Analysis of roadway capacity for heterogeneous traffic flows considering the degree of trust of drivers of HVs in CAVs

Abstract

Future traffic flows will be driven by a mixture of CAVs and HVs. Under heterogeneous traffic flow conditions, drivers of HVs will change their following distance when driving behind CAVs due to psychological factors, which will affect road capacity. This study builds a heterogeneous traffic flow model based on the Markov chain theory, which can derive the proportions of different car-following types under different vehicle distributions. The roadway capacity model is constructed by comprehensively considering the penetration rate of CAVs, platoon intensity, maximum platoon size, and the driver's trust degree of CAVs, and the effects of the four factors on the roadway capacity are analyzed through numerical experiments. Finally, the theoretical model was validated using python-sumo joint simulation experiments. The results show that increasing the degree of trust that HV drivers have in CAVs can further enhance roadway capacity, and the effect of the degree of trust on capacity becomes progressively smaller as the platoon intensity increases. It was also found that platoon intensity can be harmful to the improvement of capacity when the degree of trust is high and the maximum platoon size is small.