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

State and local departments of transportation (DOTs) are being asked to solve ever more complex transportation problems and issues. Artificial Intelligence (AI) is being proposed and implemented to help address a number of these issues, such as improving safety, alleviating traffic congestion, assisting in real-time systems management, accommodating connected/automated vehicles, preserving the infrastructure, improving organizational efficiency, and customer service, among others. According to Gartner Information Technology Glossary (2021), AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. At the same time, large amounts of both structured and unstructured data from various sources have become available for transportation applications. A Transport Research International Documentation (TRID) literature search identified almost 100 papers on AI applications in transportation published in the Transportation Research Board’s (TRB) Transportation Research Record (TRR) in the last 5 years alone. However, almost all of these papers deal with very specific applications of AI. With the exception of the Transportation Research Circular E-C113: “Artificial Intelligence in Transportation” (2007) and Transportation Research Circular E-C168: “Artificial Intelligence Applications to Critical Transportation Issues” (2012), there is no strategic guidance that state and local DOTs can use to develop guidance, policies, and standards, and ensure a knowledgeable workforce that will enable them to effectively understand, develop, and apply AI solutions to improve their operations and to solve transportation problems. There is also a need to document and share current information on agency experiences with AI, including promising applications. The final report for the project is available as NCHRP Web-Only Document 403: Artificial Intelligence Opportunities for State and Local DOTs: A Research Roadmap, and details possible steps for state and local DOTs to adopt AI in their pipelines.

• Record URL:
  http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4934

Language

• English

Project

• Funding: $200,000
• 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

• Performing Organizations:

  Virginia Polytechnic Institute and State University, Blacksburg

  208 Patton Hall
  Blacksburg, VA  United States  24061
• Principal Investigators:

  Sarkar, Abhijit

• Start Date: 20211102
• Expected Completion Date: 20231231
• Actual Completion Date: 20231231

Subject/Index Terms

• TRT Terms: Artificial intelligence; Highway departments; Literature reviews; Research; State departments of transportation
• Subject Areas: Data and Information Technology; Highways; Research;

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 20 2020 9:52PM