In the extensive urban rail transit network, interruptions will lead to service delays on the current line and spread to other lines, forcing many passengers to wait, detour, or even give up their trips. This paper proposes an event-driven simulation method to evaluate the impact of interruptions on passenger flow distribution. With this method, passengers are regarded as individual agents who can obtain complete information about the current traffic situation, and the impact of the occurrence, duration, and recovery of interruption events on passengers’ travel decisions is analyzed in detail. Then, two modes are used to assign passenger paths: experience-based pre-trip mode and response-based entrap mode. In the simulation process, the train is regarded as an individual agent with a fixed capacity. With the advance of the simulation clock, the network loading is completed through the interaction of the three agents of passengers, platforms, and trains. Interruption events are considered triggers, affecting other agents by affecting network topology and train schedules. Finally, taking Chongqing Metro as an example, the accuracy and effectiveness of the model are analyzed and verified. And the impact of interruption on passenger flow distribution indicators such as inbound volume, outbound volume, and transfer volume is studied from both the individual and overall dimensions. The results show that this study provides an effective method for calculating the passenger flow distribution of an extensive urban rail transit network in the case of interruption.

在庞大的城市轨道交通网络中，中断将导致本线服务延误并波及其他线路，迫使许多乘客等待、绕行，甚至放弃出行。本文提出了一种事件驱动的仿真方法来评估中断对客流分布的影响。该方法将乘客视为能够获取当前交通状况完整信息的个体主体，详细分析中断事件的发生、持续时间和恢复对乘客出行决策的影响。然后，两种模式用于分配乘客路径：基于经验的预行程模式和基于响应的诱捕模式。在仿真过程中，列车被视为具有固定容量的个体智能体。随着仿真时钟的进步，通过乘客、站台、列车三个主体的交互完成网络加载。中断事件被视为触发器，通过影响网络拓扑和列车时刻表来影响其他代理。最后以重庆地铁为例，分析验证了模型的准确性和有效性。并从个体和整体两个维度研究中断对进站量、出站量、换乘量等客流分布指标的影响。结果表明，该研究为大范围城市轨道交通网络中断情况下的客流分布计算提供了一种有效的方法。中断事件被视为触发器，通过影响网络拓扑和列车时刻表来影响其他代理。最后以重庆地铁为例，分析验证了模型的准确性和有效性。并从个体和整体两个维度研究中断对进站量、出站量、换乘量等客流分布指标的影响。结果表明，该研究为大范围城市轨道交通网络中断情况下的客流分布计算提供了一种有效的方法。中断事件被视为触发器，通过影响网络拓扑和列车时刻表来影响其他代理。最后以重庆地铁为例，分析验证了模型的准确性和有效性。并从个体和整体两个维度研究中断对进站量、出站量、换乘量等客流分布指标的影响。结果表明，该研究为大范围城市轨道交通网络中断情况下的客流分布计算提供了一种有效的方法。并从个体和整体两个维度研究中断对进站量、出站量、换乘量等客流分布指标的影响。结果表明，该研究为大范围城市轨道交通网络中断情况下的客流分布计算提供了一种有效的方法。并从个体和整体两个维度研究中断对进站量、出站量、换乘量等客流分布指标的影响。结果表明，该研究为大范围城市轨道交通网络中断情况下的客流分布计算提供了一种有效的方法。