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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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    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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