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

TL;DR

This study investigates how face masks, vaccination rates, and crowding influence London Underground demand during COVID-19, revealing key drivers and heterogeneity in user preferences through a stated choice experiment.

Contribution

It provides novel insights into the combined effects of health measures and crowding on transit demand during a pandemic, using detailed modeling of user preferences.

Findings

Mandatory face masks increase demand.

Higher vaccination rates boost demand, especially with crowding.

Preference heterogeneity shows not all users respond positively.

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.