Implementing and Leveraging Machine Learning at State Departments of Transportation

The objective of this research is to advance the understanding and use of machine learning (ML) tools and techniques at state DOTs and other transportation agencies. The proposed research will aid state DOTs in transitioning to a more advanced state of practice by:  (1) Demonstrating the feasibility and practical value of ML in the context of transportation systems, to better understand its application opportunities, implementation processes, and data requirements. (2) Identifying skills, capabilities, resource, and organizational capacities necessary to leverage ML. (3) Identifying and learning from existing applications at transportation agencies. (4) Providing insight into costs, benefits, and performance and limitations considerations. (5) Identifying and sharing ML frameworks, tools, guidance, and ML code for common use cases.

• Record URL:
  http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=5001

Language

• English

Project

• Status: Active
• Funding: $350000
• Contract Numbers:

  Project 23-16

• 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:

  Old Dominion University

  Norfolk, VA  United States  23529
• Principal Investigators:

  Cetin, Mecit

• Start Date: 20220324
• Expected Completion Date: 20230323
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data collection; Feasibility analysis; Machine learning; Plan implementation; State departments of transportation; State of the art; State of the practice
• Subject Areas: Administration and Management; Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01739666
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: Project 23-16
• Files: TRB, RIP
• Created Date: May 21 2020 10:18AM