Calibration of a Macroscopic Traffic Flow Model with Stochastic Saturation Rates

It is well known that freeway capacity (saturation rate) is highly uncertain and that a nominal value alone may not suffice for traffic management purposes. To analyze the impact of capacity dis- ruptions, the authors consider a macroscopic traffic flow model with stochastic saturation rates, called the stochastic switching cell transmission model (SS-CTM). The SS-CTM enables analysis of traffic delay and throughput drop due to uncertainty of capacity. In this article, the authors present a method for calibration of parameters of the SS-CTM using real traffic data from the Caltrans Performance Measurement System (PeMS). Nominal parameters except saturation rates are estimated using a linear regression-based method. The stochastic saturation rate model is developed by clustering of observed saturation rates (mode identification) and constructing a Markov chain governing the switches between the identified modes. The authors also apply the proposed method to a segment of the US Route 101. The results imply that the saturation rate may deviate from the nominal value for a remarkable fraction of time. In addition, correlation between saturation rates at adjacent locations is significant and is thus relevant for delay and throughput analysis

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

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Jin, Li
    • Amin, Saurabh
  • Conference:
  • Date: 2017


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 25p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01628069
  • Record Type: Publication
  • Report/Paper Numbers: 17-02496
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 2 2017 5:04PM