Development and Performance of a Connected Car-Following Model

Abstract

Taking advantage of vehicle-to-vehicle (V2V) communications, driving behavior will be impacted by information on preceding vehicles in communication range. In this work, a cooperative adaptive cruise control (CACC) model is proposed based on the traditional car-following model by introducing the speed and acceleration signals received from multiple preceding vehicles with less delay through V2V communication. With two-vehicle and platoon car-following sections from a trajectory data set, the parameters of the car-following model are calibrated, respectively. The stability analysis of the CACC model is conducted to explore the impact of preceding vehicles’ information. Then, the optimal parameter values of the CACC model and the CACC maximum platoon size are explored by the multiobjective influence analysis framework. The experiments and analysis indicate that (1) V2V communication and the increase in the number of connected vehicles help the traffic to be more stable; (2) the framework of the optimal value of model parameters can be obtained by considering the traffic safety, efficiency, and environmental impact; and (3) the optimized maximum platoon size can be determined by the trade-off among multiple objectives.