Corridor level cooperative trajectory optimization with connected and automated vehicles

Trajectory planning for connected and automated vehicles (CAVs) has been studied at both isolated intersections and multiple intersections under the fully CAV environment in the literature. However, most of the existing studies only model limited interactions of vehicle trajectories at the microscopic level, without considering the coordination between vehicle trajectories. This study proposes a mixed-integer linear programming (MILP) model to cooperatively optimize the trajectories of CAVs along a corridor for system optimality. The car-following and lane-changing behaviors of each vehicle along the entire path are optimized together. The trajectories of all vehicles along the corridor are coordinated for system optimality in terms of total vehicle delay. All vehicle movements (i.e., left-turning, through, and right-turning) are considered at each intersection. The ingress lanes are not associated with any specific movement and can be used for all vehicle movements, which provides much more flexibility. Vehicles are controlled to pass through intersections without traffic signals. Due to varying traffic conditions, the planning horizon is adaptively adjusted in the implementation procedure of the proposed model to find a balance between solution feasibility and computational burden. Numerical studies validate the advantages of the proposed CAV-based control over the coordinated fixed-time control at different demand levels in terms of vehicle delay and throughput. The analyses of the safety time gaps for collision avoidance within intersection areas show the promising benefits of traffic management under the fully CAV environment.