A Combined Model to Jointly Optimize the Left-Turn Trajectory and Approaching Speed for the Connected and Autonomous Vehicles

Currently, with the development of connected and autonomous vehicles (CAVs), rich traffic flow information could be obtained easily to optimize trajectories. Existing studies that generally simplify trajectories as ideal curves or design a passing strategy for all vehicles at a constant speed fail to coordinate global trajectory and speed fluctuation. Therefore, a combined model aiming at optimizing the left-turn trajectory and approaching speed jointly for CAVs is proposed in this study. First, a rule-based classification method is proposed based on relevant indicators and three categories are subdivided. A changeable speed adjustment strategy, considering speed fluctuation is designed to generate the optimal strategy with minimum total time. Finally, the combined model is tested in three scenarios. Results show that delay of left-turn vehicles is optimized by 19.8%, and the proposed model could improve the operation efficiency of intersections.