Real Time Traffic Congestion Prediction and Mitigation at the City Scale

For the first time in human history we have the necessary tools to pursue ambitious experimental research on human mobility at the global scale. Researchers from fields of computer, information, data, behavior, and social sciences, may finally have their “Large Hadron Collider” to sense, curate, and analyze an incredible amount of real-world human mobility data; this is enabled by the ubiquitous wireless connectivity and over six billion mobile devices and connected vehicles. This project focuses on vehicular traffic in major cities around the world. The research involves: 1) Sensing: Collect and curate global positioning system (GPS) traces from large fleets of vehicles in major cities; 2) Analytics: Leverage data analytic and machine learning techniques to generate accurate traffic flow and congestion models based on extensive historical data; and 3) Services: a. Develop accurate real-time prediction system that utilizes historical models and real-time data; and b. Develop novel ways to introduce real-time intervention to mitigate the potential on set of traffic congestions. The project team plans to partner with the Data Science research group at Uber. Carnegie Mellon University (CMU) PhD students will be able to access real-world data as research interns at Uber. Without such access the proposed research would be impossible. With Uber as the deployment partner, the team has the opportunity to deploy their research ideas in the real world environment and to gather data via such in-situ experiments. Previous studies on vehicular traffic were mostly based on simulation and limited field-collected data from taxi fleets from just few cities. The team has the opportunity to compare traffic patterns from major cities around the world to characterize their similarities and differences. The team can research traffic congestion prediction and mitigation techniques that take into account cultural and driver behavior differences. The team also wants to research the potential of leveraging private enterprises to produce valuable public services for societal good.

Project

  • Status: Active
  • Funding: $100000
  • Contract Numbers:

    69A3551747111

  • Sponsor Organizations:

    Mobility21

    Carnegie Mellon University
    500 Forbes Avenue, Hamburg Hall
    Pittsburgh, PA  United States  15213

    Office of the Assistant Secretary for Research and Technology

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

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Project Managers:

    Ehrlichman, Courtney

  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Principal Investigators:

    Shen, John

  • Start Date: 20170701
  • Expected Completion Date: 20180630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01645910
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Sep 7 2017 5:55PM