Route Choice in Congested Grid Networks

The proposed research is to model the performance of grid street networks in which drivers choose their routes in response to traffic conditions. The research effort includes two main components. First, data from taxi trips in New York City will be used to measure the travel time reliability of certain parts of the network and to quantify the tendency of taxi drivers to avoid unreliable or congested streets. This part of the study will make use of a set of taxi records from New York City that includes pick-up and drop-off times and locations, as well as the distance that the vehicle traveled between the two points. The data will be used to quantify how much additional distance taxis tend to travel to avoid traffic congestion. The second part of the study will be to extend equilibrium models to account for the adaptive behavior of drivers in grid. Unlike existing models that describe the choices that drivers make between parallel routes from an origin to a destination, travelers in an urban grid network have many opportunities to adjust their route in response to observed traffic conditions. The expected result will be a model for traffic in grid networks that is consistent with realistic driver behavior.

  • Record URL:
  • Supplemental Notes:
    • Contract to a Performing Organization has not yet been awarded.


  • English


  • Status: Active
  • Funding: $249688.00
  • Contract Numbers:



  • Sponsor Organizations:

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Start Date: 20131001
  • Expected Completion Date: 0
  • Actual Completion Date: 20180930
  • Source Data: RiP Project 39335

Subject/Index Terms

Filing Info

  • Accession Number: 01557730
  • Record Type: Research project
  • Source Agency: New England University Transportation Center
  • Contract Numbers: DTRT13-G-UTC31, UMAR25-27
  • Files: UTC, RiP
  • Created Date: Mar 25 2015 1:00AM