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Face masks, vaccination rates and low crowding drive the demand for the London Underground during the COVID-19 pandemic

Author

Listed:
  • Prateek Bansal
  • Roselinde Kessels
  • Rico Krueger
  • Daniel J Graham

Abstract

The COVID-19 pandemic has drastically impacted people's travel behaviour and out-of-home activity participation. While countermeasures are being eased with increasing vaccination rates, the demand for public transport remains uncertain. To investigate user preferences to travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N=961). We analysed the collected data using multinomial and mixed logit models. Our analysis provides insights into the sensitivity of the demand for the London Underground with respect to travel attributes (crowding density and travel time), the epidemic situation (confirmed new COVID-19 cases), and interventions (vaccination rates and mandatory face masks). Mandatory face masks and higher vaccination rates are the top two drivers of travel demand for the London Underground during COVID-19. The positive impact of vaccination rates on the Underground demand increases with crowding density, and the positive effect of mandatory face masks decreases with travel time. Mixed logit reveals substantial preference heterogeneity. For instance, while the average effect of mandatory face masks is positive, preferences of around 20% of the pre-pandemic users to travel by the Underground are negatively affected. The estimated demand sensitivities are relevant for supply-demand management in transit systems and the calibration of advanced epidemiological models.

Suggested Citation

  • Prateek Bansal & Roselinde Kessels & Rico Krueger & Daniel J Graham, 2021. "Face masks, vaccination rates and low crowding drive the demand for the London Underground during the COVID-19 pandemic," Papers 2107.02394, arXiv.org.
  • Handle: RePEc:arx:papers:2107.02394
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    References listed on IDEAS

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    1. Prateek Bansal & Daniel Horcher & Daniel J. Graham, 2020. "A Dynamic Choice Model with Heterogeneous Decision Rules: Application in Estimating the User Cost of Rail Crowding," Papers 2007.03682, arXiv.org.
    2. 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.
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    4. Elizabeth L. Anderson & Paul Turnham & John R. Griffin & Chester C. Clarke, 2020. "Consideration of the Aerosol Transmission for COVID‐19 and Public Health," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 902-907, May.
    5. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    6. Mark Wardman & Gerard Whelan, 2011. "Twenty Years of Rail Crowding Valuation Studies: Evidence and Lessons from British Experience," Transport Reviews, Taylor & Francis Journals, vol. 31(3), pages 379-398.
    7. 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.
    8. Roselinde Kessels & Bradley Jones & Peter Goos & Martina Vandebroek, 2011. "The usefulness of Bayesian optimal designs for discrete choice experiments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(3), pages 173-188, May.
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    Cited by:

    1. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.

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