Joint Optimization of Intersection Signal Control and Trajectory Control in Novel Heterogenous Traffic Flow Scenarios

In scenarios of mixed traffic flows consisting of human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), existing intersection joint optimization methods place high computational demands on either centralized controllers or on-board computing units due to centralized and individual vehicle controls, respectively. This paper studies a joint optimization method that integrates the cell transmission model (CTM) with a bi-level programming model. This approach utilizes adjustable cell lengths to balance the computational requirements needed for signal control and CAV trajectory optimization, thereby flexibly allocating computational resources based on the capacities of central controllers and on-board computing units. The upper-level model predicts traffic flow states and optimizes signal control parameters by dynamically adjusting cell lengths to reduce the computational load on central controllers. The lower-level model uses these traffic state predictions to globally plan CAV trajectories, thereby enhancing intersection throughput. To improve solution optimality and real-time response, an iterative optimization algorithm that combines stochastic gradient descent with a genetic algorithm is employed to avoid local optima and enhance solution efficiency. Using data from the intersection of Xian-feng Middle Road and Chun-feng South Road in Wuxi City as an example, the optimization effects under different CAV penetration rates were verified. Results show: ① Compared to the baseline scenario, the proposed collaborative optimization scheme can reduce average vehicle travel time at the intersection by up to 8.09%, effectively reducing congestion propagation upstream. ② With CAV penetration rates of 30%, 60% and 90%, the optimization percentages are 2.51%, 5.08% and 7.88% respectively. ③ In scenarios where the inbound flow rate exceeds 3, 000 pcu/h, optimal signal control schemes can still be obtained within 100 seconds, supporting real-time optimization. The method can effectively improve urban traffic congestion and enhance the efficiency of intersections in novel mixed traffic flow scenarios.