Identification of time-varying parameters in Gipps model for driving behavior analysis

This paper proposes a new method to analyze driver behavior. Analysis of the behavior is done through the observation of the time-evolution of parameters of simple driver models. The behavior analysis is decomposed in two steps. First the driver model have to be selected or designed to represent the average behavior of a large sample of drivers. Then personal driver's behavior evolution can be analyzed over the time. To be able to identify time-varying non-linear hybrid model parameters, an iterative metaheuristic method based on particle optimization and moving average filtering has been created. This method enables to identify parameters of any model type while filtering the parameter time-variation based on the possible parameter dynamics. This methods also enables to interpolate parameters values while model output values are occluded. Demonstration of the identification algorithm efficiency with Gipps car-following driver model is done based on theoretical examples, and time-evolution of parameter are identified from real-world measured data.