A Deep Learning-based Network-wide Traffic Prediction Model for Integrated Corridor Management Systems

The objectives for this study are as follows: (1) Develop a deep learning-based modeling framework for high-fidelity traffic prediction utilizing traffic sensors, link capacity, socio-economic, and land use data; (2) Develop a predictive strategy evaluator to assess the impact of potential traffic management strategy given an incident and predicted traffic; (3) Develop a data pipeline that can feed a range of datasets and deliver prediction outputs to a visualization application; and (4) Create a data visualization dashboard providing traffic flow information (such as volume and travel time by link) to show future traffic forecast.


    • English


    • Status: Active
    • Funding: $401058
    • Contract Numbers:

      BED26 977-09

    • Sponsor Organizations:

      Florida Department of Transportation

      Research Center
      605 Suwannee Street MS-30
      Tallahassee, FL  United States  32399-0450
    • Project Managers:

      Dilmore, Jeremy

    • Performing Organizations:

      University of Central Florida, Orlando

      12443 Research Parkway, Suite 207
      Orlando, FL  United States  32826-
    • Principal Investigators:

      Eluru, Naveen

    • Start Date: 20230606
    • Expected Completion Date: 20250930
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01884675
    • Record Type: Research project
    • Source Agency: Florida Department of Transportation
    • Contract Numbers: BED26 977-09
    • Files: RIP, STATEDOT
    • Created Date: Jun 7 2023 7:33AM