既有针对交通事件的信号控制方法未考虑当前交叉口控制方案调整所引起的交通流重分布对邻近交叉口的影响，故本质上只是转移了交通问题，并不利于拥堵的疏散。鉴于此，该文基于网联交通的可测性、网联自动驾驶汽车的可控性和网联人工驾驶汽车的可诱导性，致力于解决交通事件引起的拥堵问题，提出一种自适应交通事件的信号控制方法（SCM-ATE）。SCM-ATE方法是在确定因事件受阻的车道及交通流量、邻近交叉口富裕通行能力基础上，采用最短路算法规划受阻交通流的分流路径，并以分流路径上全部交叉口的车均延误最小为优化目标，采取动态规划法联合优化分流路径上交叉口的信号配时与网联汽车行驶轨迹，以弱化交通事件的不利影响。仿真结果表明：在低、中、高3种交通负荷下，相较于传统信号控制方法，采用SCM-ATE方法的车均延误分别下降了12.56%、20.34%和5.29%；相较于采用单交叉口交通信号与协作车辆轨迹联合优化的单层方法（JOTS-CVT），车均延误分别降低了13.27%、10.40%和1.25%，从而证实了SCM-ATE方法的有效性。进一步研究表明，交叉口交通负荷、网联自动驾驶汽车渗透率对SCM-ATE的优化成效影响显著，SCM-ATE方法更适用于网联自动驾驶汽车渗透率≥0.3及交叉口车道流率比≤0.7的交通场景。

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.