Riding the wave: Predicting the use of the bike-sharing system in Barcelona before and during COVID-19

https://doi.org/10.1016/j.scs.2022.103929Get rights and content
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Highlights

  • Not all factors influencing BSS are relevant to predict their use.

  • The global pandemic has generally reduced the relevance of the effect of factors to predict BSS ridership.

  • Increasing bike-related infrastructure, trip distance, and income are the most relevant predictors of BSS usage.

  • Tactical urbanism is effective to increase the use of BSS.

Abstract

To simultaneously promote health, economic, and environmental sustainability, a number of cities worldwide have established bike-sharing systems (BSS) that complement the conventional public transport systems. As the rapid spread of COVID-19 becoming a global pandemic disrupted urban mobility due to government-imposed lockdowns and the heightened fear of infection in crowded spaces, populations were increasingly less likely to use public transportation and instead shifted toward alternative transport systems, including BSS. In this study, we use probabilistic machine learning in a quasi-experimental research design to identify how the relevance of a comprehensive set of factors to predict the use of Bicing (the BSS in Barcelona) may have changed as COVID-19 unfolded. We unpack the key factors in predicting the use of Bicing, uncovering evidence of increasing bike-related built infrastructure (e.g., tactical urbanism), trip distance, and the income levels of neighborhoods as the most relevant predictors. Moreover, we find that the relevance of the factors in predicting Bicing usage has generally decreased during the global pandemic, suggesting altered societal behavior. Our study enhances the understanding of BSS and societal behavior, which can have important implications for developing resilient programs for cities to adopt sustainable practices through transport policy, infrastructure planning, and urban development.

Keywords

Bike-sharing system
Bicing Barcelona
COVID-19
Probabilistic machine learning
Quasi-experimental research
Sustainability
Tactical urbanism

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