IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0264805.html
   My bibliography  Save this article

Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks

Author

Listed:
  • Sanmay Shelat
  • Thijs van de Wiel
  • Eric Molin
  • J W C van Lint
  • Oded Cats

Abstract

Introduction: Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers’ confidence it is critical to assess their risk determinants and trade-offs. Methods: To this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers’ COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively. Results: We find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact—nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: reduced crowding, mask mandates, and increased sanitization. Conclusion: Since we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0264805
    DOI: 10.1371/journal.pone.0264805
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0264805
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0264805&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0264805?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
    ---><---

    References listed on IDEAS

    as
    1. Ilona D M Bos & Rob E C M Van der Heijden & Eric J E Molin & Harry J P Timmermans, 2004. "The Choice of Park and Ride Facilities: An Analysis Using a Context-Dependent Hierarchical Choice Experiment," Environment and Planning A, , vol. 36(9), pages 1673-1686, September.
    2. Paul T E Cusack, 2020. "Anxiety Disorders," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24255-24260, October.
    3. Beck, Matthew J. & Hensher, David A., 2020. "Insights into the impact of COVID-19 on household travel and activities in Australia – The early days of easing restrictions," Transport Policy, Elsevier, vol. 99(C), pages 95-119.
    4. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    5. Elias, Wafa & Albert, Gila & Shiftan, Yoram, 2013. "Travel behavior in the face of surface transportation terror threats," Transport Policy, Elsevier, vol. 28(C), pages 114-122.
    6. Li, Zheng & Hensher, David A., 2011. "Crowding and public transport: A review of willingness to pay evidence and its relevance in project appraisal," Transport Policy, Elsevier, vol. 18(6), pages 880-887, November.
    7. Beck, Matthew J. & Hensher, David A., 2020. "Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions," Transport Policy, Elsevier, vol. 96(C), pages 76-93.
    8. Molin, Eric & Blangé, Joey & Cats, Oded & Chorus, Caspar, 2017. "Willingness to pay for safety improvements in passenger air travel," Journal of Air Transport Management, Elsevier, vol. 62(C), pages 165-175.
    9. Aaditya, Bh. & Rahul, T.M., 2021. "Psychological impacts of COVID-19 pandemic on the mode choice behaviour: A hybrid choice modelling approach," Transport Policy, Elsevier, vol. 108(C), pages 47-58.
    10. Kuo-Ying Wang, 2014. "How Change of Public Transportation Usage Reveals Fear of the SARS Virus in a City," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rossetti, Tomás & Daziano, Ricardo A., 2024. "Crowding multipliers on shared transportation in New York City: The effects of COVID-19 and implications for a sustainable future," Transport Policy, Elsevier, vol. 145(C), pages 224-236.
    2. Hsueh, Chieh & Lin, Jen-Jia, 2023. "Influential factors of the route choices of scooter riders: A GPS-based data study," Journal of Transport Geography, Elsevier, vol. 113(C).
    3. 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.

    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. 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.
    2. Balbontin, Camila & Hensher, David A. & Beck, Matthew J., 2022. "Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    3. Basnak, Paul & Giesen, Ricardo & Muñoz, Juan Carlos, 2022. "Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 140-156.
    4. Kim, Suji & Lee, Sujin & Ko, Eunjeong & Jang, Kitae & Yeo, Jiho, 2021. "Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price," Journal of Transport Geography, Elsevier, vol. 96(C).
    5. Carolina Silva Costa & Cira Souza Pitombo & Felipe Lobo Umbelino de Souza, 2022. "Travel Behavior before and during the COVID-19 Pandemic in Brazil: Mobility Changes and Transport Policies for a Sustainable Transportation System in the Post-Pandemic Period," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    6. Currie, Graham & Jain, Taru & Aston, Laura, 2021. "Evidence of a post-COVID change in travel behaviour – Self-reported expectations of commuting in Melbourne," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 218-234.
    7. Das, Sanhita & Boruah, Alice & Banerjee, Arunabha & Raoniar, Rahul & Nama, Suresh & Maurya, Akhilesh Kumar, 2021. "Impact of COVID-19: A radical modal shift from public to private transport mode," Transport Policy, Elsevier, vol. 109(C), pages 1-11.
    8. Hamza Zubair & Ampol Karoonsoontawong & Kunnawee Kanitpong, 2022. "Effects of COVID-19 on Travel Behavior and Mode Choice: A Case Study for the Bangkok Metropolitan Area," Sustainability, MDPI, vol. 14(15), pages 1-26, July.
    9. Loa, Patrick & Hossain, Sanjana & Mashrur, Sk. Md. & Liu, Yicong & Wang, Kaili & Ong, Felita & Habib, Khandker Nurul, 2021. "Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area," Transport Policy, Elsevier, vol. 110(C), pages 71-85.
    10. Figliozzi, Miguel & Unnikrishnan, Avinash, 2021. "Exploring the impact of socio-demographic characteristics, health concerns, and product type on home delivery rates and expenditures during a strict COVID-19 lockdown period: A case study from Portlan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 1-19.
    11. Mashrur, Sk.Md. & Wang, Kaili & Habib, Khandker Nurul, 2022. "Will COVID-19 be the end for the public transit? Investigating the impacts of public health crisis on transit mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 352-378.
    12. Tscharaktschiew, Stefan & Reimann, Felix, 2021. "On employer-paid parking and parking (cash-out) policy: A formal synthesis of different perspectives," Transport Policy, Elsevier, vol. 110(C), pages 499-516.
    13. Molloy, Joseph & Schatzmann, Thomas & Schoeman, Beaumont & Tchervenkov, Christopher & Hintermann, Beat & Axhausen, Kay W., 2021. "Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel," Transport Policy, Elsevier, vol. 104(C), pages 43-51.
    14. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    15. Shuai Yu & Bin Li & Dongmei Liu, 2023. "Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing–Tianjin–Hebei Urban Agglomera," IJERPH, MDPI, vol. 20(2), pages 1-22, January.
    16. van Wee, Bert & Witlox, Frank, 2021. "COVID-19 and its long-term effects on activity participation and travel behaviour: A multiperspective view," Journal of Transport Geography, Elsevier, vol. 95(C).
    17. Claudiu George Bocean & Luminita Popescu & Anca Antoaneta Varzaru & Costin Daniel Avram & Anica Iancu, 2023. "Work-Life Balance and Employee Satisfaction during COVID-19 Pandemic," Sustainability, MDPI, vol. 15(15), pages 1-15, July.
    18. Hensher, David A. & Wei, Edward & Beck, MatthewJ. & Balbontin, Camila, 2021. "The impact of COVID-19 on cost outlays for car and public transport commuting - The case of the Greater Sydney Metropolitan Area after three months of restrictions," Transport Policy, Elsevier, vol. 101(C), pages 71-80.
    19. Feuillet, Thierry & Bulteau, Julie & Dantan, Sophie, 2021. "Modelling context-specific relationships between neighbourhood socioeconomic disadvantage and private car use," Journal of Transport Geography, Elsevier, vol. 93(C).
    20. Wang, Yiyuan & Shen, Qing & Abu Ashour, Lamis & Dannenberg, Andrew L., 2022. "Ensuring equitable transportation for the disadvantaged: Paratransit usage by persons with disabilities during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 84-95.

    More about this item

    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:plo:pone00:0264805. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.