A Mixed-traffic Car-Following Model Considering Acceleration and Memory Mechanism

Abstract

This study proposes a new car-following (CF) model for mixed traffic with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Specifically, the model is established by considering the acceleration of the front vehicle and memory mechanism. Compared with the Cooperative Adaptive Cruise Controller (CACC) model, the proposed model has smaller velocity fluctuation. At the same time, the model allows us to explicitly study the performance of mixed traffic under different CAV penetration rates and different CAV spatial distributions. A simulation experiment is performed and results show that CAVs forms a platoon traveling in the front of the fleet has better dynamic performance in smoothness and responsiveness. The impact of CAV penetration rates on traffic stability is further studied, the results prove that the larger CAV penetration rates can improve the stability of mixed traffic.