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Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands

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  • Singh, Jyotsna
  • Homem de Almeida Correia, Gonçalo
  • van Wee, Bert
  • Barbour, Natalia

Abstract

After the outbreak of COVID-19 pandemic, crowding has been highlighted as a risk factor for contracting acute respiratory infections (ARIs) such as COVID-19, which has affected the demand for public transport. Although several countries, including the Netherlands, have implemented differential fare systems for peak and off-peak travel to reduce crowding during the rush hours, the problem of overcrowding on trains has remained prevalent and is expected to cause more disutility than even before the pandemic. A stated choice experiment in the Netherlands is conducted to understand the extent to which people can be motivated to change their departure time to avoid crowded trains during rush hours by offering them real-time information on on-board crowding levels and a discount on the train fare. To gain further insights into how travelers respond to crowding and capture unobserved heterogeneity in the data, latent class models have been estimated. Unlike the previous studies, the respondents were segregated into two groups before the start of the choice experiment based on their indicated preference to schedule a delay earlier or later than their desired departure. To study the change in travel behavior during the pandemic, the context of different vaccination stages was also provided in the choice experiment. Background information collected in the experiment was broadly categorized as socio-demographic, travel and work-related factors, and attitudes towards health and COVID-19. It was found that the coefficients obtained for the main attributes which were presented in the choice experiment (on-board crowd levels, scheduled delay and discount offered on full fare) were found statistically significant, and in line with previous research. It was concluded that when most of the people are vaccinated in the Netherlands, the travelers become less averse to on-board crowding. The research also indicates that certain groups of respondents, such as those who are highly crowd averse, and are not students, can be motivated to change their departure time if real-time crowding information was provided. Other groups of respondents who were found to value fare discounts can also be motivated to change their departure by similar incentives.

Suggested Citation

  • Singh, Jyotsna & Homem de Almeida Correia, Gonçalo & van Wee, Bert & Barbour, Natalia, 2023. "Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:transa:v:170:y:2023:i:c:s0965856423000484
    DOI: 10.1016/j.tra.2023.103628
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    1. Kazem Nasserinejad & Joost van Rosmalen & Wim de Kort & Emmanuel Lesaffre, 2017. "Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    2. Nema Dean & Adrian Raftery, 2010. "Latent class analysis variable selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 11-35, February.
    3. 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.
    4. Chris Hendrickson & George Kocur, 1981. "Schedule Delay and Departure Time Decisions in a Deterministic Model," Transportation Science, INFORMS, vol. 15(1), pages 62-77, February.
    5. Cox, Tom & Houdmont, Jonathan & Griffiths, Amanda, 2006. "Rail passenger crowding, stress, health and safety in Britain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 244-258, March.
    6. Bert Van Wee & David Banister, 2016. "How to Write a Literature Review Paper?," Transport Reviews, Taylor & Francis Journals, vol. 36(2), pages 278-288, March.
    7. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2017. "Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 228-246.
    8. Wen, Chieh-Hua & Lai, Shan-Ching, 2010. "Latent class models of international air carrier choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(2), pages 211-221, March.
    9. 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.
    10. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    11. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    12. Joan Walker & Jieping Li, 2007. "Latent lifestyle preferences and household location decisions," Journal of Geographical Systems, Springer, vol. 9(1), pages 77-101, April.
    13. 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.
    14. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
    15. Li, Haiying & Li, Xian & Xu, Xinyue & Liu, Jun & Ran, Bin, 2018. "Modeling departure time choice of metro passengers with a smart corrected mixed logit model - A case study in Beijing," Transport Policy, Elsevier, vol. 69(C), pages 106-121.
    16. Tang, Yili & Jiang, Yu & Yang, Hai & Nielsen, Otto Anker, 2020. "Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 247-267.
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    2. Barbour, Natalia & Abdel-Aty, Mohamed & Sevim, Alican, 2024. "Intended work from home frequency after the COVID-19 pandemic and the role of socio-demographic, psychological, disability, and work-related factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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