The effects of built environment spatial variation on bike-sharing usage: A case study of Suzhou, China

In recent years, many cities have implemented bike-sharing programs (BSP) to improve the travel efficiency of short trips. Early studies have analyzed how built environment factors affected bike-sharing usage. However, these studies mainly used global regression models that cannot demonstrate the spatial variation relationship between the built environment and bike usage. Therefore, this study employs both a global regression model and a geographically weighted regression (GWR) model to examine the global and local influences of the built environment on bike usage, which represents the average bike trips on workdays and non-workdays. This research takes Suzhou, China as a case study area. It uses one-year bike-sharing trip data, metro ridership data, cycling infrastructure data, cellular signaling data, and points of interest (POI) data. The global regression results show that bike stations near public transit, restaurants, shopping malls, and educational and financial places have high numbers of bike trips on both workdays and non-workdays; however, bike station proximity to workplaces is positively associated with bike trips on workdays but not on non-workdays. The results of GWR are partially consistent with the global regression results and show the local effects of the built environment on bike usage in different parts of Suzhou. Also, the goodness of fit in the GWR is better than that of the global regression model. The findings of this study provide strategic guidance to improve the service quality of bike-sharing systems as there is a pressing need to integrate BSP policies into the land use planning framework to encourage more diverse transport modal change and incentivize more commuting.

Automated summary

This study examines both global and local influences of the built environment on bike-sharing usage and finds that the location of bike stations and surrounding facilities are associated with the number of bike trips on workdays and non-workdays. The Geographically Weighted Regression (GWR) model has a better goodness of fit in showing the local effects of the built environment on bike usage in different parts of Suzhou.