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.
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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
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DOI: https://doi.org/10.1007/978-3-319-48944-5_23
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