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.

Language

• English

Project

• 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

• TRT Terms: Data mining; Highway traffic; Highway traffic control; Machine learning; Predictive models; Traffic flow; Traffic forecasting; Visualization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

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