Exploring spatial variation of bike sharing trip production and attraction: A study based on Chicago's Divvy system. (February 2020)

• Record Type: Journal Article
• Authors: Yang, Hongtai; Zhang, Yibei; Zhong, Lizhi; Zhang, Xiaojia; Ling, Ziwen
• Is Part Of: Applied geography. Volume 115(2020)
• Publication Date: 2020-02
• Subjects: Public bike -- Built environment -- Direct modeling -- Land use -- Geographically weighted regression -- Transit; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2019.102130
• Languages: English
• ISSNs: 0143-6228

Abstract

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. Highlights: S-GWR model is used to explore the spatially varying relationship between bike sharing ridership and influencing variables. Trip production, trip attraction of members and all trips of 24-h users are modeled separately. S-GWR model has the highest goodness-of-fit, followed by GWR model, and then OLS model. Distance to CBD is a local variable and capacity of a station is global variable in all S-GWR models. Number of employments is only significantly associated with trip attraction.