Citation

Hossain S, Loa P, Ong F, Habib KN. Transportation (Amst) 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11116-023-10378-0

PMID

37363368

PMCID

PMC10012301

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.

Language: en

Keywords

Geographically weighted regression; COVID-19 pandemic; Bicycle-sharing demand; Multilevel modelling; Spatiotemporal factors