Modeling and analysis of mixed traffic flow capacity and stability considering human-driven vehicle drivers' trust attitude towards intelligent connected vehicles

Abstract

During the proliferation process of Intelligent Connected Vehicles (ICVs), mixed traffic consisting of Human-Driven Vehicles (HDVs) and ICVs is inevitable. Notably, varying trust attitudes of HDV drivers towards ICVs lead to diverse driving behaviors when they interact with ICVs. The impact of these differential driving behaviors on the operational characteristics of the mixed traffic flow is currently unclear. This study focuses on examining the capacity and stability of ICV mixed traffic flow in a single-lane, no-overtaking scenario. We investigate the car-following behaviors of HDV drivers with different trust attitudes towards ICVs. From this, we discern the expected distribution probabilities of vehicles exhibiting varying behaviors in the mixed flow. Utilizing these findings, a theoretical model has been developed to analyze the fundamental diagram and stability of mixed traffic flow, considering the trust attitudes of HDV drivers toward ICVs. Through numerical analysis and simulation experiments, the impact of HDV drivers' trust attitudes in ICVs on the capacity and stability of mixed traffic flow was examined. The results show that ICV integration can bolster traffic flow capacity. Further, heightened trust attitude in ICVs among HDV drivers magnifies the positive effect of ICVs on traffic operational efficiency. However, if the ICV penetration rate remains below 70% and the trust level of HDV drivers towards ICVs does not reach a critical threshold, the integration of ICVs could potentially reduce traffic stability. Conversely, once the ICV penetration rate surpasses 70%, a simultaneous increase in ICV penetration rate and the trusting attitude of HDV drivers towards ICVs significantly improves the stability of mixed traffic flow.