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.

本研究调查了多伦多 COVID-19 大流行期间社会经济、土地利用、建筑环境和天气因素对自行车共享出行需求的影响。它使用 2019 年和 2020 年的“多伦多共享单车”乘客数据以及两阶段方法。首先，使用多级模型来分析与大流行前相比，这些因素如何影响大流行期间的每月车站级出行产生。然后，进行地理加权回归分析，以更好地了解这些关系如何因社区和地区而异。研究结果表明，疫情期间通勤服务需求下降，休闲、维修出行需求大幅增加。此外，高收入社区的工作日出行次数较少，而移民较多的社区在疫情期间共享单车的出行量却有所增加。此外，与大流行前的出行发生率相比，大流行出行发生率对车站缓冲区内自行车设施的可用性更为敏感。研究结果还表明，大流行期间解释因素对自行车共享需求的影响程度存在显着的空间异质性。根据调查结果，提出了一些针对特定社区的政策建议，为有关新车站的位置和容量以及现有车站的管理的决策提供信息，从而充分考虑到对系统使用的公平担忧。