Toward Artificial Intelligence-Enabled Decision Support Systems for TSMO Applications

As contemporary transportation systems get more complicated, it becomes much more challenging for decision makers to consider a large number of intertwined factors to optimize the systemwide processes, including planning, operation, asset management, and maintenance. For example, when an accident occurs, operational strategies need to be put forward to provide timely and effective multimodal services, such as vehicle routing, ramp metering, variable speed limits, emergency vehicle preemption, hard shoulder running, and adaptive traffic signal control to improve traffic incident management performance. Over the years, decision support systems (DSSs), which are primarily computer-based information systems used to sort, rank, or choose alternatives, have been developed to help infrastructure owners and operators (IOOs) and policy makers to gear transportation systems towards favorable directions. However, conventional DSSs are usually built on a set of expert rules that might not be able to provide customized and optimal solutions. On the other hand, artificial intelligence (AI), especially advanced machine learning (ML) technique, has been revolutionizing every facet of daily life, including transportation. It takes advantage of the availability of a massive amount of real-time data to model system behaviors, predict traffic states, and evaluate overall performance, which is well aligned with the key functions of DSSs. Therefore, there is a need to explore AI potential for transportation DSSs. As an effective tool to support offline planning and online operation, transportation DSSs have received much attention from practitioners, researchers, and policy makers in the past few decades. They are widely used for land use planning, networked traffic assignment, logistics and supply chains, congestion or bottleneck mitigation, traffic incident management, and fleet/asset repair and maintenance. However, most of these DSS tools are rule-based or model-based without taking full advantage of available data from various sources. Recent advances in artificial intelligence, such as deep neural networks, have unlocked a myriad of opportunities to improve transportation systems, such as the development of connected and automated vehicles. Relatively few studies and deployments have been focused on the exploration of AI or machine learning to the development of data-driven DSSs for traffic system management and operations. The objective of this research is to leverage the state-of-the-art development in artificial intelligence and machine learning, and explore their potential to improve DSSs mainly for transportation system management and operations (TSMO). The research should address (at a minimum) the following questions: (1) General: (a) What is the state of the art about decision support systems for TSMO? (b) What are the gaps of existing DSSs for TSMO? (c) What is the state of the practice for the application of artificial intelligence and machine learning in DSSs for TSMO? (2) Data: (a) What would be the minimum requirement about data (such as data sources, contents, spatial/temporal resolutions, and data quality) to enable AI application for TSMO? (b) Are there any innovative ways to collect data (while keeping privacy) necessary to support or improve AI-enabled DSSs for TSMO applications? (3) Methodology: (a) What are the suitable machine learning-based methodologies and tools for data pre-processing (e.g., cleaning, fusing) in DSSs for TSMO applications? (b) What are the appropriate AI tools that TSMO should use to solve specific functionalities? (4) Digital infrastructure: (a) What kind of sensors are suitable for different functionalities to collect data for AI-enabled TSMO applications? (b) What kind of communication means are required to support reliable information transmission for AI-enabled DSSs for TSMO applications? (c) What kind of computational power is needed to support timely decision-making in AI-enabled DSSs for TSMO applications?  


  • English


  • 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: 20220607
  • 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 23 2022 3:03PM