Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: A case study in Nanjing, China

Bike-sharing systems have rapidly expanded around the world. Previous studies found that docked and dockless bike-sharing systems are different in terms of user demand and travel characteristics. However, their usage regularity and its determinants have not been fully understood. This research aims to fill this gap by exploring smart card data of a docked bike-sharing scheme and GPS trajectory data of a dockless bike-sharing scheme in Nanjing, China, over the same period. Both docked and dockless bike-sharing users can be classified into regular users and occasional users according to their usage frequency. Two systems are cross-compared regarding their travel characteristics. Then, binary logistic models are applied to reveal the impacts of travel characteristics and built environment factors on the regularity of bike-sharing usage. Results show that for both bike-sharing systems, regular users and occasional users share similar riding time and distance, while significant differences in the spatio-temporal distribution between docked and dockless bike-sharing systems are observed. The regression model results show that the “Trips during morning and afternoon peak hours” are positively associated with the regularity of both docked and dockless bike-sharing usage. However, the “Riding distance” variable is negatively associated with the usage regularity of both systems. Built environment factors including working point of interest (POI), residential POI, and transit POI promote the usage regularity of both bike-sharing systems. Finally, policy implications are proposed, such as increasing the density of docking stations in suburban areas and developing high-quality parking area for dockless bike-sharing around public transport stations. This study can help operators or governments to launch or improve the service of bike-sharing systems.

[1]  Candace Brakewood,et al.  Sharing riders: How bikesharing impacts bus ridership in New York City , 2017 .

[2]  Yang Tang,et al.  Research on Users’ Frequency of Ride in Shanghai Minhang Bike-sharing System , 2017 .

[3]  Xuesong Wang,et al.  Classifying Road Network Patterns Using Multinomial Logit Model , 2015 .

[4]  F. ogilvie,et al.  Inequalities in usage of a public bicycle sharing scheme: socio-demographic predictors of uptake and usage of the London (UK) cycle hire scheme. , 2012, Preventive medicine.

[5]  Yingling Fan,et al.  Public bicycle as a feeder mode to rail transit in China: The role of gender, age, income, trip purpose, and bicycle theft experience , 2017 .

[6]  Robert B. Noland,et al.  Bikeshare Trip Generation in New York City , 2016 .

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

[8]  William Riggs,et al.  Cargo Bikes as a Growth Area for Bicycle vs. Auto Trips: Exploring the Potential for Mode Substitution Behavior , 2016 .

[9]  Dietmar Bauer,et al.  Daily travel behavior: lessons from a week-long survey for the extraction of human mobility motifs related information , 2013, UrbComp '13.

[10]  Ben Waterson,et al.  Using automatic number plate recognition data to investigate the regularity of vehicle arrivals , 2017 .

[11]  Wei Tu,et al.  Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system , 2019, Comput. Environ. Urban Syst..

[12]  Ahmed El-Geneidy,et al.  Overcoming barriers to cycling: understanding frequency of cycling in a University setting and the factors preventing commuters from cycling on a regular basis , 2017 .

[13]  Jana A. Hirsch,et al.  Roadmap for free-floating bikeshare research and practice in North America , 2019, Transport reviews.

[14]  Meisy A. Ortega-Tong.  Classification of London's public transport users using smart card data , 2013 .

[15]  G. Currie,et al.  To be or not to be dockless: Empirical analysis of dockless bikeshare development in China , 2019, Transportation Research Part A: Policy and Practice.

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

[17]  Haojie Li,et al.  Effects of dockless bike-sharing systems on the usage of the London Cycle Hire , 2019 .

[18]  Statistical Methods in Neuropsychology: Common Procedures Made Comprehensible , 2012 .

[19]  Luis F. Miranda-Moreno,et al.  Exploring the link between the neighborhood typologies, bicycle infrastructure and commuting cycling over time and the potential impact on commuter GHG emissions , 2016 .

[20]  Xiaohu Zhang,et al.  Understanding the usage of dockless bike sharing in Singapore , 2018 .

[21]  Diao Lin,et al.  Electric fence planning for dockless bike-sharing services , 2019, Journal of Cleaner Production.

[22]  Aijun Liu,et al.  Research on the recycling of sharing bikes based on time dynamics series, individual regrets and group efficiency , 2019, Journal of Cleaner Production.

[23]  P. Zhao.  The Impact of the Built Environment on Bicycle Commuting: Evidence from Beijing , 2014 .

[24]  Ying Zhang,et al.  Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China , 2017 .

[25]  Gulsah Akar,et al.  Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City , 2019, Journal of Transport Geography.

[26]  TrépanierMartin,et al.  Are transit users loyal? Revelations from a hazard model based on smart card data , 2012 .

[27]  Naveen Eluru,et al.  Analyzing Destination Choice Preferences in Bicycle Sharing Systems: An Investigation of Chicago’s Divvy System , 2015 .

[28]  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 .

[29]  Ahmed M El-Geneidy,et al.  Who cycles more? Determining cycling frequency through a segmentation approach in Montreal, Canada , 2015 .

[30]  J. Jiao,et al.  Promoting public bike-sharing: A lesson from the unsuccessful Pronto system. , 2018, Transportation research. Part D, Transport and environment.

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

[32]  Xiaolei Ma,et al.  Mining smart card data for transit riders’ travel patterns , 2013 .

[33]  Naveen Eluru,et al.  An Empirical Analysis of Bike Sharing Usage and Rebalancing: Evidence from Barcelona and Seville , 2015 .

[34]  W. Y. Szeto,et al.  A static free-floating bike repositioning problem with multiple heterogeneous vehicles, multiple depots, and multiple visits , 2018, Transportation Research Part C: Emerging Technologies.

[35]  Charles Raux,et al.  Who are bike sharing schemes members and do they travel differently? The case of Lyon’s “Velo’v” scheme , 2017 .

[36]  K. Chatterjee,et al.  An exploration of the importance of social influence in the decision to start bicycling in England , 2014 .

[37]  Niels van Oort,et al.  Analysing the trip and user characteristics of the combined bicycle and transit mode , 2018, Research in Transportation Economics.

[38]  David Ogilvie,et al.  New walking and cycling infrastructure and modal shift in the UK: A quasi-experimental panel study , 2017, Transportation research. Part A, Policy and practice.

[39]  Miriam Ricci,et al.  Bike sharing: A review of evidence on impacts and processes of implementation and operation , 2015 .

[40]  Serge P. Hoogendoorn,et al.  Modelling multimodal transit networks integration of bus networks with walking and cycling , 2017, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[41]  So Young Sohn,et al.  An Optimization Approach for the Placement of Bicycle-sharing stations to Reduce Short Car Trips: An Application to the City of Seoul , 2017 .

[42]  M. Bierlaire,et al.  Discrete Choice Methods and their Applications to Short Term Travel Decisions , 1999 .

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

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

[45]  Q. Shen,et al.  Bike-Sharing Systems in Beijing, Shanghai, and Hangzhou and Their Impact on Travel Behavior , 2011 .

[46]  Zhibin Li,et al.  Empirical Analysis of a Mode Shift to Using Public Bicycles to Access the Suburban Metro: Survey of Nanjing, China , 2016 .

[47]  Xingle Long,et al.  Determinants of intention and behavior of low carbon commuting through bicycle-sharing in China , 2019, Journal of Cleaner Production.

[48]  Changhyun Kwon,et al.  Analyzing Mobility Patterns and Imbalance of Free Floating Bike Sharing Systems , 2018 .

[49]  Xuefeng Li,et al.  Free-Floating Bike Sharing in Jiangsu: Users’ Behaviors and Influencing Factors , 2018, Energies.

[50]  Xu Tan,et al.  Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data , 2018, Transport Policy.

[51]  Xiaohong Chen,et al.  How to Make Dockless Bikeshare Good for Cities: Curbing Oversupplied Bikes , 2019, Transportation Research Record: Journal of the Transportation Research Board.

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

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

[54]  Chao-Che Hsu,et al.  Using a hybrid method for evaluating and improving the service quality of public bike-sharing systems , 2018, Journal of Cleaner Production.

[55]  Simon Washington,et al.  Factors influencing bike share membership : an analysis of Melbourne and Brisbane , 2015 .

[56]  Jing Wang,et al.  A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha , 2018 .

[57]  E. Sardianou,et al.  Who are the eco-bicyclists? , 2015 .

[58]  B. Ostro,et al.  Health cobenefits and transportation-related reductions in greenhouse gas emissions in the San Francisco Bay area. , 2013, American journal of public health.

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

[60]  Kees Maat,et al.  The effect of work-related factors on the bicycle commute mode choice in the Netherlands , 2013 .