Joint Optimization of Signal Scheme and Vehicle Trajectories Based on Vehicular Delay Estimation Model

The development of connected and automated vehicles (CAVs), vehicle-to-infrastructure (V2I) communication technologies, and Internet of Things (IoT) provides new opportunities for intelligent intersection management. Existing research primarily focuses on optimizing the signal scheme of the intersection based on CAV trajectory data, while overlooking the influence of human-driven vehicles (HDVs) and the interactive relationship between the signal scheme and vehicle trajectories. This article proposes a joint optimization framework for both signal schemes and CAV trajectories at an isolated intersection. A vehicular delay estimation model is developed to predict the travel delay of each vehicle based on its trajectory under a given signal scheme. The estimation model consists of analytical trajectory generation models for both CAVs and HDVs. A heuristic search algorithm is designed to efficiently solve for the optimal signal scheme and CAV trajectories. Simulations are conducted under varying traffic demands and penetration rates, and the results indicate that the proposed method can significantly improve the traffic efficiency of a typical intersection. A sensitivity analysis is performed to investigate the impact of control zone length on the performance of the method.