Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China

Abstract

Many countries have implemented public bike systems to promote sustainable public transportation. Despite the rapid development of such systems, few studies have investigated how built environment factors affect the use of public bikes at station level using trip data, taking account of the spatial correlation between nearby stations. Built environment factors are strongly associated with travel demand and play an important role in the success of public bike systems. Using trip data from Zhongshan's public bike system, this paper employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations. It also considered the spatial correlations of PBS usage between nearby stations, using the spatial weighted matrix. These built environment variables mainly refer to station attributes and accessibility, cycling infrastructure, public transport facilities, and land use characteristics. Generally, we found that both trip demand and the ratio of demand to supply at bike stations were positively influenced by population density, length of bike lanes and branch roads, and diverse land-use types near the station, and were negatively influenced by the distance to city center and the number of other nearby stations. However, public transport facilities do not show a significant impact on both demand and D/S at stations, which might be attributed to local modal split. We also found that the PBS usage at stations is positively associated with usage at nearby stations. Model results also suggest that adding a new station (with empty capacity) within a 300 m catchment of a station to share the capacity of the bike station can improve the demand-supply ratio at the station. Referring to both trip demand models and D/S models, regression fits were quite strong with larger R 2 for weekdays than for weekends and holidays, and for morning and evening peak hours than for off-peak hours. These quantitative analyses and findings can be beneficial to urban planners and operators to improve the demand and turnover of public bikes at bike stations, and to expand or build public bike systems in the future.

Environmental benefits of bike sharing: A big data-based analysis

“…Some researchers have a particular interest in understanding the factors that affect bike sharing (such as the built environment, weather, and socio-economic demographics) (Bachand-Marleau et al, 2012;El-Assi et al, 2017;Faghih-Imani et al, 2014). Most of the existing studies focus on the analysis of an individual city, such as London (the UK) (Ogilvie and Goodman, 2012;Pfrommer et al, 2014;Wood et al, 2011), Washington DC (the United States) (Buck et al, 2013;Buehler, 2012), Toronto and Montreal (Canada) (Fuller et al, 2011;Habib et al, 2014), and Hangzhou and Zhongshan (China) (Chen et al, 2017;Shaheen et al, 2011;Zhang et al, 2017).…”

Electric fence planning for dockless bike-sharing services

“…Wood et al (2011) Researchers may have a particular interest in understanding the factors that affect bike sharing (such as the built environment, weather, and socioeconomic demographics) (Bachand-Marleau et al, 2012;El-Assi et al, 2017;Faghih-Imani et al, 2014). Most previous studies have focused on the analysis of an individual city, such as London (the United Kingdom) (Ogilvie and Goodman, 2012;Pfrommer et al, 2014;Wood et al, 2011), Washington, D.C. and Seattle (the United States) (Buck et al, 2013;Buehler, 2012;Rixey, 2013), Toronto and Montreal (Canada) (Fuller et al, 2011;Habib et al, 2014), and Hangzhou and Zhongshan (China) (Chen et al, 2017;Shaheen et al, 2011;Zhang et al, 2017), Singapore (Shen et al, 2018), Hanover (Germany) (Campbell et al, 2008). However, studies have also performed comparative analyses of the bike-sharing systems in different cities.…”

An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis

“…Based on the location of the bicycle rent/return station, we added its nearby metro station in the 300 m buffer to the data frame. A buffer of 300 m radius was used as the walkable distance for public bicycle trips [32], because the planning standard promulgated by Nanjing government has suggested the walkable distance for community public facility is 300 m (5-min walk) [33].…”