Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studies explore disruption impacts on individual passenger behavior, and most studies use manually collected survey data. This study examines the potential of using automated collection data to comprehensively analyze unplanned disruption impacts. We propose a systematic approach to evaluate disruption impacts on system performance and individual responses in urban railway systems using automated fare collection (AFC) data. We develop a set of performance metrics to evaluate performance from the perspectives of train operations, information provision (communication), and bridging strategy (shuttle bus services to connect stations impacted by a disruption). We also propose an inference method to quantify the individual response to disruptions (e.g. travel or not, change stations or modes) depending on their trip characteristics with respect to the location and timing of the disruption. The proposed approach is demonstrated using data from a busy metro system. The results highlight the ability of AFC data in providing new insights for the analysis of unplanned disruptions, which are difficult to extract from traditional data collection methods. The case study shows that the disruption impacts are network-wide, and the impacts on passengers continue for a significant amount of time after the incident ended. The behavior highlights the importance of real-time information and the need for timely dissemination.

地铁系统中断是一个大问题，因为它们会影响安全、服务质量和运营效率。更好地了解意外中断下的系统性能和乘客行为对于高效决策、有效客户沟通和识别潜在改进至关重要。然而，很少有研究探讨中断对个人乘客行为的影响，大多数研究使用手动收集的调查数据。本研究考察了使用自动收集数据全面分析计划外中断影响的潜力。我们提出了一种系统方法，可以使用自动收费 (AFC) 数据评估城市铁路系统中中断对系统性能和个体响应的影响。我们制定了一套绩效指标，以从列车运营、信息提供（通信）和桥接策略（连接受中断影响的车站的穿梭巴士服务）的角度评估绩效。我们还提出了一种推理方法来量化个人对中断的反应（例如旅行与否，改变车站或模式），这取决于他们的旅行特征与中断的位置和时间有关。使用来自繁忙地铁系统的数据演示了所提出的方法。结果突出了 AFC 数据为分析计划外中断提供新见解的能力，这些见解难以从传统数据收集方法中提取。案例研究表明，中断影响是全网络的，事件结束后，对乘客的影响会持续很长时间。这种行为突出了实时信息的重要性和及时传播的必要性。