Abstract
Free-floating bike sharing systems are an emerging new generation of bike rentals, that eliminates the need for specific stations and allows to leave a bicycle (almost) everywhere in the network. Although free-floating bikes allow much greater spontaneity and flexibility for the user, they need additional operational challenges especially in facing the bike relocation process. Then, we suggest a methodology able to generate spatio-temporal clusters of the usage patterns of the available bikes in every zone of the city, forecast the bicycles use trend (by means of Non-linear Autoregressive Neural Networks) for each cluster, and consequently enhance and simplify the relocation process in the network.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fanaee-T, H., Gama, J.: Event labeling combining ensemble detectors and background knowledge. Progr. Artif. Intell., 1–15. Springer, Heidelberg (2013)
Pal, A., Zhang, Y.: Free-floating bike sharing: solving real-life large-scale static rebalancing problems. Technical report, University of South Florida (2015)
Zhou, X.: Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PLoS ONE 10(10), e0137922 (2015)
Xu, H., Ying, J., Wu, H., Lin, F.: Public bicycle traffic flow prediction based on a hybrid model. Appl. Math. Inf. Sci. 7, 667–674 (2013)
Lee, C., Wang, D., Wong, A.: Forecasting utilization in city bike-share program. Technical report, CS 229 2014 Project (2014)
Vogel, P., Greiser, T., Mattfeld, D.C.: Understanding bike-sharing systems using data mining: exploring activity patterns. Procedia Soc. Behav. Sci. 20, 514–523 (2011)
Côme, E., Randriamanamihaga, A., Oukhellou, L.: Spatio-temporal Usage Pattern Analysis of the Paris Shared Bicycle Scheme: A Data Mining Approach. Transport Research Arena, Paris (2014)
Wang, X., Lindsey, G., Schoner, J.E., Harrison, A.: Modeling bike share station activity: the effects of nearby business and jobs on trips to and from stations. Transp. Res. Rec. 43, 45 (2012)
Froehlich, J., Neumann J., Oliver, N.: Sensing and predicting the pulse of the city through shared bicycling. In: The International Joint Conferences on Artificial Intelligence, pp. 1420–1426 (2009)
Han, Y., Côme, E., Oukhellou, L.: Towards bicycle demand prediction of large-scale bicycle sharing system. In: Transportation Research Board 93rd Annual Meeting (2014)
Zeng, D., Xu, J., Gu, J., Liu, L., Xu, G.: Short term traffic flow prediction based on online learning SVR. In: Workshop on Power Electronics and Intelligent Transportation System, pp. 616–620 (2008)
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C 43, 3–19 (2014)
Mori, U., Mendiburu, A., Álvarez, M., Lozano, J.A.: A review of travel time estimation and forecasting for advanced traveller information systems. Transportmetrica A Transp. Sci. 11(2), 119–157 (2015)
Smith, B., Demetsky, M.: Traffic flow forecasting: comparison of modeling approaches. J. Transp. Eng. 123(4), 261–266 (1997)
Sun, Y., Leng, B., Guan, W.: A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166, 109–121 (2015)
Dougherty, M.: A review of neural networks applied to transport. Transp. Res. Part C 3(4), 247–260 (1995)
Wei, Y., Chen, M.: Forecasting the short-term metro passenger flow with empirical model decomposition and neural networks. Transp. Res. Part C 21(1), 148–162 (2012)
Sapankevych, N., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Intell. Syst. 4(2), 24–38 (2009)
Yu, H., Yang, J., Han, J., Li, X.: Making SVMs scalable to large data sets using hierarchical cluster indexing. Data Min. Knowl. Disc. 11(3), 295–321 (2005)
Zhang, Z., Zhang, P., Yin, Y., Hou, L.: Analysis on urban traffic network states evolution based on grid clustering and wavelet de-noising. In: 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, pp. 1183–1188 (2008)
Guan, L., Feng, X.: Research on factors of the signal de-noising effect basing on the wavelet transform and the matlab practice. Autom. Instrum. 6, 43–46 (2004)
Caggiani, L., Ottomanelli, M.: A dynamic simulation based model for optimal fleet repositioning in bike-sharing systems. Procedia Soc. Behav. Sci. 87, 203–210 (2013)
Caggiani, L., Ottomanelli, M.: A modular soft computing based method for vehicles repositioning in bike-sharing systems. Procedia Soc. Behav. Sci. 54, 675–684 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Caggiani, L., Ottomanelli, M., Camporeale, R., Binetti, M. (2017). Spatio-temporal Clustering and Forecasting Method for Free-Floating Bike Sharing Systems. In: Świątek, J., Tomczak, J. (eds) Advances in Systems Science. ICSS 2016. Advances in Intelligent Systems and Computing, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-48944-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-48944-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48943-8
Online ISBN: 978-3-319-48944-5
eBook Packages: EngineeringEngineering (R0)