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

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 R2 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.

[1]  L. Anselin,et al.  Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL , 2014 .

[2]  R. Noland,et al.  The impact of weather conditions on bikeshare trips in Washington, DC , 2014 .

[3]  Elin Charles‐Edwards,et al.  Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events , 2014 .

[4]  Stacey Guzman,et al.  China's Hangzhou Public Bicycle , 2011 .

[5]  Ahmed M El-Geneidy,et al.  Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use , 2012 .

[6]  Dirk C. Mattfeld,et al.  Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns , 2011 .

[7]  Christian Rudloff,et al.  Modeling Demand for Bicycle Sharing Systems - neighboring stations as a source for demand and a reason for structural breaks , 2014 .

[8]  Reid Ewing,et al.  Travel and the Built Environment: A Synthesis , 2001 .

[9]  David Daddio.  Maximizing bicycle sharing: an empirical analysis of capital bikeshare usage , 2012 .

[10]  Elise Miller-Hooks,et al.  Large-Scale Vehicle Sharing Systems: Analysis of Vélib' , 2013 .

[11]  Michael Rabbat,et al.  How Does Land-Use and Urban Form Impact Bicycle Flows--Evidence from the Bicycle-Sharing System (BIXI) in Montreal , 2014 .

[12]  Naveen Eluru,et al.  Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system , 2016 .

[13]  Ralph Buehler,et al.  Bike Lanes and Other Determinants of Capital Bikeshare Trips , 2012 .

[14]  Frank Witlox,et al.  Why people use their cars while the built environment imposes cycling , 2013 .

[15]  Inês Frade,et al.  Bicycle sharing systems demand , 2014 .

[16]  Hua Zhang,et al.  HANGZHOU PUBLIC BICYCLE: UNDERSTANDING EARLY ADOPTION AND BEHAVIORAL RESPONSE TO BIKESHARING IN HANGZHOU, CHINA , 2011 .

[17]  Simon Washington,et al.  Bike Share: A Synthesis of the Literature , 2013 .

[18]  Megan S. Ryerson,et al.  Factors influencing the choice of shared bicycles and shared electric bikes in Beijing , 2016 .

[19]  Rafael E. Banchs,et al.  Article in Press Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Urban Cycles and Mobility Patterns: Exploring and Predicting Trends in a Bicycle-based Public Transport System , 2022 .

[20]  Lindsay Kathryn Maurer.  Feasibility Study for a Bicycle Sharing Program in Sacramento, California , 2012 .

[21]  R. Alexander Rixey,et al.  Station-Level Forecasting of Bikesharing Ridership , 2013 .

[22]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[23]  Adam Astrid Kemperman,et al.  Influences of Built Environment on Walking and Cycling by Latent Segments of Aging Population , 2009 .

[24]  J. Pucher,et al.  Infrastructure, programs, and policies to increase bicycling: an international review. , 2010, Preventive medicine.

[25]  Wen Cheng,et al.  Solving the Last Mile Problem: Ensure the Success of Public Bicycle System in Beijing , 2012 .

[26]  Javier Gutiérrez,et al.  Transit ridership forecasting at station level: an approach based on distance-decay weighted regression , 2011 .

[27]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[28]  K. Krizek,et al.  Proximity to Trails and Retail: Effects on Urban Cycling and Walking , 2006 .

[29]  Céline Robardet,et al.  Shared Bicycles in a City: a Signal Processing and Data Analysis Perspective , 2011, Adv. Complex Syst..

[30]  S. Shaheen,et al.  Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future , 2010 .

[31]  Christian Rudloff,et al.  Modeling Demand for Bikesharing Systems , 2014 .

[32]  Ta-Hui Yang,et al.  Strategic design of public bicycle sharing systems with service level constraints , 2011 .

[33]  Jun Zhang,et al.  Sustainable bike-sharing systems: characteristics and commonalities across cases in urban China , 2015 .

[34]  R. Cervero,et al.  Influences of Built Environments on Walking and Cycling: Lessons from Bogotá , 2009 .

[35]  Naveen Eluru,et al.  Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system , 2015 .

[36]  Jessica Schoner,et al.  Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations , 2016, 2207.10577.

[37]  Felipe González,et al.  A combined destination and route choice model for a bicycle sharing system , 2016 .

[38]  Kristiann C Heesch,et al.  Cycling for transport and recreation: Associations with the socio-economic, natural and built environment. , 2015, Health & place.

[39]  Wafic El-Assi,et al.  Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto , 2017 .

[40]  T. Toivonen,et al.  Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach☆ , 2013 .

[41]  A. Cheadle,et al.  Cycling and the Built Environment, a US Perspective , 2005 .

[42]  Reid Ewing,et al.  Travel and the Built Environment , 2010 .