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2023 | OriginalPaper | Chapter

37. Machine Learning Approaches to Predict the Use of Share Bicycles According to Weather Conditions

Authors : Alejandro Escudero-Santana, Andrea Beltrante, Elena Barbadilla-Martín, María Rodríguez-Palero

Published in: Industry 4.0: The Power of Data

Publisher: Springer International Publishing

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Abstract

Bike sharing services are a reality that is developing more and more every day, contributing to reduce private car use. A bike sharing system is not limited to the fleet and the stations, but requires important support of internal office services, cyclical maintenance of the bikes, and their continuous redistribution. The various supporting services should be organized according to the number of circulating bicycles, and thus accurate demand previsions can provide considerable help in optimizing the costs bear by the service provider. The use of bicycles follows a cyclical pattern, but it also depends highly on the weather conditions. This work aims to adapt and apply different machine learning algorithms to predict this demand. It uses a real database, containing data on two years of bicycle rentals in London. The results obtained validate the methodology.

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Metadata
Title
Machine Learning Approaches to Predict the Use of Share Bicycles According to Weather Conditions
Authors
Alejandro Escudero-Santana
Andrea Beltrante
Elena Barbadilla-Martín
María Rodríguez-Palero
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-29382-5_37

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