The National Cooperative Highway Research Program has released a new roadmap outlining how state and local Departments of Transportation (DOTs) can integrate artificial intelligence into transportation planning, operations, and infrastructure management.
Titled Artificial Intelligence Opportunities for State and Local DOTs: A Research Roadmap, the guide examines how AI can modernize transportation systems while addressing long-standing operational and safety challenges.
Research-Driven Approach to AI in Transportation
Developed through collaborative research led by Virginia Tech and FM Consultants, the roadmap draws on insights from more than 65,000 research articles and DOT case studies. As a result, it provides one of the most comprehensive assessments to date of AI’s role in public transportation systems.
The research highlights AI’s ability to improve traffic management, road safety, infrastructure maintenance, and environmental sustainability. At the same time, it acknowledges that many DOTs remain in the early stages of AI adoption.
Real-World AI Applications Already Emerging
AI is already reshaping parts of the transportation sector. Autonomous vehicles, predictive traffic systems, and AI-driven infrastructure monitoring tools are becoming more common.
For example, Bellevue, Washington, uses AI-powered cameras to analyze traffic flow and improve intersection safety. However, despite these early successes, many state and local DOTs still lack the resources or expertise to scale similar projects.
Key Research Priorities and Workforce Needs
The roadmap identifies 11 priority research areas. These include AI-enabled emergency response, environmental monitoring, and automated maintenance planning.
Importantly, the roadmap also emphasizes workforce development. It stresses the need for training programs that help DOT staff understand, manage, and evaluate AI systems. Ethical considerations also feature prominently, with a focus on fairness, transparency, and accessibility in AI deployment.
Addressing Barriers to AI Adoption
Despite its promise, AI adoption presents clear challenges. Many DOTs face limitations related to technical infrastructure, data quality, and interoperability between systems.
In addition, concerns around data privacy and public trust remain significant. For this reason, the roadmap calls for clear governance structures and consistent policies to guide responsible AI use.
A Phased Path Forward for DOTs
To help agencies move forward, the roadmap proposes a phased approach to AI integration. This approach includes investing in workforce skills, partnering with universities and private-sector experts, and adopting scalable, cost-effective AI tools.
Workshops and interviews with DOT personnel revealed that solutions must remain flexible. Regional needs, funding levels, and operational priorities vary widely across jurisdictions.
Collaboration as a Foundation for Success
Finally, the roadmap underscores the importance of interdisciplinary collaboration. By bringing together engineers, policymakers, and technologists, DOTs can develop AI strategies that deliver public value while managing risk.
Through coordinated planning and targeted research, the roadmap positions AI as a practical tool for tackling congestion, infrastructure degradation, and climate resilience challenges across the transportation sector.
Need Help?
Keeping track of the growing AI regulatory landscape can be difficult. So if you have any questions or concerns, don’t hesitate to reach out to BABL AI. Their Audit Experts can offer valuable insight, and ensure you’re informed and compliant.
