Citation

Yang H, Zhang Y, Zhong L, Zhang X, Ling Z. Appl. Geogr. 2020; 115: e102130.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.apgeog.2019.102130

PMID

unavailable

Abstract

Bike sharing systems are adopted by many cities due to its contribution to energy saving and mitigation of traffic congestion. Understanding factors that influence bike sharing ridership and accurate estimation of ridership play an important role in designing the system. Previous studies assume the relationship between predicting variables and the response variable are the same across the study area. However, this assumption may not be true, since the study area is usually wide and thus the relationship between predicting variabels and the response variable may change across space. As a result, semi-parametric geographically weighted regression (S-GWR) model is used to explore the spatially varying relationship. S-GWR is an extension of the GWR model. While in GWR model, all predicting variables are local variables with spatially varying relationship with the response variable, S-GWR model allows predicting variables to be either global or local, which is closer to reality. We also extend previous studies by differenciating members and 24-h pass users, as well as data related to trip production and trip attraction.

RESULTS show that S-GWR models fit the data better and the relationship between some predicting variables and response variable are local while other relationships are global. Ridership of both members and 24-h users are positively related to number of employed residents nearby and capacity of the station, and negatively related to distance to central business area and percent of low-income workers living nearby. Number of employments is only significantly associated with trip attraction. Among them, the variable capacity is always a global variable, with higher capacity associated with higher ridership. As a result, S-GWR model could be used to estimate the ridership of stations for accurate prediction and spatially varying relationship between ridership and influencing factors should be considered when designing bike sharing system.

Language: en

Keywords

Built environment; Direct modeling; Geographically weighted regression; Land use; Public bike; Transit