Trajectory Planning Model for Vehicle Platoons at Off-ramp

Traffic delay and congestion frequently occur in off-ramp areas, but few researches focus on generating the microscopic trajectory plan for individual connected and automated vehicles (CAV) to instruct their real-time acceleration/deceleration rate and lane-changing maneuvers at off-ramps. This paper proposes a trajectory planning model for vehicle platoons at off-ramp based on mixed-integer nonlinear programming (MINLP). The model can generate systematical optimal trajectories for CAVs passing the off-ramp safely and efficiently. Aiming at optimizing the overall traffic efficiency, we developed a series of constraints to model the behavior of vehicles running in the trajectory area. Especially, the impact of the lane-changing of vehicles is carefully designed to further improve the efficiency and safety by coordinating the behavior of different vehicles on the main road into several vehicle platoons and simultaneously generates trajectories for all vehicles in a platoon. The experiments show that the model can improve the traffic efficiency of pure CAV flow on the off-ramp with safety and effectively solve traffic congestion at the off-ramp.