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Influence of perceived risk on travel mode choice during Covid-19

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  • Wang, Yu
  • Choudhury, Charisma
  • Hancock, Thomas O.
  • Wang, Yacan
  • Ortúzar, Juan de Dios

Abstract

We aim to understand the effect of different information types on risk perception and examine the relationship between perceived risk and travel behaviour during a pandemic outbreak. A hybrid choice model structure, incorporating a multiple discrete-continuous extreme value model, was formulated and estimated to explore travellers' mode choice and usage changes. We used a risk perception map to visually explain which risk elements felt unfamiliar and uncontrollable to travellers. Virus variation, Potential sequelae, and Long-term coexistence of coronavirus with humans were perceived as the most unfamiliar and uncontrollable risk elements. The model results indicate that increased perceived risk tends to reduce travellers' use of public transport and increase the use of shared bikes and private cars. Reducing passengers’ perceived risk is critical to encourage the re-uptake of public transport in the post-pandemic era. As travellers also show significant heterogeneity, governments should aim to design targeted intervention strategies to encourage different travellers to return to public transport when considering risk communication.

Suggested Citation

  • Wang, Yu & Choudhury, Charisma & Hancock, Thomas O. & Wang, Yacan & Ortúzar, Juan de Dios, 2024. "Influence of perceived risk on travel mode choice during Covid-19," Transport Policy, Elsevier, vol. 148(C), pages 181-191.
    • Handle: RePEc:eee:trapol:v:148:y:2024:i:c:p:181-191
    DOI: 10.1016/j.tranpol.2024.01.009
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    1. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    2. Wang, Yu & Wang, Yacan & Ettema, Dick & Mao, Zidan & Charlton, Samuel G. & Zhou, Huiyu, 2020. "Commuter value perceptions in peak avoidance behavior: An empirical study in the Beijing subway system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 70-84.
    3. Schroeder, Ashley & Pennington-Gray, Lori & Kaplanidou, Kiki & Zhan, Fangzi, 2013. "Destination risk perceptions among U.S. residents for London as the host city of the 2012 Summer Olympic Games," Tourism Management, Elsevier, vol. 38(C), pages 107-119.
    4. 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.
    5. Konstantinos Gkiotsalitis & Oded Cats, 2021. "Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions," Transport Reviews, Taylor & Francis Journals, vol. 41(3), pages 374-392, May.
    6. John M. Barry, 2009. "Pandemics: avoiding the mistakes of 1918," Nature, Nature, vol. 459(7245), pages 324-325, May.
    7. Christidis, Panayotis & Navajas Cawood, Elena & Fiorello, Davide, 2022. "Challenges for urban transport policy after the Covid-19 pandemic: Main findings from a survey in 20 European cities," Transport Policy, Elsevier, vol. 129(C), pages 105-116.
    8. 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.
    9. Emina Herovic & Timothy L. Sellnow & Deanna D. Sellnow, 2020. "Challenges and opportunities for pre-crisis emergency risk communication: lessons learned from the earthquake community," Journal of Risk Research, Taylor & Francis Journals, vol. 23(3), pages 349-364, March.
    10. 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.
    11. 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.
    12. Larissa Neuburger & Roman Egger, 2021. "Travel risk perception and travel behaviour during the COVID-19 pandemic 2020: a case study of the DACH region," Current Issues in Tourism, Taylor & Francis Journals, vol. 24(7), pages 1003-1016, April.
    13. Vicki S. Freimuth & Amelia Jamison & Gregory Hancock & Donald Musa & Karen Hilyard & Sandra Crouse Quinn, 2017. "The Role of Risk Perception in Flu Vaccine Behavior among African‐American and White Adults in the United States," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2150-2163, November.
    14. Naveen, B.R. & Gurtoo, Anjula, 2022. "Public transport strategy and epidemic prevention framework in the Context of Covid-19," Transport Policy, Elsevier, vol. 116(C), pages 165-174.
    15. Vickerman, Roger, 2021. "Will Covid-19 put the public back in public transport? A UK perspective," Transport Policy, Elsevier, vol. 103(C), pages 95-102.
    16. Motoaki, Yutaka & Daziano, Ricardo A., 2015. "A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 217-230.
    17. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
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