The Use of Large Scale Datasets for Understanding Traffic Network State

The goal of this proposal is to develop novel modeling techniques to infer individual activity patterns from the large scale cell phone datasets and taxi data from New York City (NYC). As such this research offers a paradigm shift from traditional transportation modeling by using large scale, disaggregate data and provides an unique perspective to understand the complex interactions among human behavior, urban environments and traffic patterns. The proposed research will develop a model of dynamic activity pattern using the geo-location data to understand where, when and how long people participate in activities in a given day. The research will contribute to the understanding of activity schedule development and traffic prediction on a traffic network. In addition, it has the potential to contribute to traffic forecasting, predictive traveler guidance, urban event planning and management and disaster preparedness.


  • English


  • Status: Active
  • Funding: $143985.00
  • Contract Numbers:


  • Sponsor Organizations:

    Research and Innovative Technology Administration

    Department of Transportation
    1200 New Jersey Avneue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Mooney, Deborah

  • Performing Organizations:

    City College of New York

    Civil Engineering, Steinman T-127
    140th Street and Convent Avenue
    New York, NY  United States  10031
  • Principal Investigators:

    Ukkusuri, Satish

    Kamga, Camille

  • Start Date: 20120301
  • Expected Completion Date: 0
  • Actual Completion Date: 20131031
  • Source Data: RiP Project 29263

Subject/Index Terms

Filing Info

  • Accession Number: 01485878
  • Record Type: Research project
  • Source Agency: University Transportation Research Center
  • Contract Numbers: 49111-21-22
  • Files: UTC, RiP
  • Created Date: Jul 9 2013 1:01AM