Joint Optimization of Intersection Signals and Vehicle Trajectories with Connected and Automated Vehicles

With the development of economy and the acceleration of urbanization, the traffic demand and the pressure of urban traffic management are increasing day by day. The development and maturity of connected and automated vehicle (CA V) technology provides a hardware basis for intelligent intersection control. By integrating vehicle trajectory control and intersection signal control into a single process, delays and emissions can be optimized simultaneously to achieve maximum benefits. This paper proposes a two-stage optimization model with signal optimization and vehicle trajectory control for signalized intersections. The signal optimization problem is modeled as a dynamic programming problem with the goal of minimizing vehicle delay. Optimal control theory is used to solve the vehicle trajectory control problem with the goal of minimizing fuel consumption and emissions. This paper also considers different levels of CA V market penetration in mixed traffic scenarios. Simulation results show that the proposed optimization model, compared to fixed-time and adaptive signal control, can reduce both vehicle delay and emissions by up to 20.9% and 13.4% under a variety of demand levels. In mixed traffic scenarios, system performance improves as market penetration increases. Even if CA V penetration is low, there are significant benefits in decreasing emissions.