Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways

Abstract

In the connected and autonomous vehicle (CAV) environment, vehicles with different levels of automation are being deployed on public roads. Most research focuses on traffic flow simulation for a single vehicle type, while there are few studies on the interactions of mixed traffic involving CAVs, autonomous vehicles (AVs), and human-driven vehicles (HDVs). To fill this gap, this study investigates the traffic performance of heterogeneous traffic on multi-lane ring roads and highways with on-ramps. Leveraging the Python and SUMO simulation platform, the JAD strategy is introduced to optimize the dynamic interactions within heterogeneous traffic flow. Various scenarios with different proportions of CAVs, AVs, and HDVs were simulated to assess their impact on traffic efficiency, dynamics, safety, and environmental factors. The findings indicate that traffic efficiency, stability, and environmental impact improve as the share of HDVs declines and the proportion of CAVs and AVs rises. In scenarios with more HDVs, the improvements are minimal. Traffic safety gradually improves as the proportion of CAVs and AVs increases, with significant improvements observed when CAVs account for 40% of vehicles on ring roads and 50% on highways. This study advances the understanding of complex interactions in mixed traffic scenarios and their implications for traffic management.