A Bayesian Framework for Post-disruption Travel Time Prediction in Metro Networks

• URL: http://arxiv.org/abs/2602.19952v1
• Date: Mon, 23 Feb 2026 15:20:46 GMT
• Title: A Bayesian Framework for Post-disruption Travel Time Prediction in Metro Networks
• Authors: Shayan Nazemi, Aurélie Labbe, Stefan Steiner, Pratheepa Jeganathan, Martin Trépanier, Léo R. Belzile,
• Abstract summary: Disruptions can induce irregular train operations that generate substantial uncertainty in passenger waiting and travel times.<n>This paper develops a modeling framework for post-disruption train travel times that captures train interactions, headway imbalance, and non-Gaussian distributional characteristics observed during recovery periods.<n> Empirical results indicate that post-disruption travel times exhibit pronounced distributional asymmetries that vary with distance, as well as significant error dependence across trains.
• Score: 0.6927055673104933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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