"Real Time Traffic Congestion Prediction and Mitigation at the City Scale" (year 2, continuation from year 1)

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 research team plans to partner with the Data Science research group at Uber. 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.

Language

  • English

Project

  • Status: Active
  • Funding: $90000
  • Contract Numbers:

    69A3551747111

  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    

    Office of the Assistant Secretary for Research and Technology

    University Transportation Program
    ,    
  • Managing Organizations:

    Carnegie Mellon Univeristy

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    
  • Project Managers:

    Schweyer, Lisa Kay

  • Performing Organizations:

    Carnegie Mellon University

    ,    
  • Principal Investigators:

    Shen, John

  • Start Date: 20180701
  • Expected Completion Date: 20190630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01677516
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Aug 7 2018 12:05PM