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The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities

Author

Listed:
  • Xu, Xinyue
  • Liu, Jing
  • Zhang, Anzhong
  • XieLan, Shiyu
  • Li, Zinuo
  • Liu, Jun
  • Ran, Bin

Abstract

The outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers’ route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers' route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model’s fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers’ personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership.

Suggested Citation

  • Xu, Xinyue & Liu, Jing & Zhang, Anzhong & XieLan, Shiyu & Li, Zinuo & Liu, Jun & Ran, Bin, 2024. "The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:transa:v:183:y:2024:i:c:s0965856424000946
    DOI: 10.1016/j.tra.2024.104046
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    as
    1. Barrero, Jose Maria & Bloom, Nick & Davis, Steven J., 2020. "Why Working From Home Will Stick," SocArXiv wfdbe, Center for Open Science.
    2. Chang, Hung-Hao & Lee, Brian & Yang, Feng-An & Liou, Yu-You, 2021. "Does COVID-19 affect metro use in Taipei?," Journal of Transport Geography, Elsevier, vol. 91(C).
    3. Aghabayk, Kayvan & Esmailpour, Javad & Shiwakoti, Nirajan, 2021. "Effects of COVID-19 on rail passengers’ crowding perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 186-202.
    4. 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.
    5. 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).
    6. Boeri, Marco & Scarpa, Riccardo & Chorus, Caspar G., 2014. "Stated choices and benefit estimates in the context of traffic calming schemes: Utility maximization, regret minimization, or both?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 121-135.
    7. 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.
    8. Han, Xiao & Yu, Yun & Gao, Zi-You & Zhang, H. Michael, 2021. "The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 205-226.
    9. Carlo Prato, 2014. "Expanding the applicability of random regret minimization for route choice analysis," Transportation, Springer, vol. 41(2), pages 351-375, March.
    10. Shahadat Hossain, Md & Rahman Fatmi, Mahmudur, 2022. "Modeling individuals’ preferences towards different levels of vehicle autonomy: A random parameter rank-ordered logit model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 88-99.
    11. Patni, Sagar & Srinivasan, Sivaramakrishnan & Suarez, Juan, 2023. "The impact of COVID-19 on route-level changes in transit demand an analysis of five transit agencies in Florida, USA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    12. Eran Ben-Elia & Erel Avineri, 2015. "Response to Travel Information: A Behavioural Review," Transport Reviews, Taylor & Francis Journals, vol. 35(3), pages 352-377, May.
    13. Bansal, Prateek & Kessels, Roselinde & Krueger, Rico & Graham, Daniel J., 2022. "Preferences for using the London Underground during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 45-60.
    14. Parker, Madeleine E.G. & Li, Meiqing & Bouzaghrane, Mohamed Amine & Obeid, Hassan & Hayes, Drake & Frick, Karen Trapenberg & Rodríguez, Daniel A. & Sengupta, Raja & Walker, Joan & Chatman, Daniel G., 2021. "Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period," Transport Policy, Elsevier, vol. 111(C), pages 53-62.
    15. Marra, Alessio D. & Sun, Linghang & Corman, Francesco, 2022. "The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area," Transport Policy, Elsevier, vol. 116(C), pages 258-268.
    16. 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.
    17. Chorus, Caspar G. & Arentze, Theo A. & Timmermans, Harry J.P., 2008. "A Random Regret-Minimization model of travel choice," Transportation Research Part B: Methodological, Elsevier, vol. 42(1), pages 1-18, January.
    18. Xin, Mengwei & Shalaby, Amer & Feng, Shumin & Zhao, Hu, 2021. "Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method," Transport Policy, Elsevier, vol. 111(C), pages 1-16.
    19. Bagdatli, Muhammed Emin Cihangir & Ipek, Fatima, 2022. "Transport mode preferences of university students in post-COVID-19 pandemic," Transport Policy, Elsevier, vol. 118(C), pages 20-32.
    20. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    21. Ha, Jaehyun & Lee, Sugie & Ko, Joonho, 2020. "Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 147-166.
    22. Raveau, Sebastián & Muñoz, Juan Carlos & de Grange, Louis, 2011. "A topological route choice model for metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 138-147, February.
    23. 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.
    24. Bansal, Prateek & Hurtubia, Ricardo & Tirachini, Alejandro & Daziano, Ricardo A., 2019. "Flexible estimates of heterogeneity in crowding valuation in the New York City subway," Journal of choice modelling, Elsevier, vol. 31(C), pages 124-140.
    25. 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.
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