Toward Artificial Intelligence-Enabled Decision Support Systems for TSMO Applications

As contemporary transportation systems get more complex, it becomes more challenging for decision makers to consider the large number of intertwined factors needed to optimize systemwide processes and performance. For example, when an on-roadway vehicle crash occurs, operational systems such as dynamic vehicle routing and variable speed limits may need to be activated and used to provide timely and effective improvement in traffic incident management performance. These systems are examples of transportation systems management and operations (TSMO) strategies. According to the Federal Highway Administration (FHWA), “TSMO is defined as an integrated set of strategies to optimize the performance of existing infrastructure through the implementation of multimodal and intermodal, cross-jurisdictional systems, services, and projects designed to preserve capacity and improve security, safety, and reliability of the transportation system.” Decision support systems (DSSs), which are primarily computer-based information systems used to sort, rank, or choose alternatives, have been developed to improve TSMO. However, conventional DSSs are usually built on a set of expert rules that might not be able to provide customized and optimal solutions. Meanwhile, artificial intelligence (AI) offers potential to revolutionize many facets of our daily lives, including transportation. AI has the capacity to process multiple-sourced, large-scale, real-time data to model system behaviors, predict traffic conditions and evaluate system performance, which aligns with the key functions of DSSs. Research is needed to support state departments of transportation (DOTs) in selecting and deploying the right AI technologies in DSSs for TSMO applications.  The objective of this research is to develop a guide, including implementation roadmaps, to help state DOTs and other transportation agencies in developing and deploying next-generation, data-driven, and AI-enabled DSSs for TSMO applications. A key emphasis should be on identifying areas where AI technologies can improve DSSs for TSMO applications and providing detailed implementation steps, resource needs, and assessing reliability and scalability of AI-based solutions. 

Language

  • English

Project

  • Status: Proposed
  • Funding: $450000
  • Contract Numbers:

    Project 07-34

  • 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:

    Deng, Zuxuan

  • Start Date: 20221108
  • Expected Completion Date: 0
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01847387
  • Record Type: Research project
  • Source Agency: Transportation Research Board
  • Contract Numbers: Project 07-34
  • Files: TRB, RIP
  • Created Date: May 26 2022 5:00PM