Leveraging Artificial Intelligence (AI) Techniques to Detect, Forecast, and Manage Freeway Congestion

To improve the quality and effectiveness of the Texas surface transportation system, it is important to be able to predict where and when prolonged congestion will start and how it will spread, as well as to track atypical events and estimate their evolution. Artificial intelligence (AI) approaches provide a unique opportunity to estimate precise congestion measures by utilizing data from agency-owned sensors, third-party providers, and big enterprise data. This project envisions to mitigate the current research gap by conducting two major project phases. The first phase can confirm the validity of commercial data sources for planning and operations, while the second involves understanding which AI models/ algorithm are the most suitable for addressing TxDOT needs based on desirable use cases and data availability. Furthermore, it is important to analyze the required data models and workflows to determine whether it is sustainable to train, test, and validate the proposed AI techniques. The research teams understand that achieving the research goals requires a comprehensive analysis and documentation of commercial big data platforms and datasets, appropriate AI algorithms, and robust prototype tool to foster return on investment (ROI) and reduce freeway congestion.

Language

  • English

Project

  • Status: Active
  • Funding: $297,204
  • Contract Numbers:

    0-7131

  • Sponsor Organizations:

    Texas Department of Transportation

    125 E. 11th Street
    Austin, TX  United States  78701-2483
  • Managing Organizations:

    Texas Department of Transportation

    125 E. 11th Street
    Austin, TX  United States  78701-2483
  • Project Managers:

    Steele, Joanne

  • Performing Organizations:

    Texas A&M Transportation Institute

    Texas A&M University System
    3135 TAMU
    College Station, TX  United States  77843-3135

    Texas Southern University, Houston

    Center for Transportation Training and Research
    3100 Cleburne
    Houston, TX  United States  77004
  • Principal Investigators:

    Das, Subasish

  • Start Date: 20210914
  • Expected Completion Date: 20230831
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01782705
  • Record Type: Research project
  • Source Agency: Texas Department of Transportation
  • Contract Numbers: 0-7131
  • Files: RIP, STATEDOT
  • Created Date: Sep 23 2021 11:07AM