Improving the Efficacy of Car-Following Models With a New Stochastic Parameter Estimation and Calibration Method

Abstract

Microscopic roadway traffic simulators, which attempt to mimic real-world driver behaviors, are based on car-following models and have been widely used as a cost-effective tool for intelligent transportation system (ITS) evaluation. In addition to evaluation, ITSs can benefit from accurate car-following models that can provide current estimations and future predictions of various traffic situations to support real-time traffic management. The accuracy and reliability of these applications are greatly dependent on the appropriate calibration of car-following models. In this paper, 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 based on the Markov chain Monte Carlo simulation that uses the Bayesian estimation theory. An intelligent driver model and Helly's car-following model were utilized to evaluate this method. The Bayesian approach provided better results in terms of the cost function than the deterministic optimization algorithm. With the Bayesian approach, the mean square error per vehicle is decreased with the increased number of vehicles. Analysis also revealed that the Bayesian approach predicted drivers' speed and acceleration/deceleration profiles more closely to the real-world data compared with the deterministic approach evaluated in this paper. Positive validation outcomes suggest potential efficacy of the calibration approach presented in this paper for future applications.