Unscented particle filter for delayed car-following models estimation

Microscopic simulation models have become widely applied tools in traffic engineering. Nevertheless, parameter identification remains a difficult task. This is for one caused by the fact that parameters are generally not directly observable from common traffic data. The second difficulty stems from the fact that real driving behavior is variable in time and space, etc. This paper puts forward a new approach to identify changing parameters of delayed car-following models, i.e. models that include a reaction time. The approach is based on the unscented particle filter approach, which is generalized to enable estimation of reaction times. The estimation of this true delay is achieved without linearization. Besides the methodological contribution, we show empirical evidence for changing driving behavior by applying the approach to real-life microscopic traffic data