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Resilience of public transport in the face of disruptions: Insights from explainable machine learning

Author

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  • Cottreau, Benjamin
  • Celbiş, Mehmet Güney
  • Manout, Ouassim
  • Bouzouina, Louafi

Abstract

Disruptions in public transport (PT) can have a major impact on passenger activities and on the attractiveness of the service, particularly when they are not absorbed by the network as a whole. The present study aims to detect the presence of disruption and assess the contribution of existing alternative bus or tramway stops to the resilience of the PT network, using explainable machine learning techniques. The detection task is formulated as a supervised classification problem performed using Random Forest (RF) for 39 different subway stations, using Automatic Fare Collection (AFC) data and Service Disruption logs (SD-logs). Furthermore, the SHapley Additive exPlanation (SHAP) interpretation method is implemented to retrieve the magnitude and the direction of each alternative stop’s contribution to PT resilience. Results show that the proposed modeling framework has high prediction performance, can minimize false alarm rates, and can foresee the occurrence of disruptions 5 min before their registered beginning in SD-logs. Findings also indicate where demand is reallocated, resulting in 5 different resilience clusters for subway stations. Density and connectivity emerge as two major attributes of resilience that have a central role in the design of disruption management (tactical) and development (strategical) plans. The proposed approach has been applied to the PT network of Lyon (France) and is replicable by adapting the hyperparameters to the observed use in other PT networks.

Suggested Citation

  • Cottreau, Benjamin & Celbiş, Mehmet Güney & Manout, Ouassim & Bouzouina, Louafi, 2025. "Resilience of public transport in the face of disruptions: Insights from explainable machine learning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:transa:v:199:y:2025:i:c:s0965856425001788
    DOI: 10.1016/j.tra.2025.104550
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    References listed on IDEAS

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    1. Benjamin Cottreau & Adel Adraoui & Ouassim Manout & Louafi Bouzouina, 2023. "Spatio‐temporal patterns of the impact of COVID‐19 on public transit: An exploratory analysis from Lyon, France," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(8), pages 1702-1721, October.
    2. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    3. Zhang, X. & Miller-Hooks, E. & Denny, K., 2015. "Assessing the role of network topology in transportation network resilience," Journal of Transport Geography, Elsevier, vol. 46(C), pages 35-45.
    4. Menno Yap & Oded Cats, 2021. "Predicting disruptions and their passenger delay impacts for public transport stops," Transportation, Springer, vol. 48(4), pages 1703-1731, August.
    5. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    6. Reggiani, Aura & Nijkamp, Peter & Lanzi, Diego, 2015. "Transport resilience and vulnerability: The role of connectivity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 4-15.
    7. Liu, Luyu & Porr, Adam & Miller, Harvey J., 2024. "Measuring the impacts of disruptions on public transit accessibility and reliability," Journal of Transport Geography, Elsevier, vol. 114(C).
    8. Vodopivec, Neža & Miller-Hooks, Elise, 2019. "Transit system resilience: Quantifying the impacts of disruptions on diverse populations," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Simona-Vasilica Oprea & Adela Bâra & Florina Camelia Puican & Ioan Cosmin Radu, 2021. "Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
    10. Egu, Oscar & Bonnel, Patrick, 2020. "How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 267-282.
    11. Cox, Andrew & Prager, Fynnwin & Rose, Adam, 2011. "Transportation security and the role of resilience: A foundation for operational metrics," Transport Policy, Elsevier, vol. 18(2), pages 307-317, March.
    12. Khaled, Abdullah A. & Jin, Mingzhou & Clarke, David B. & Hoque, Mohammad A., 2015. "Train design and routing optimization for evaluating criticality of freight railroad infrastructures," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 71-84.
    13. Ali El Zein & Adrien Beziat & Pascal Pochet & Olivier Klein & Stephanie Vincent, 2022. "What drives the changes in public transport use in the context of the COVID‐19 pandemic? Highlights from Lyon metropolitan area," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S1), pages 122-141, November.
    14. Yap, M.D. & Nijënstein, S. & van Oort, N., 2018. "Improving predictions of public transport usage during disturbances based on smart card data," Transport Policy, Elsevier, vol. 61(C), pages 84-95.
    15. Benjamin Cottreau & Adel Adraoui & Louafi Bouzouina & Ouassim Manout, 2023. "Spatio‐temporal patterns of the impact of COVID‐19 on public transit: An exploratory analysis from Lyon, France," Post-Print halshs-04232773, HAL.
    16. Malandri, Caterina & Fonzone, Achille & Cats, Oded, 2018. "Recovery time and propagation effects of passenger transport disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 7-17.
    17. Benjamin Cottreau & Ouassim Manout & Louafi Bouzouina, 2025. "Spatio-temporal impacts of unplanned service disruptions on public transit demand," Post-Print hal-05233733, HAL.
    18. Sun, Huijun & Wu, Jianjun & Wu, Lijuan & Yan, Xiaoyong & Gao, Ziyou, 2016. "Estimating the influence of common disruptions on urban rail transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 62-75.
    19. Mo, Baichuan & Koutsopoulos, Haris N. & Shen, Zuo-Jun Max & Zhao, Jinhua, 2023. "Robust path recommendations during public transit disruptions under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 82-107.
    20. Cats, O., 2016. "The robustness value of public transport development plans," Journal of Transport Geography, Elsevier, vol. 51(C), pages 236-246.
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