Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections

Abstract

Existing traffic signal control systems only allocate green time to different phases to avoid conflicting vehicle movements. With advances in connected and automated vehicle (CAV) technologies, CAV trajectories not only provide more information than existing infrastructure-based detection systems, but also can be controlled to further improve mobility and sustainability. This paper presents a mixed integer linear programming (MILP) model to optimize vehicle trajectories and traffic signals in a unified framework at isolated signalized intersections in a CAV environment. A new planning horizon strategy is applied to conduct the optimization. All vehicle movements such as left-turning, right-turning and through are considered. Phase sequences, green start and duration of each phase, and cycle lengths are optimized together with vehicle lane-changing behaviors and vehicle arrival times for delay minimization. Vehicles are split into platoons and are guaranteed to pass through the intersection at desired speeds and avoid stops at stop bars. Exact vehicle trajectories are determined based on optimized vehicle arrival times. For the trajectory planning of platoon leading vehicles, an optimal control model is implemented to minimize fuel consumption/emission. For following vehicles in a platoon, Newell's car-following model is applied. Simulation results validate the advantages of the proposed control method over vehicle-actuated control in terms of intersection capacity, vehicle delays, and CO2 emissions. A sensitivity analysis is conducted to show the potential benefits of a short minimum green duration as well as the impacts of no-changing zones on the optimality of the proposed model.