Exploring the spatiotemporal factors affecting bicycle-sharing demand during the COVID-19 pandemic

Sanjana Hossain (), Patrick Loa (), Felita Ong () and Khandker Nurul Habib ()
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Sanjana Hossain: University of Toronto
Patrick Loa: University of Toronto
Felita Ong: University of Toronto
Khandker Nurul Habib: University of Toronto

Transportation, 2024, vol. 51, issue 5, No 1, 1575-1610

Abstract: Abstract This study investigates the roles of the socio-economic, land use, built environment, and weather factors in shaping up the demand for bicycle-sharing trips during the COVID-19 pandemic in Toronto. It uses “Bike Share Toronto” ridership data of 2019 and 2020 and a two-stage methodology. First, multilevel modelling is used to analyze how the factors affect monthly station-level trip generation during the pandemic compared to pre-pandemic period. Then, a geographically weighted regression analysis is performed to better understand how the relationships vary by communities and regions. The study results indicate that the demand of the service for commuting decreased, and the demand for recreational and maintenance trips increased significantly during the pandemic. In addition, higher-income neighborhoods are found to generate fewer weekday trips, whereas neighbourhoods with more immigrants experienced an increase in bike-share ridership during the pandemic. Moreover, the pandemic trip generation rates are more sensitive to the availability of bicycle facilities within station buffers than pre-pandemic rates. The results also suggest significant spatial heterogeneity in terms of the level of influence of the explanatory factors on the demand for bicycle-sharing during the pandemic. Based on the findings, some neighbourhood-specific policy recommendations are made, which inform decisions regarding the locations and capacity of new stations and the management of existing stations so that equity concerns about the usage of the system are adequately accounted for.

Keywords: Bicycle-sharing demand; COVID-19 pandemic; Spatiotemporal factors; Multilevel modelling; Geographically weighted regression (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11116-023-10378-0

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