A Parameter Estimation and Calibration Method for Car-Following Models

Microscopic roadway traffic simulators, which attempt to mimic real-world driver behaviors, are based on car-following models. The accuracy and reliability of microscopic traffic simulation models are greatly dependent on the calibration of car-following models, which requires a large amount of real world vehicle trajectory data. In the last few decades, many car-following models have been developed; however, studies are still needed to improve their accuracy and reliability. In this research, the authors developed a process to apply a stochastic calibration method with appropriate regularization to estimate the distribution of parameters for car-following models. The calibration method is founded on the Markov Chain Monte Carlo (MCMC) simulation that uses the Bayesian estimation theory. This research includes a case study, which is based on the Linear (Helly) model with a different number of vehicle trajectories in a highway network. The stochastic approach facilitated the calibration of car-following models more realistically than the deterministic method, as the deterministic algorithm can easily get stuck at a local minimum. The Bayesian approach provided better results in terms of the cost function than the deterministic optimization algorithm. With the Bayesian approach, the average mean square error per vehicle is decreased with increased number of vehicles. Analysis also revealed that the Bayesian approach predicted drivers’ acceleration and deceleration profile more closely compared to the deterministic approach considered in this study. The positive validation outcomes suggest potential efficacy of the calibration approach presented in this paper.

• 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:
  • Rahman, Mizanur
  • Chowdhury, Mashrur
  • Khan, Taufiquar
  • Bhavsar, Parth
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 23p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Bayes' theorem; Calibration; Car following; Microscopic traffic flow; Parametric equations; Traffic flow theory; Traffic simulation
• Subject Areas: Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 01520407
• Record Type: Publication
• Report/Paper Numbers: 14-4634
• Files: TRIS, TRB, ATRI
• Created Date: Mar 28 2014 3:31PM