Real‐time network‐level traffic signal and trajectory optimization with connected automated and human‐driven vehicles

This paper introduces a real-time framework designed to optimize intersection signal timing and vehicles’ trajectories across a network of intersections in a mixed environment of human-driven and automated fleets. The network-level optimization model is decomposed into intersection-level sub-models, whose decisions are coordinated through information exchange, aiming to push them toward the network model's optimal solutions. At each intersection, a bi-level framework addresses both the signal timing and trajectory optimization models. A specialized greedy heuristic algorithm is developed for the lower-level problem where optimal connected and automated vehicles (CAVs) trajectories are constructed for a given signal timing plan. At the upper level, all the feasible signal timing plans are created, and the system selects the most effective one to implement. The study integrates the entire solution process into a receding horizon framework to ensure efficient handling throughout the study period. A case study demonstrated the system's capability to adjust signals and trajectories effectively under various traffic demands and CAV market shares. Results showed a reduction in overall arterial delay correlating with higher proportions of CAVs. The proposed system delivered solutions in less than 70 ms, which is significantly faster than the half-second solving time steps, ensuring decisions were made quicker than in real-time.