A spatiotemporal analysis of the impact of lockdown and coronavirus on London’s bicycle hire scheme: from response to recovery to a new normal

ABSTRACT

The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives. At the peak of the outbreak, lockdown measures and social distancing changed the ways in which cities function. In particular, they had profound impacts on urban transportation systems, with public transport being shut down in many cities. Bike share systems (BSS) were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels. However, the studies published to date focus mainly on the first year of the pandemic, when various waves saw continual relaxing and reintroductions of restrictions. Therefore, they fall short of exploring the role of BSS as we move to the post-pandemic period. To address this gap, this study uses origin-destination (O-D) flow data from London’s Santander Cycle Hire Scheme from 2019–2021 to analyze the changing use of BSS throughout the first two years of the pandemic, from lockdown to recovery. A Gaussian mixture model (GMM) is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance. The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021. In agreement with previous research, BSS usage was found to have declined by nearly 30% during the first lockdown. Usage then saw a sharp increase as restrictions were lifted, characterized by longer, less direct trips throughout the afternoon rather than typical peak commuting trips. Although the aggregate number of BSS trips appeared to return to normal by October 2020, this was against the backdrop of continuing restrictions on international travel and work from home orders. The period between July and December 2021 was the first period that all government restrictions were lifted. During this time, BSS trips reached higher levels than in 2019. Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours. The results indicate that the pandemic may have had sustained impacts on travel behavior, leading to a “new normal” that reflects different ways of working.

KEYWORDS:

• COVID-19
• micro-mobility
• restrictive measures
• bicycle share system

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The cycling data that support the findings of this study are openly available in Transport for London at https://cycling.data.tfl.gov.uk/.

Additional information

Notes on contributors

Xiaowei Gao

Xiaowei Gao is a PhD student in professor in SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, UCL. Xiaowei is supervised by James and Huanfa. His research interests include spatial-temporal traffic risk data mining, urban computing on cycling related mobility, and bicycle share systems.

Huanfa Chen

Huanfa Chen is a lecturer in Centre for Advanced Spatial Analysis, Faculty of the Built Environment, UCL. Huanfa’ research interests lie in GIS, machine learning, and spatio-temporal optimisation to address contemporary challenges in the planning and operations of urban services, incl. police, healthcare, and transport.

James Haworth

James Haworth is an associate professor in SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, UCL. James’ research interests lie in spatio-temporal modelling and analytics, in particular applications of machine learning, deep learning and computer vision to the geoinformation sciences.