[Paper Review] Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London

Shahram Heydari, Garyfallos Konstantinoudis+1 more|arXiv (Cornell University)|Jul 24, 2021

Urban Transport and Accessibility52 references62 citations

TL;DR

This study uses a Bayesian second-order random walk time-series model to estimate the impact of the COVID-19 pandemic on Santander Cycles in London from March to December 2020. It finds that cycle hire demand rebounded to pre-pandemic levels by May 2020, indicating system resilience, while average hire times increased significantly in April–June, suggesting a shift from public transit to longer cycling trips.

ABSTRACT

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March-December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

Motivation & Objective

• To assess the effect of the COVID-19 pandemic on bike-sharing demand and hire duration in London’s Santander Cycles system.
• To estimate counterfactuals for cycle hires and hire times under a no-pandemic scenario using pre-lockdown data.
• To evaluate how government policies and public transport disruptions influenced cycling behavior during the pandemic.
• To investigate whether the pandemic induced a lasting shift toward active mobility, particularly cycling.
• To inform urban planning by assessing the resilience of bike-sharing systems during public health crises.

Proposed method

• Employed a Bayesian second-order random walk time-series model to account for temporal correlation in daily cycle hire and hire duration data.
• Calibrated the model on pre-pandemic data (July 2010 – February 2020) to generate counterfactual predictions for March–December 2020.
• Used leave-one-year-out cross-validation to validate model performance and ensure robustness.
• Compared observed post-lockdown data with 95% credible intervals of counterfactual predictions to identify statistically significant deviations.
• Incorporated weather variables (temperature, rainfall, wind speed, humidity) as covariates to control for environmental influences.
• Applied posterior predictive checks to assess model fit and uncertainty propagation across all model layers.

Experimental results

Research questions

• RQ1 How did the COVID-19 pandemic affect the number of cycle hires in London’s Santander Cycles system between March and December 2020?
• RQ2 Did the average hire time (trip duration) of Santander Cycles change significantly during the pandemic, and if so, when and by how much?
• RQ3 To what extent did the pandemic-induced shift from public transit to cycling alter travel behavior in London?
• RQ4 Was the London bike-sharing system resilient to pandemic-related disruptions, as indicated by post-lockdown demand patterns?
• RQ5 How did government policies and public transport disruptions correlate with changes in bike-sharing usage and trip duration?

Key findings

• Cycle hire numbers showed statistically significant reductions in March and April 2020 compared to the counterfactual, but rebounded to expected levels from May 2020 onward.
• The average hire time increased by 16.48 minutes (95% credible interval [12.97, 19.18]) in April 2020, indicating a substantial shift toward longer trips.
• Hire time remained significantly elevated in May (excess of 13.72 minutes, CI [9.88, 17.42]) and June (excess of 10.11 minutes, CI [5.86, 13.95]) 2020.
• By July 2020, the excess hire time decreased to 3.96 minutes (CI [-0.72, 8.25]), with uncertainty including zero, suggesting a return toward baseline.
• A second national lockdown in November 2020 led to a renewed but smaller increase in hire time, with an excess of 3.96 minutes (CI [-0.72, 8.25]).
• The findings suggest that the London bike-sharing system demonstrated resilience, with no statistically significant decline in demand from May to December 2020.