An Integrated Optimization Framework for Connected and Automated Vehicles and Traffic Signals in Urban Networks

This paper proposes an integrated sustainable optimization framework consisting of three modules to optimize vehicle routes, traffic signals, and CAV trajectories at the network level. The route guidance module determines optimal routes to maximize network throughput. The signal optimization module dynamically adjusts signal timings to improve network efficiency, and the trajectory planning module optimizes vehicle accelerations to enhance ride comfort and reduce travel time. All modules exchange their outputs immediately at each iteration. The optimization framework is solved using the Dijkstra algorithm, dynamic programming, and linear programming, with linearization and decomposition techniques employed to improve computational efficiency. Specifically, the network-level signal optimization and trajectory planning modules are decomposed into intersection-level and lane-level subproblems, respectively. The performance and scalability of the proposed optimization framework are demonstrated by experimental simulation, considering different traffic densities and road network sizes. The results show that the proposed framework is effective in improving traffic efficiency and sustainability, including ride comfort and travel delay, in urban road networks. A comparative analysis further evaluates the effectiveness of each module and reveals the significant impact of signal optimization on improving traffic efficiency, highlighting its importance in urban road networks.