一种用于地铁网络灾后旅行时间预测的贝叶斯框架
A Bayesian Framework for Post-disruption Travel Time Prediction in Metro Networks
AI总结：
本文提出一种贝叶斯框架，用于预测地铁网络灾后旅行时间，通过考虑分布灵活性和误差依赖性，提高预测精度和不确定性量化。
摘要：
交通系统中的中断是固有特征，其发生具有不可预测性和持续时间不一的特点。即使在事件被报告为解决后，中断仍可能引发不规律的列车运行，从而在乘客等待和旅行时间上产生显著不确定性。因此，准确预测灾后旅行时间仍然是公共交通运营商和乘客信息系统的关键挑战。本文开发了一种贝叶斯时空建模框架，用于灾后列车旅行时间，明确捕捉恢复期间观察到的列车互动、班次不平衡和非高斯分布特征。所提出的模型将旅行时间分解为延误和行程组成部分，并采用移动平均误差结构来表示连续列车之间的依赖性。斯肯正常分布和斯肯t分布被用来灵活地适应灾后旅行时间中的异方差性、偏态和重尾行为。该框架使用高分辨率轨道占用和中断日志数据对蒙特利尔地铁系统进行了评估，涵盖了两条线路的两个行驶方向。实证结果表明，灾后旅行时间表现出显著的分布不对称性，且随行驶距离变化，同时列车之间的误差依赖性显著。所提出的模型在点预测精度和不确定性量化方面均优于基准规范，其中斯肯t模型在较长行程中表现出最稳健的性能。这些发现强调了在预测城市轨道交通系统灾后旅行时间时，纳入分布灵活性和误差依赖性的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
