What Is MIRE 2.1? A Complete Guide to FHWA’s Roadway Data Standard

In the era of AI-powered road inventory and automated roadway element detection, having standardized, high-quality roadway data is no longer optional—it’s essential. In the United States, the Federal Highway Administration (FHWA) developed the Model Inventory of Roadway Elements (MIRE) 2.1 as a robust framework to help agencies systematically collect, manage, and analyze roadway and traffic data.

This guide explores how MIRE 2.1 aligns with cutting-edge AI roadway inspection systems, supports predictive analysis, and integrates seamlessly with modern AI road asset management tools like those developed by RoadVision AI.

What Is MIRE 2.1?

MIRE 2.1 is the latest version of a comprehensive data model created by the FHWA to guide state and local agencies in collecting uniform, geospatially accurate roadway and traffic data. It expands on earlier versions (MMIRE, MIRE 1.0, and MIRE 2.0), introducing a refined list of 202 data elements to be collected across all public roads.

Its core mission? To enable data-driven safety analysis, better decision-making, and compliance with federal regulations—especially for the Highway Safety Improvement Program (HSIP).

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Why MIRE 2.1 Matters for the USA’s Road Infrastructure?

Under federal mandates, States must collect 37 Fundamental Data Elements (FDEs) on all public roads by September 30, 2026. These include essential information such as:

• Route number and name
• Surface type
• Number of through lanes
• Annual average daily traffic (AADT)
• Intersection and interchange data

This standardized framework ensures that agencies can efficiently implement predictive safety analysis, fulfill HSIP goals, and integrate with platforms like RoadVision AI’s pavement condition surveys.

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MIRE 2.1 and AI-Powered Road Asset Management

With the growing adoption of AI road inspection technologies, MIRE 2.1 provides the foundational data structure that supports automated roadway element detection. By aligning MIRE data with AI-driven tools, transportation agencies can:

• Reduce manual data collection costs
• Improve data accuracy and consistency
• Integrate insights from AI roadway inspection systems
• Prioritize repairs using predictive analysis

RoadVision AI has pioneered such integrations, using machine vision, deep learning, and geospatial tagging to extract MIRE-compatible data directly from roads during active inspection.

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How MIRE 2.1 Enhances Predictive Analysis?

One of MIRE 2.1’s key advancements is its alignment with the Highway Safety Manual (HSM) and Safe System Approach. This means the data it mandates supports:

• Crash frequency prediction models
• Roadway departure risk evaluation
• Intersection safety assessments
• Speed and volume pattern analysis

When paired with AI road asset management platforms like RoadVision AI, these datasets enable dynamic, proactive infrastructure planning rather than reactive maintenance.

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The Role of AI in Automating MIRE 2.1 Data Collection

Traditional methods of collecting MIRE data (manual surveys, static GPS data) are slow and resource-intensive. Enter AI-powered road inventory solutions.

Using technologies like LiDAR, 360° imaging, and deep learning, tools like those provided by RoadVision AI’s road inventory inspection service automate the extraction of:

• Segment geometry
• Lane markings and signage
• Interchange ramp features
• Pavement condition metrics
• Intersection controls

These insights are generated from video or imagery captured via dashcams or inspection vans, drastically reducing the time and cost of compliance.

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MIRE 2.1 Compliance: A Roadmap for States and Agencies

To meet the federal deadline, agencies must:

1. Update their Linear Referencing Systems (LRS) in line with the ARNOLD requirement.
2. Ensure full coverage of FDEs across all public roads (paved and unpaved).
3. Prioritize geospatial accuracy for better data integration and system-wide analysis.
4. Use tools like RoadVision AI to automate and validate data against the MIRE structure.
5. Integrate MIRE data with crash databases, traffic counts, and GIS systems for a holistic digital roadway ecosystem.

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How RoadVision AI Supports MIRE 2.1 Implementation?

RoadVision AI offers a complete suite of solutions aligned with MIRE 2.1 standards. Through AI-driven road safety audits, automated pavement assessments, and dynamic traffic surveys, the platform helps municipalities and DOTs:

• Automate MIRE data collection
• Maintain data accuracy with real-time cloud syncing
• Integrate compliance checks into daily operations
• Leverage predictive analytics to prioritize investments
• Deliver insights via dashboards aligned with MIRE categories

Explore our services:

• Pavement Condition Survey
• Road Safety Audit
• Road Inventory Inspection
• Traffic Survey

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Real-World Impact: Why It Matters

Accurate MIRE data leads to:

• Better funding decisions
• Safer road design
• Faster response to hazardous conditions
• Equity-focused improvements in underserved areas
• Enhanced transportation planning and smart city development

As AI continues to transform how roads are monitored and maintained, MIRE 2.1 and tools like RoadVision AI provide the structure and scalability to build a future-ready transportation network.

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Conclusion

MIRE 2.1 isn’t just a federal requirement. It’s a blueprint for the future of roadway data management in the USA, enabling AI roadway inspection systems, predictive safety analysis, and automated infrastructure planning.

RoadVision AI is revolutionizing the way we build and maintain infrastructure by leveraging the power of AI in roads to enhance road safety and optimize road management. By utilizing cutting-edge roads AI technology, the platform enables the early detection of potholes, cracks, and other road surface issues, ensuring timely maintenance and improved road conditions.

With a mission to build smarter, safer, and more sustainable roads, RoadVision AI ensures full compliance with IRC Codes as well as MIRE 2.1 standards, empowering engineers and infrastructure stakeholders to make data-driven decisions that cut costs, reduce risks, and enhance the overall transportation experience.

‍Book a demo with RoadVision AI to see how your agency can automate compliance and lead the way in intelligent road infrastructure.

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FAQs

Q1. What is the deadline for MIRE 2.1 compliance?

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All U.S. States must have access to MIRE Fundamental Data Elements on public roads by September 30, 2026, per FHWA regulations.

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Q2. How can AI help with MIRE 2.1 compliance?

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AI enables automated data collection, reduces errors, enhances coverage, and aligns collected data directly with MIRE specifications.

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Q3. What data is included in MIRE FDEs?

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MIRE FDEs include 37 essential elements such as road type, traffic volume, intersections, surface type, and ramp features.

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