Heterogeneous traffic flow cellular automata model mixed with intelligent controlled vehicles

Abstract

Autonomous driving technology is advancing rapidly, and heterogeneous traffic flow consisting of intelligent controlled vehicles (ICV) and human driving vehicles (HDV) is expected to become the dominant mode on roads. In this study, three driving styles of HDV were analyzed using actual data sets, and the Gipps rule was introduced to establish safety conditions for following and lane-changing rules. A two-lane heterogeneous traffic flow cellular automata model was proposed and simulated using Matlab. According to the simulation results, the impact of ICV penetration rate on traffic capacity is significant, exhibiting a 23.4% to 62.5% increase compared to homogeneous HDV flow within the range of 0.2 to 0.8 ICV penetration rate. Homogeneous ICV flow is about 1.5 times higher than homogeneous HDV flow. Moreover, as the ICV penetration rate increases, there is a gradual decrease in both the lane-changing frequency and congestion level. These findings provide valuable insights into the influence of ICV mixed on traffic flow performance and offer a theoretical basis for managing and controlling heterogeneous traffic flow.