IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v203y2026ics0965856425003416.html

Heterogeneity in route choice behaviour during unplanned train disruptions considering the possibility of teleworking

Author

Listed:
  • Bickel, Julia
  • Geržinič, Nejc
  • van Oort, Niels
  • de Bruyn, Menno
  • Molin, Eric

Abstract

Unplanned train disruptions are a source of passenger dissatisfaction because they are often accompanied by overcrowding and lack of information. To better accommodate passengers during disruptions and preventing travellers from switching to other less sustainable modes of transport, mitigating control strategies can be applied by railway operators. This however requires predicted passenger flows over all available travel options as an input. Due to the COVID-19 pandemic these passenger flows have becomes less predictable, as many travellers have gained an additional feasible alternative to cope with unplanned disruptions on outbound commuter trips − they may return home and start teleworking. Because this travel option is only available to teleworkers and now utilized more than before the COVID-19 pandemic, heterogeneity in route choice behaviour has increased. To fill this knowledge gap and provide predictions of passenger flows, an online survey containing a labelled stated choice experiment was carried out among Dutch train commuters. Consequently, a latent class choice model was estimated to investigate the influence of disruption characteristics, teleworking, COVID-19 risk perception and information provision on travel behaviour during train disruptions in the Netherlands and uncover heterogeneity in behaviour. Our results indicate that the strongest predictors of route choice behaviour are the moment of discovering the disruption, the disruption length and job characteristics. Uncovering four latent classes shows the different valuations of crowding, waiting times and additional travel times among commuters. Commuters with the option to telework are more likely to return back home during disruptions as well as commuters who are sceptic towards the provided information and those who are still conscious of COVID-19. Commuters who cannot telework and trust the provided information are more likely to reroute within the train network whereas commuters who cannot telework and do not trust the provided information are more likely to wait for the disrupted services to resume.

Suggested Citation

  • Bickel, Julia & Geržinič, Nejc & van Oort, Niels & de Bruyn, Menno & Molin, Eric, 2026. "Heterogeneity in route choice behaviour during unplanned train disruptions considering the possibility of teleworking," Transportation Research Part A: Policy and Practice, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:transa:v:203:y:2026:i:c:s0965856425003416
    DOI: 10.1016/j.tra.2025.104708
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856425003416
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2025.104708?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tommaso Schettini & Ola Jabali & Federico Malucelli, 2022. "Demand-Driven Timetabling for a Metro Corridor Using a Short-Turning Acceleration Strategy," Transportation Science, INFORMS, vol. 56(4), pages 919-937, July.
    2. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    3. Wardman, Mark, 2004. "Public transport values of time," Transport Policy, Elsevier, vol. 11(4), pages 363-377, October.
    4. Arkadiusz Adam Drabicki & Md Faqhrul Islam & Andrzej Szarata, 2021. "Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City," Energies, MDPI, vol. 14(16), pages 1-14, August.
    5. Sanmay Shelat & Oded Cats & Sander van Cranenburgh, 2021. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Papers 2104.10973, arXiv.org, revised Apr 2022.
    6. Shelat, Sanmay & Cats, Oded & van Cranenburgh, Sander, 2022. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 357-371.
    7. Joshua Auld & Hubert Ley & Omer Verbas & Nima Golshani & Josiane Bechara & Angela Fontes, 2020. "A stated-preference intercept survey of transit-rider response to service disruptions," Public Transport, Springer, vol. 12(3), pages 557-585, October.
    8. Soza-Parra, Jaime & Raveau, Sebastián & Muñoz, Juan Carlos & Cats, Oded, 2019. "The underlying effect of public transport reliability on users’ satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 83-93.
    9. Wen Hua & Ghim Ping Ong, 2018. "Effect of information contagion during train service disruption for an integrated rail-bus transit system," Public Transport, Springer, vol. 10(3), pages 571-594, December.
    10. Nguyen-Phuoc, Duy Q. & Currie, Graham & De Gruyter, Chris & Young, William, 2018. "Transit user reactions to major service withdrawal – A behavioural study," Transport Policy, Elsevier, vol. 64(C), pages 29-37.
    11. Sanmay Shelat & Thijs van de Wiel & Eric Molin & J W C van Lint & Oded Cats, 2022. "Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-19, March.
    12. Kapatsila, Bogdan & Bahamonde-Birke, Francisco J. & van Lierop, Dea & Grisé, Emily, 2023. "Impact of the COVID-19 pandemic on the comfort of riding a crowded bus in Metro Vancouver, Canada," Transport Policy, Elsevier, vol. 141(C), pages 83-96.
    13. Jaime Soza-Parra & Sebastián Raveau & Juan Carlos Muñoz, 2022. "Public transport reliability across preferences, modes, and space," Transportation, Springer, vol. 49(2), pages 621-640, April.
    14. van Oort, Niels, 2014. "Incorporating service reliability in public transport design and performance requirements: International survey results and recommendations," Research in Transportation Economics, Elsevier, vol. 48(C), pages 92-100.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peftitsi, Soumela & Jenelius, Erik & Cats, Oded, 2022. "Modeling the effect of real-time crowding information (RTCI) on passenger distribution in trains," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 354-368.
    2. Tinessa, Fiore & Román Garcia, Concepción & Simonelli, Fulvio & Papola, Andrea & Pagliara, Francesca, 2024. "How public transport users would react to different pandemic alert scenarios in the post-vaccine era? An analysis of preferences and attitudes of the users in the metropolitan area of Naples (Italy)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    3. Sarker, Rumana Islam & Kaplan, Sigal & Mailer, Markus & Timmermans, Harry J.P., 2019. "Applying affective event theory to explain transit users’ reactions to service disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 593-605.
    4. Valenzuela, Berenice & Galilea, Patricia & Raveau, Sebastián, 2025. "Sexual harassment and its effects on transport mode choice: An analysis with a gender perspective," Research in Transportation Economics, Elsevier, vol. 112(C).
    5. Jaydarifard, Saeed & Morawska, Lidia & Paz, Alexander, 2024. "Mitigating airborne infection risks in public transportation: A systematic review," Transport Policy, Elsevier, vol. 155(C), pages 309-320.
    6. Esmailpour, Javad & Aghabayk, Kayvan & Aghajanzadeh, Mohammad & De Gruyter, Chris, 2022. "Has COVID-19 changed our loyalty towards public transport? Understanding the moderating role of the pandemic in the relationship between service quality, customer satisfaction and loyalty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 80-103.
    7. Menno Yap & Howard Wong & Oded Cats, 2025. "Public transport crowding valuation in a post-pandemic era," Transportation, Springer, vol. 52(1), pages 287-306, February.
    8. Semple, Torran & Fonzone, Achille & Fountas, Grigorios & Downey, Lucy, 2023. "An empirical analysis of the factors influencing Scottish residents’ compliance with COVID-19 travel restrictions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    9. Kwang-Sub Lee & Jin Ki Eom, 2024. "Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility," Transportation, Springer, vol. 51(5), pages 1907-1961, October.
    10. Guilhem Lecouteux & Léonard Moulin, 2023. "Cycling in the Aftermath of COVID-19: An Empirical Estimation of the Social Dynamics of Bicycle Adoption in Paris," GREDEG Working Papers 2023-02, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    11. Jenelius, Erik, 2018. "Public transport experienced service reliability: Integrating travel time and travel conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 275-291.
    12. Karimi, Sina & Samadzad, Mahdi & Lesteven, Gaele, 2024. "Navigating public transport during a pandemic: Key lessons on travel behavior and social equity from two surveys in Tehran," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    13. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    14. Lijesen, Mark, 2025. "Valuation of stochastic occupancy levels and public transport policy options during the COVID pandemic," Research in Transportation Economics, Elsevier, vol. 109(C).
    15. Peer, Stefanie & Knockaert, Jasper & Verhoef, Erik T., 2016. "Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 314-333.
    16. Basnak, Paul & Balbontín, Camila & Vallejo-Borda, Jose Agustin & Feres, Fernando & Reyes, José & Beck, Matthew J. & Hensher, David A. & Giesen, Ricardo, 2025. "Determinants of mode changes in times of COVID-19: A study of 11 main Latin American cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 196(C).
    17. Xi, Haoning & Li, Qin & Hensher, David A. & Nelson, John D. & Ho, Chinh, 2023. "Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments," Transport Policy, Elsevier, vol. 136(C), pages 98-112.
    18. Nejc Geržinič & Niels Oort & Sascha Hoogendoorn-Lanser & Oded Cats & Serge Hoogendoorn, 2023. "Potential of on-demand services for urban travel," Transportation, Springer, vol. 50(4), pages 1289-1321, August.
    19. Cheng, Long & Ning, Zhe & Lei, Da & Cai, Xinmei & Chen, Xuewu, 2024. "Assessing the influence of the COVID-19 pandemic on passengers' reliance on public transport," Journal of Transport Geography, Elsevier, vol. 119(C).
    20. Yeganeh, Sayna Firoozi & Khademi, Navid & Farahani, Hojjatollah, 2025. "Beyond gender-neutral transportation planning: Developing a multidimensional scale for assessing the suitability of a travel mode for women," Journal of Transport Geography, Elsevier, vol. 128(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:203:y:2026:i:c:s0965856425003416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.