Trajectory Optimization for Mixed Traffic Flow of Human-Driven Vehicles and Connected and Autonomous Vehicles

Frequent acceleration and deceleration of vehicles will lead to increased fuel consumption and gas emissions. Trajectory of mixed traffic flow of human-driven vehicles (HVs) and connected and autonomous vehicles (CAVs) can be optimized to reduce the number of acceleration and deceleration. Therefore, this paper, considering the queuing vehicles, proposes the trajectory optimization model under mixed traffic flow in four scenarios: vehicles pass through intersection at uniform speed, speed up, slow down, and stopping at the stop line. The objective is to minimize fuel consumption. Particle swarm optimization (PSO) is used to solve the model. In addition, Simulation of Urban MObility (SUMO) is carried out to verify the accuracy and feasibility of the model based on the penetration rate of CAVs and traffic saturation. The results show that, under higher CAV penetration rates and traffic saturation, the proposed model can significantly reduce fuel consumption, carbon emissions, and mean halting duration.