Efficient Methodology for Traffic Flow Model Calibration

Proactive traffic management and control relies on sound traffic flow models that is central to traffic prediction and analysis. One of the challenges faced by such models is the calibration of these models to prevailing local conditions. As such, an efficient methodology is called for to fine turn model parameters so that they reflect local traffic characteristics. Many calibration procedures have been developed in the past with varying transferability, complexity, and accuracy. For example, some procedures are customized for certain models and are not easy to be adapted to other models; some procedures may involve optimization of multiple levels, so calibrating a model becomes a time-consuming job; some procedures optimize only one side of the model at the cost of the other inherently related side. The objective of this research is to develop a methodology for calibrating equilibrium traffic flow models that is accurate in nature, independent on models, efficient in computation, robust to calibration data. The result of this research can help traffic analysts and transportation agencies to better understand traffic flow characteristics, predict traffic evolution, mitigate traffic congestion, and deploy resources to anticipate incidents.

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


  • English


  • Status: Completed
  • Funding: $200014.00
  • 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: 20120602
  • Expected Completion Date: 0
  • Actual Completion Date: 20160131
  • Source Data: RiP Project 39221

Subject/Index Terms

Filing Info

  • Accession Number: 01556872
  • Record Type: Research project
  • Source Agency: New England University Transportation Center
  • Contract Numbers: DTRT12-G-UTC01, UMAR24-22A
  • Files: UTC, RiP
  • Created Date: Mar 13 2015 1:00AM