Disruptions are an inherent feature of transportation systems, occurring unpredictably and with varying durations. Even after an incident is reported as resolved, disruptions can induce irregular train operations that generate substantial uncertainty in passenger waiting and travel times. Accurately forecasting post-disruption travel times therefore remains a critical challenge for transit operators and passenger information systems. This paper develops a Bayesian spatiotemporal modeling framework for post-disruption train travel times that explicitly captures train interactions, headway imbalance, and non-Gaussian distributional characteristics observed during recovery periods. The proposed model decomposes travel times into delay and journey components and incorporates a moving-average error structure to represent dependence between consecutive trains. Skew-normal and skew-$t$ distributions are employed to flexibly accommodate heteroskedasticity, skewness, and heavy-tailed behavior in post-disruption travel times. The framework is evaluated using high-resolution track-occupancy and disruption log data from the Montréal metro system, covering two lines in both travel directions. Empirical results indicate that post-disruption travel times exhibit pronounced distributional asymmetries that vary with traveled distance, as well as significant error dependence across trains. The proposed models consistently outperform baseline specifications in both point prediction accuracy and uncertainty quantification, with the skew-$t$ model demonstrating the most robust performance for longer journeys. These findings underscore the importance of incorporating both distributional flexibility and error dependence when forecasting post-disruption travel times in urban rail systems.

翻译：中断是交通系统的固有特征，其发生具有不可预测性且持续时间各异。即使在事件被报告为已解决后，中断仍可能引发列车运行异常，从而为乘客候车时间和行程时间带来显著不确定性。因此，准确预测中断后的行程时间对公交运营商和乘客信息系统而言仍是一项关键挑战。本文开发了一个用于中断后列车行程时间的贝叶斯时空建模框架，该框架明确捕捉了恢复期间观察到的列车交互作用、发车间隔不均衡性以及非高斯分布特征。所提模型将行程时间分解为延误分量和行程分量，并引入移动平均误差结构以表征连续列车间的依赖性。采用偏态正态分布和偏态-$t$分布来灵活适应中断后行程时间中的异方差性、偏态性以及厚尾行为。该框架使用来自蒙特利尔地铁系统的高精度轨道占用数据与中断日志数据进行评估，涵盖两条线路的双向运行。实证结果表明，中断后行程时间呈现出随行驶距离变化的显著分布不对称性，以及列车间显著的误差依赖性。所提模型在点预测精度和不确定性量化方面均持续优于基准设定，其中偏态-$t$模型在较长行程中表现出最稳健的性能。这些发现强调了在城市轨道交通系统中预测中断后行程时间时，同时纳入分布灵活性与误差依赖性的重要性。