Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

Abstract

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China

“…Three clusters are obtained. The Cluster 1 includes station 4; Cluster 2 contains the station 1, 5, 6, 7, 8, 28, 29, 32, and 33; Cluster 3 includes station 3,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,30, and 31. Different clusters are marked in Figure 1.…”

“…The distance people ride varies from workdays to weekends. Ma et al [15] revealed that the average activity distance of metro-bike-sharing on weekends was smaller than that on weekdays in Nanjing, China. Moreover, the distance of using bicycles in different areas of the same city should be different, such as the city center and suburbs, because the land use characteristics and population density of different areas of the city are different.…”

“…Zhao et al [18] found that the travel distance was the most vital factor influencing the usage rate of cycling between home or workplace and metro station. Ma et al [15] constructed a spatial error model and an ordinary least squares regression to explore the effects of the exterior environment of the metro stations and revealed that the closer it is to the central business district (CBD), the smaller space there is for metro-bike-sharing activity. The development of the city center is intensive, with dense buildings and rich transportation modes, such as buses and taxis.…”

Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making

“…A few years ago, only station-based bike-sharing was available, and people must use a smart card to unlock, use, and pay for it, and the associated registration process is overly complicated. But dockless bike-sharing has gradually replaced station-based bike-sharing in numerous Chinese cities [38][39][40][41]. The implementation of smartphone payments has been the norm in today's China, substantially simplifying the previously complicated registration process.…”

Exploring the Impact of Dockless Bikeshare on Docked Bikeshare—A Case Study in London

“…As a healthy and low-cost way to travel, BS has been widely studied by researchers [3,[5][6][7][8][9][10][11][12][13]. Simultaneously, many studies have shown that DBS has a great impact on other modes.…”