The effects of different travel modes on COVID-19 transmission in global cities

Abstract Previous studies have been focused primarily on modelling and predicting the transmission of COVID-19. While little research has been conducted to understand the impacts of different travel modes on the transmission of COVID-19, without an explicit understanding of the travel mode effects, many people intuitively perceive non-motorized travel modes to be safer than public transit as passengers in public transit are confined to small, enclosed spaces where the virus can transmit more easily. During the period when urban mobility gradually returns towards what was called ‘normal’ and transit systems and urban facilities reopen, new waves of the pandemic might be generated as travel mode choices significantly differ across cities and different travel behaviors are associated with diverse infectious sources. Thus, the current study focuses on understanding the impact of different travel modes on the transmission of COVID-19 in the long-term and at world-wide scales, aspects that have not received much attention in the research literature. Accordingly, a multivariate time series analysis has been developed to examine the impacts of daily confirmed cases and travel modes, based on driving, public transit, and walking as recorded in the Apple Mobility Trends Reports on COVID-19 transmission risks in 71 cities throughout the world from January to November 2020. The impact of population density in built-up areas and the degree to which the `wearing' of facemasks affects infections are also investigated. Among the three travel modes we examine, driving is the safest way to commute because drivers are physically separate from crowds. Unexpectedly, walking has a relatively low risk when the population density in built-up areas is high, which suggests that, globally, people have increased awareness of pandemic prevention. Although the general public is more worried about using public transit, this mode can still be safe in many large cities, a factor that is vital for informing policy making and developing trust among citizens so they will continue to commute using public transit when strict preventative measures are in place. From another perspective, infectious sources make the largest contribution to daily confirmed cases, thus demonstrating the importance of strict quarantine measures to block the source of infection. The results and conclusions presented herein are based on an analysis of spatio-temporal data that helps inform policy making and enable cities to be kept open when controlling the pandemic, which has become an urgent task for the international community when rebuilding the economy.