Dynamic Car–Following Model Calibration Using SPSA and ISRES Algorithms

Abstract

Calibration plays a fundamental role in successful applications of traffic simulation and Intelligent Transportation Systems. In this research, the calibration of car–following models is seen as a dynamic problem, which is solved at each individual time–step. The optimization of model parameters is fulfilled using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The output of the optimization is a distribution of parameter values, capturing a wide range of various traffic conditions. The methodology is demonstrated via a case study, where the proposed framework is implemented for the dynamic calibration of the car–following model used in the TransModeler traffic simulation model and Gipps′ model. This method results to model parameter distributions, which are superior to simply using point parameter values, as they are more realistic, capturing the heterogeneity of driver behavior. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.