Abstract
In this paper we explore factors that affect Bike Sharing System (BSS) usage and how they differentiate between discrete groups of potential users. BSS have known a rampant growth during recent years, through technological advances, re-evaluated business models and reinvention of the mean’s utility. Yet, for a realized use of dockless BSS and a successful integration in the urban mobility ecosystem to be achieved, the factors that promote willingness to use them need to be explored. By using a sample of 500 stated preference data, classification trees and random forest models are built for three groups of potential BSS users; car users, bus users and pedestrians. Among the considered factors are BSS cost gains, BSS In Vehicle Time (IVT) and Out of Vehicle Time (OVT) gains, trip frequency, purpose and duration. More specific, it was found that BSS potential, increases for short duration trips of up to 21 min for car users. Bus users and pedestrians were found to be more likely to choose a BSS option for a higher cost up to 0,60 and 0,75 euros respectively. On the other side sociodemographic characteristics such as household income, gender, education level and occupation did not found to be the dominant factors for the mode choice decision. OVT is found only to be relatively important for bus users, while the cost gains are comparatively more significant for bus users and pedestrians.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Departemen of Economic and Social Affairs United Nation: World Urbanization Prospects 2018 (2018)
Cavill, N., Foster, C., Oja, P., Martin, B.W.: An evidence-based approach to physical activity promotion and policy development in Europe: contrasting case studies. Promot. Educ. 13, 104–111 (2006). https://doi.org/10.1177/10253823060130020104
Hamilton, T.L., Wichman, C.J.: Bicycle infrastructure and traffic congestion: evidence from DC’s Capital Bikeshare. J. Environ. Econ. Manag. 87, 72–93 (2018). https://doi.org/10.1016/j.jeem.2017.03.007
DeMaio, P.: Bike-sharing: history, impacts, models of provision, and future. J. Public Transp. 12, 41–56 (2009). https://doi.org/10.5038/2375-0901.12.4.3
Shaheen, S.A., Zhang, H., Martin, E., Guzman, S.: China’s Hangzhou public bicycle: understanding early adoption and behavioral response to bikesharing. Transp. Res. Rec. J. Transp. Res. Board. 2247, 33–41 (2011). https://doi.org/10.3141/2247-05
Shaheen, S.A., Guzman, S., Zhang, H.: Bikesharing in Europe, the Americas, and Asia. Transp. Res. Rec. J. Transp. Res. Board. 2143, 159–167 (2010). https://doi.org/10.3141/2143-20
Gu, T., Kim, I., Currie, G.: To be or not to be dockless: empirical analysis of dockless bikeshare development in China. Transp. Res. Part A Policy Pract. 119, 122–147 (2019). https://doi.org/10.1016/j.tra.2018.11.007
Institute for Transportation & Development Policy: The Bikeshare Planning Guide. Institute for Transportation & Development Policy (2018)
Zarif, R., Pankratz, D., Kelman, B.: Small is Beautiful (2019). https://doi.org/10.1049/me:19900212
LDA Consulting: Capital Bikeshare 2011 Member Survey Report, Washington, DC (2012)
Buck, D., Buehler, R., Happ, P., Rawls, B., Chung, P., Borecki, N.: Are bikeshare users different from regular cyclists? Transp. Res. Rec. J. Transp. Res. Board. 2387, 112–119 (2013). https://doi.org/10.3141/2387-13
Ogilvie, F., Goodman, A.: Inequalities in usage of a public bicycle sharing scheme: socio-demographic predictors of uptake and usage of the London (UK) cycle hire scheme. Prev. Med. 55, 40–45 (2012). https://doi.org/10.1016/j.ypmed.2012.05.002
Rixey, R.A.: Station-level forecasting of bikesharing ridership. Transp. Res. Rec. J. Transp. Res. Board. 2387, 46–55 (2013). https://doi.org/10.3141/2387-06
Fuller, D., et al.: Use of a new public bicycle share program in Montreal. Canada. Am. J. Prev. Med. 41, 80–83 (2011). https://doi.org/10.1016/j.amepre.2011.03.002
Martin, E.W., Shaheen, S.A.: Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two U.S. cities. J. Transp. Geogr. 41, 315–324 (2014). https://doi.org/10.1016/j.jtrangeo.2014.06.026
Bachand-Marleau, J., Lee, B.H.Y., El-Geneidy, A.M.: Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transp. Res. Rec. J. Transp. Res. Board. 2314, 66–71 (2012). https://doi.org/10.3141/2314-09
Buck, D., Buehler, R.: Bike lanes and other determinants of capital bikeshare trips. In: 91st Transportation Research Board Annual Meeting (2012)
Cervero, R., Duncan, M.: Walking, bicycling, and urban landscapes: evidence from the San Francisco bay area. Am. J. Public Health 93, 1478–1483 (2003). https://doi.org/10.2105/AJPH.93.9.1478
Zhao, J., Deng, W., Song, Y.: Ridership and effectiveness of bikesharing: the effects of urban features and system characteristics on daily use and turnover rate of public bikes in China. Transp. Policy 35, 253–264 (2014). https://doi.org/10.1016/j.tranpol.2014.06.008
Campbell, A.A., Cherry, C.R., Ryerson, M.S., Yang, X.: Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transp. Res. Part C Emerg. Technol. 67, 399–414 (2016). https://doi.org/10.1016/j.trc.2016.03.004
Li, W., Kamargianni, M.: Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: a mode choice model and policy simulation for Taiyuan-China. Transp. Res. Part A Policy Pract. 111, 277–291 (2018). https://doi.org/10.1016/j.tra.2018.01.019
Limesurvey. https://www.limesurvey.org/. Accessed 20 July 2019
R Core Team: R: A language and environment for statistical computing (2020). https://www.r-project.org/
Wickham, H., Francois, R., Henry, L., Muller, K.: dplyr: A Grammar of Data Manipulation (2019). https://cran.r-project.org/package=dplyr
Therneau, T., Atkinson, B.: rpart: Recursive Partitioning and Regression Trees (2019). https://cran.r-project.org/package=rpart
Liaw, A., Wienar, M.: Classification and Regression by randomForest. R News 2, 18–22 (2002)
Kuhn, M.: caret: Classification and Regression Training (2019). https://cran.r-project.org/package=caret
Milborrow, S.: rpart.plot: Plot “rpart” Models: An Enhanced Version of “plot.rpart” (2019). https://cran.r-project.org/package=rpart.plot
Wickham, H.: ggplot2: Elegant Graphics for Data Analaysis. Springer, New York (2016). https://doi.org/10.1007/978-3-319-24277-4
Acknowledgements
This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-04582).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Politis, I., Fyrogenis, I., Papadopoulos, E., Nikolaidou, A., Verani, E. (2020). Understanding Willingness to Use Dockless Bike Sharing Systems Through Tree and Forest Analytics. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-58802-1_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58801-4
Online ISBN: 978-3-030-58802-1
eBook Packages: Computer ScienceComputer Science (R0)