Joint Optimization of CAV Trajectories and Signal Phase Control in Mixed Traffic at Signalized Intersections

Abstract

Connected and automated vehicles (CAVs) have the potential to significantly improve the efficiency and safety of urban intersections, yet their benefits depend strongly on penetration rates and their interaction with signal control strategies. This study proposes a joint optimization model that integrates a multiagent deep Q-network (MADQN)-based strategy for CAV trajectory control and an adaptive strategy for signal phase adjustment in mixed traffic. The proposed model incorporates key states from both vehicles and signals, including speed, acceleration, queue length, and phase information, and employs a global reward that combines average delay and energy consumption. An adaptive signal control driven by queue pressure dynamically adjusts phase durations in response to real-time traffic conditions, with its outcomes feeding back into the environment for CAVs training. Simulation experiments conducted on a real-world multidirectional intersection demonstrate that the proposed model effectively reduces delays across most movements even at low CAV penetration rates. As the CAV penetration rate increases, consistent and more pronounced reductions in both delay and energy consumption are observed for all movements.