Signal Control Method of Adaptive Traffic Events in Connected Traffic Environment

The existing signal control methods for traffic incidents often fail to account for the impact of traffic flow redistribution caused by current intersection control scheme adjustments on adjacent intersections. This oversight essentially shifts traffic problems to adjacent intersections rather than resolving congestion effectively. In view of this, this study proposes an adaptive traffic incident signal control method (SCM-ATE) by leveraging the testability of networked traffic, the controllability of connected autonomous vehicles (CAVs), and the inducibility of connected human-driven vehicles. The SCM-ATE method uses the shortest path algorithm to plan the diversion path of obstructed traffic flow based on the determination of lanes and traffic flow caused by events, as well as the sufficient traffic capacity of adjacent intersections. The optimization objective is to minimize the average delay of vehicles at all intersections on the diversion path. A dynamic programming approach is then applied to jointly optimize signal timings and trajectories of connected vehicles along the designated route, thereby mitigating the adverse effects of incidents. The simulation results show that under low, medium, and high traffic loads, compared with traditional signal control methods, the SCM-ATE method reduces the average delay of vehicles by 12.56%, 20.34%, and 5.29%, respectively. Compared with the single-layer method using single intersection traffic signals and collaborative vehicle trajectory joint optimization (JOTS-CVT), the average delay of vehicles is reduced by 13.27%, 10.40%, and 1.25%, respectively. These outcomes confirm the effectiveness of SCM-ATE in enhancing traffic efficiency. Further research shows that the intersection traffic load and the penetration rate of networked autonomous vehicle have a significant impact on the optimization effect of SCM-ATE and the SCM-ATE method is more suitable for traffic scenarios where the penetration rate of networked autonomous vehicle is ≥ 0.3 and the lane flow rate ratio at the intersection is ≤ 0.7.