Proactive Congestion Management

This project is applicable to corridors, both with and without managed lanes. The research team intends to combine data from conventional sources, such as loop detectors and traditional probe-based data, with newer sources, such as Bluetooth and connected vehicle (CV) data, to identify conditions that signal impending congestion. The objective is to forecast the likely occurrence of both recurrent and non-recurrent congestion. The team will apply machine-learning techniques to produce data-driven models that rely on near- or real-time traffic measurements capable of generating predictions proactively based on complex and often subtle factors that trigger congestion. The team believes that exploiting the advances in big-data science will result in success, where other efforts with similar objectives (but relying on more conventional analysis methods and data sources) have largely failed. The team will design and test traffic control strategies that might mitigate the identified triggers of congestion or delay the onset of congestion, thereby reducing its duration and impact. Strategies considered will range from driver alerts to ramp metering, and CV-based variable speed limit and speed harmonization advisories.

Language

  • English

Project

  • Status: Active
  • Funding: $259764
  • Contract Numbers:

    69A3551947136

    79070-11

    79070-00-B

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    University of South Florida, Tampa

    Center for Urban Transportation Research
    3650 Spectrum Boulevard
    Tampa, FL  United States  33612-9446
  • Project Managers:

    Li, Xiaopeng

  • Performing Organizations:

    University of South Florida, Tampa

    Center for Urban Transportation Research
    3650 Spectrum Boulevard
    Tampa, FL  United States  33612-9446

    Texas A&M Transportation Institute

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

    Concas, Sisinnio

    Perk, Victoria

    Kuhn, Beverly

  • Start Date: 20200601
  • Expected Completion Date: 20210831
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01751473
  • Record Type: Research project
  • Source Agency: National Institute for Congestion Reduction
  • Contract Numbers: 69A3551947136, 79070-11, 79070-00-B
  • Files: UTC, RiP
  • Created Date: Jun 29 2020 2:35PM