Spatio-temporal impacts of unplanned service disruptions on public transit demand

Benjamin Cottreau, Ouassim Manout () and Louafi Bouzouina
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Benjamin Cottreau: LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique, KEOLIS
Ouassim Manout: LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique
Louafi Bouzouina: LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique

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Abstract: Public Transit (PT) operators often face unplanned subway service disruptions, which are challenging to handle because they need rapid and effective management strategies. Under such conditions, their goal is to limit user dropout by proposing attractive solutions. With the development of Automatic Fare Collection (AFC) systems and the ability to deal with increasing data volumes, research has recently focused on demand-oriented analysis of disruptions and demand-based strategies. However, AFC data can be incomplete and delayed when uploaded in real time and, therefore, are poorly monitored by PT operators. For this reason, this work seeks to show the relevance of using AFC data to enhance the implementation of disruption management strategies. It aims to understand when, where, and why service disruptions occur. Findings show that 39% of the service disruptions observed between 2021 and 2023 have a negative impact on demand. Thanks to the Gaussian Mixture Model (GMM) algorithm, we distinguish 3 levels of negative impacts: high, medium and low intensity. Results highlight that the PT system is more vulnerable to long-lasting disruptions, which occur during peak hours. The connection with the national railway system also increases the vulnerability of PT systems to disruptions. Using a Multinomial Logistic Regression model, this work highlights the causes that mostly harm the demand and calls for line-specific management strategies. In addition, the contextual analysis introduced in this study reveals the clues left by disruptions in demand levels and argues for the development of online disruption detection coupled with flow redistribution models to enhance decision-making.

Keywords: Resilience; Ridership; Smart card; Demand analysis; Public transit; Disruption (search for similar items in EconPapers)
Date: 2025-03
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Published in Transportation Research Interdisciplinary Perspectives, 2025, 30, Paper No. - 101354. ⟨10.1016/j.trip.2025.101354⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05233733

DOI: 10.1016/j.trip.2025.101354

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