Traffic Impact Analysis of Expressway Accident in Mixed Traffic Environment of Connected and Autonomous Considering Multi-Factor Coupling

Connected automated vehicle (CAVs) and human-driven Vehicles (HVs) are expected to coexist for an extended period, potentially introducing new safety challenges and altering traffic flow characteristics. This study examines the traffic impacts of accidents in mixed-traffic environments under various conditions, employing the Simulation of Urban Mobility (SUMO) platform. The Intelligent Driver Model (IDM) and Cooperative Adaptive Cruise Control (CACC) are utilized to model car-following behavior for HVs and CAVs, respectively. The LC2013 lane-changing model in SUMO is employed to describe lane-changing behavior for HVs and CAVs, with distinct parameter values. A sensitivity analysis of key factors, such as the penetration rate of autonomous vehicles, traffic flow, accident duration and occupancy rate of accident-affected roads, is conducted, focusing safety and efficiency aspects. The results show that higher CAV penetration rates significantly reduce delays and collision risks when a single lane is occupied. However, when two lanes are occupied, delays and collision risks initially increase before decreasing as the CAV penetration rate rises. Additionally, high traffic flow rate and long durations result in more delays, and collision risks increase significantly at high flow rates. This study provides valuable insights for optimizing mixed-traffic operations in the event of accidents and offers practical guidance for the effective deployment of CAVs.