A mixed traffic car-following behavior model

Abstract

With the advancement of communication and autonomous driving technologies, a mixed traffic flow comprising human-driven vehicles (HVs), connected human-driven vehicles (CHVs), and connected autonomous vehicles (CAVs) is emerging. In this paper, we propose a generalized car-following model for mixed traffic flow, which considers both the human drivers’ characteristics (i.e., perception ability of distance, acceleration, and speed, trust level in connected vehicle information, and driving style) and information from multiple leading connected vehicles (CVs). Through numerical experiments, we analyze the influences of mixed traffic flow composition schemes, communication distance, and human drivers’ characteristics on mixed traffic flow. The results show that the proposed model can effectively capture the car-following behavior of different types of vehicles in mixed traffic flow. The contribution of CHVs to mixed traffic flow stability is significantly less than that of CAVs due to the involvement of human drivers’ characteristics. Human drivers’ characteristics significantly influence average fuel consumption (FC) within mixed traffic flow. Specifically, inaccurate perception of distance by human drivers can lead to an increase in the average FC, while a higher level of trust in connected information leads to lower average FC. Furthermore, the results reveal that the communication distance between CVs plays a pivotal role in the stability of mixed traffic flow.