Implementing and Leveraging Machine Learning at State Departments of Transportation

With rapid advances in computing, data science, and big data, there has been a corresponding massive expansion of datasets that are acquired, recorded, and stored, and thus available for individuals and organizations to turn into actionable information. Traditional statistical methods, data mining, simulation techniques (system dynamics and agent-based modeling), and machine learning (ML) are all being used to process these datasets and produce information for decision-making. Categories of ML include supervised learning, unsupervised learning, reinforcement learning, natural language processing, computer vision, and deep learning. Transportation agencies that wish to leverage ML tools and techniques to extract information for decision-making and system operation need to understand and implement these tools as part of their business processes. In NCHRP Project 23-16, Implementing and Leveraging Machine Learning at State Departments of Transportation, Old Dominion University was asked to develop research products on how to select and implement appropriate ML techniques, by identifying promising applications, assessing costs, benefits, risks, and limitations, and by developing a roadmap for an agency ML program that includes implementation best practices. The research team (1) performed an in-depth literature review and survey of state DOTs on the state of the art and practice on ML; (2) conducted case studies with five state DOTs to document existing and potential near-term ML applications; (3) compiled illustrative available codes and tools for major use cases of ML; (4) developed a guide on how to select and implement appropriate ML techniques; and (5) developed an executive summary along with a set of presentation slides highlighting the opportunities offered by ML. NCHRP Research Report 1122: Implementing Machine Learning at State Departments of Transportation: A Guide, a pre-publication draft from TRB's National Cooperative Highway Research Program, serves as both an education and a decision-making tool to assist state DOTs and other transportation agencies in identifying promising ML applications; assessing costs, benefits, risks, and limitations of different approaches; and building a data-driven organization conducive to capitalizing on and expanding ML capabilities in a broad spectrum of transportation applications.

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

Language

• English

Project

• Status: Completed
• 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: 20240323
• Actual Completion Date: 20240323

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