Statistical Calibration for Data-Driven Microscopic Simulation Model

For many decades efforts have been made to solve transportation problems. A number of research efforts have been geared toward developing accurate traffic simulation models. One of the challenges is that the model does not always adequately reflect field conditions without proper calibrations. This paper aims to highlight the importance of proper calibrations by providing a statistical calibration procedure based on the Next Generation Simulation (NGSIM) dataset. First, a Monte Carlo approach is employed to generate candidate parameter sets for calibration. Simulations with these parameter sets are evaluated against a robust set of calibration criteria including startup and saturation flow characteristics and travel time distributions. The parameter sets that satisfy these criteria are considered as adequately calibrated. The results suggest that parameters determining distance between cars under various conditions are dominant meeting the evaluation criteria. The results suggest that this approach offers a robust and effective method of calibrating simulation models where disaggregate level vehicle data are available.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB45 Traffic Flow Theory and Characteristics.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Henclewood, Dwayne Anthony
    • Suh, Wonho
    • Rodgers, Michael Owen
    • Hunter, Michael P
  • Conference:
  • Date: 2013

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 19p
  • Monograph Title: TRB 92nd Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01477004
  • Record Type: Publication
  • Report/Paper Numbers: 13-2978
  • Files: PRP, TRIS, TRB, ATRI
  • Created Date: Mar 28 2013 9:00AM