Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

Signalized intersections play a pivotal role in urban traffic networks and are simultaneously impacted by congestion and pollution. The rapid advancement of Connected Automated Vehicles (CAVs) and associated technologies offers a promising solution to these efficiency and ecological challenges. Within the Cooperative Vehicle Infrastructure System (CVIS), Level 4 CAVs enable real-time information exchange and autonomous driving at intersections. The heterogeneous traffic flow of CAVs and Human-driven Vehicles (HVs) is expected to persist in the foreseeable future, increasing the complexity of traffic flow. This study introduces an adaptive speed optimization control model using the Mixed-Integer Linear Programming (MILP) method, which predicts traffic conditions and offers optimal speed recommendations for CAVs through a multi-objective function. Additionally, this model incorporates local decision and fault tolerance mechanisms to mitigate the impact of potential network delays. Simulation results demonstrate that the optimized CAVs smoothly and swiftly guide the entire heterogeneous traffic flow through intersections, and significantly reducing average delays and carbon emissions. Moreover, upstream intersections consideration has little impact on optimization outcomes. The model shows noticeable optimization effects as CAV penetration rates reach 30%, and becomes even more pronounced at 50% penetration rate, with optimization trends intensifying with increased flow rate. Compared to HVs, CAVs at a flow rate of 1000 veh/h witness a reduction in average delay and waiting time by 56.31 and 15.89 s, respectively, and a total decrease in CO $_{2}$ emissions by 18.5%.