Artificial Intelligence Opportunities for State and Local DOTs – A Research Roadmap

State and local DOTs are being asked to solve ever more complex transportation problems and issues. Artificial Intelligence (AI) tools are being proposed and tested to help address a number of these issues, including improving safety, alleviating traffic congestion, assisting in real-time systems management, accommodating connected/automated vehicles, and preserving the infrastructure, among others. However, transportation agencies have been provided with little overall guidance, policies, standards, or a knowledgeable workforce to effectively apply AI solutions. A TRID literature search identified almost 100 papers on artificial intelligence applications in transportation published in TRB’s Transportation Research Record: Journal of the Transportation Research Board in the last 5 years alone. However, almost all of these papers deal with very specific applications of AI. With the exception of the TRB e-Circular, “Artificial Intelligence Applications to Critical Transportation Issues,” published in 2012, there does not appear to be any strategic guidance that state and local DOTs can use to most effectively apply AI to help improve their operations and to solve transportation problems. Nor is there any up-to-date overview on experiences to date or any synopsis of the most promising application for state and local DOTs. The objective of this research is to develop a research roadmap to prioritize and fund research through the NCHRP program that will provide state and local DOTs with a better understanding of AI, potential applications, priorities, and implications for state and local DOT management and operations. The roadmap will also be useful to the U.S. DOT, state DOT research offices, universities, and other research organizations in generating research and in coordinating the research among these organizations. The roadmap should be coordinated with the February 3, 2020, Federal Highway Administration’s Exploratory Advance Research (EAR) Program Broad Agency Announcement seeking demonstration of AI in new areas of importance to highway transportation. The resulting research roadmap will help state and local DOTs answer the following questions: (1) What is artificial intelligence, and how is AI different from what we have been doing for decades, for example, using data to calibrate models? (2) When should AI be considered as a possible tool to leverage information and/or to help implement solutions? (3) How is AI currently being applied in transportation? (4) What are some of the opportunities for AI breakthroughs not previously possible? (5) What are the potential longer term implications and opportunities of AI on transportation in general, and on transportation policies in particular? (6) What are the workforce and diversity implications of AI in transportation, and how do we prepare the transportation workforce for AI? (7) What are the data and privacy challenges of AI in transportation, and how can they be overcome? (8) What are some of the potential human impacts of AI, including fairness and equity implications? (9) What policies and/or standards are needed to assist state and local DOTs in successfully applying AI?


  • English


  • Status: Proposed
  • Funding: $200000
  • Contract Numbers:

    Project 23-12

  • Sponsor Organizations:

    National Cooperative Highway Research Program

    Transportation Research Board
    500 Fifth Street, NW
    Washington, DC  United States  20001

    American Association of State Highway and Transportation Officials (AASHTO)

    444 North Capitol Street, NW
    Washington, DC  United States  20001

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Mohan, Sid

  • Start Date: 20200520
  • Expected Completion Date: 0
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01739653
  • Record Type: Research project
  • Source Agency: Transportation Research Board
  • Contract Numbers: Project 23-12
  • Files: TRB, RiP
  • Created Date: May 18 2020 3:05PM