Research on Speed Guidance Strategies for Mixed Traffic Flow Considering Uncertainty of Leading Vehicles at Signalized Intersections

In the context of intelligent connected environments, this study explores methods to guide the speed of mixed traffic flow to improve intersection efficiency. First, the composition of traffic flow is analyzed, and a car-following model for mixed traffic flow is established, considering reaction time and the psychology of human drivers. Secondly, considering the uncertainty factors of the leading vehicle, we establish a speed guidance model for mixed traffic flow platoons. Finally, a simulation environment is built using Python and SUMO, evaluating the speed guidance effect from the perspectives of different traffic volumes and CAV penetration rates based on average stop times and average delays. The research findings indicate that the speed guidance algorithm proposed in this paper can reduce the number of parking times and delays at intersections. When the mixed traffic flow remains constant, the higher the penetration rate of CAV, the more effective the guidance becomes. However, when the traffic flow reaches a certain level, congestion intensifies, and the effectiveness of the guidance gradually diminishes. Therefore, this study is more applicable to long-distance intersections or key intersections on interconnected roads outside urban areas.