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作者

Chunliang Wu;Inhi Kim;Hyungchul Chung

来源

CITIES,Vol.110,Issue1,Article 103063

语言

英文

关键字

Bike-sharing programs (BSP);Built environment;Spatial variation;Geographically weighted regression (GWR)

作者单位

Monash Institute of Transport Studies, Department of Civil Engineering, Monash University, Clayton, Victoria 3800, Australia;Department of Civil and Environmental Engineering, Kongju National University, Cheonan, Republic of Korea;Urban Planning and Design, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou Industrial Park, Suzhou 215123, China;Monash Institute of Transport Studies, Department of Civil Engineering, Monash University, Clayton, Victoria 3800, Australia;Department of Civil and Environmental Engineering, Kongju National University, Cheonan, Republic of Korea;Urban Planning and Design, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou Industrial Park, Suzhou 215123, China

摘要

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.