Document Type


Publication Date




Publication Title

Journal of Advanced Transportation




7772401 (1-15)


Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study explores spatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factors (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic, thus adjusting station locations, capacity, and rebalancing schemes accordingly.


Copyright © 2022 Hongtai Yang et al.

This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


0000-0003-4054-2376 (Zhai)

Original Publication Citation

Yang, H., Guo, Z., Zhai, G., Yang, L., & Huo, J. (2022). Exploring the spatially heterogeneous effects of the built environment on bike-sharing usage during the COVID-19 pandemic. Journal of Advanced Transportation, 2022, 1-15, Article 7772401.