FDEs related to through lanes and intersections, revealed that crashes at intersections with no traffic controls were almost 2x as likely to happen in lower income areas than higher income areas. You can view the full analysis here.

Transportation safety analysis using MIRE 2.0 guidelines

While the FDEs that will be in place in each state by September 2026 are critical to foundational transportation safety analysis, many more insights can be uncovered with all 205 data elements outlined in MIRE 2.0. This is particularly true for pedestrian and other active transportation modes of travel, which are not included as FDEs, but still recommended by MIRE 2.0.

Take for instance our analysis of crosswalk accessibility in Baltimore, Maryland. By mapping out width-attributed crosswalk and sidewalk features alongside roadways, we were able to identify areas with a disproportionate amount of vehicular traffic infrastructure compared to pedestrian routes. Learn more about this analysis here.

Automating MIRE 2.0 data collection with AI-based mapping

Although many states recognize the value of going beyond mandated FDEs and creating full MIRE 2.0 databases, data creation challenges can often seem insurmountable. For example, 6 of the 205 MIRE 2.0 recommended data elements are related to driveways, which are critical for public safety operations to ensure emergency response teams can get to people in need as quickly as possible. It took GIS professionals in Collier County, Florida four years to manually digitize all 132,000 driveways across the county’s 2,300 square mile area. Not only was the database stale by the time it was completed, but consider how long scaling this across an entire state would take - in addition to collecting the other 199 MIRE 2.0 data elements.

Fortunately, advancements in artificial intelligence (AI) have made it possible for DOTs and MPOs to automate data creation without sacrificing accuracy. In the case of Collier County, Ecopia’s AI-based mapping systems were able to map all driveways, service roads, and address points in less than a month. During the project, Ecopia found 39,000 features that were new or updated from the County’s previous manual digitization efforts. Now Collier County’s emergency services teams have an up-to-date source of truth for routing, decreasing their response times and saving more lives.

Ecopia’s AI-powered, human-verified vector map data has produced similar results for MIRE-related elements across the US. Before working with Ecopia, it took civil engineers at Fehr and Peers six months to digitize just over 4% of San Bernardino County’s sidewalks; in three months, Ecopia was able to not only digitize all 17,000 linear miles of sidewalk, but also 16 other transportation layers across the largest county in the contiguous US. Check out a sample of this data below:

If your organization is preparing FDEs for the upcoming September 2026 deadline or would like to create a more robust MIRE-inspired transportation database, get in touch with our team today to learn how Ecopia’s AI-based mapping can help you scale data creation without compromising on quality.