Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research on the duration of the disruption impacts on passenger trips is limited given that the duration is not observed directly. This paper proposes a probabilistic method to estimate the disruption impact duration using smartcard data, explores statistical and machine learning models to predict the duration of impacts on passengers, and identifies influencing factors including incident characteristics, operating conditions, infrastructure, external factors, and demand. The results highlight that prediction accuracies are acceptable for multiple linear regression, accelerated failure time, and random forest models. Disruptions caused by power failures have longer impact durations than other causes, followed by platform screen doors. The fixed block signaling system leads to a larger disruption duration than the moving block system. The study provides, for the first time, a data-driven approach to understanding the duration of the impact of disruptions on passenger trips using smartcard data which can facilitate timely and informed decision-making under unplanned disruptions.

鉴于乘客需求的快速增长，许多城市轨道交通系统都在接近满负荷运行，意外中断是不可避免的。从旅客的角度来看，行程延误的持续时间是一个主要问题，旅客行程延误的时间可能比火车延误的时间还要长。一些研究的重点是预测火车延误，但由于无法直接观察持续时间，因此对旅客出行中断影响持续时间的研究有限。本文提出了一种利用智能卡数据估计中断影响持续时间的概率方法，探索统计和机器学习模型来预测对乘客的影响持续时间，并确定影响因素，包括事件特征、运营条件、基础设施、外部因素和需求。结果表明，多元线性回归、加速失效时间和随机森林模型的预测精度是可以接受的。停电造成的中断比其他原因造成的影响持续时间更长，其次是站台屏蔽门。固定闭塞信号系统比移动闭塞系统导致更长的中断时间。该研究首次提供了一种数据驱动的方法，使用智能卡数据来了解中断对乘客出行影响的持续时间，从而有助于在意外中断情况下及时做出明智的决策。固定闭塞信号系统比移动闭塞系统导致更长的中断时间。该研究首次提供了一种数据驱动的方法，使用智能卡数据来了解中断对乘客出行影响的持续时间，从而有助于在意外中断情况下及时做出明智的决策。固定闭塞信号系统比移动闭塞系统导致更长的中断时间。该研究首次提供了一种数据驱动的方法，使用智能卡数据来了解中断对乘客出行影响的持续时间，从而有助于在意外中断情况下及时做出明智的决策。