A demonstration of distribution-based calibration

Calibration plays a fundamental role in successful applications of traffic simulation models and Intelligent Transportation Systems. In this research, the use of distributions in calibration process is motivated. 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. As a proof of concept, a case study is also presented where the proposed framework is implemented for the distribution-based calibration of the car-following model used in the TransModeler microscopic traffic simulation model. The use of parameter distributions is preferred to using point parameter values, as it is more realistic, capturing the heterogeneity of driver behavior, and allows the simultaneous study of various driving behavior patterns. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.