Parameter calibration of traffic models in microscopic online simulations

Abstract

This paper discusses the continuous parameter calibration of microscopic traffic models in online simulations. An algorithm is described which uses a recent development in filter theory, the Unscented Kalman Filter. The algorithm is supplied with data of loop detectors and, of course, with the model. It adapts continuously the model parameters to the incoming data in order to keep the online simulation as realistic as possible. The parameters to be filtered have to be chosen carefully, considering their probability density function, which should be roughly Gaussian-distributed.