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Authors: Nuno Oliveira ; Maricica Nistor and André Dias

Affiliation: CEiiA // Centre of Engineering and Product Development, Av. D. Afonso Henriques, 1825, 4450-017 Matosinhos and Portugal

Keyword(s): Bike Sharing Systems, Prediction Models, Relocation Operation, Small Systems and Cities.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Knowledge-Based Systems ; Symbolic Systems

Abstract: Bike sharing systems offer a convenient, ecologic, and economic transport mode that has been increasingly adopted. However, the distribution of bikes is often unbalanced, which decreases user satisfaction and potential revenues. Moreover, bike sharing literature is mostly focused on the prediction of demand on large scale systems and uses simulations for the assessment of relocation operations to increase the number of utilizations. We propose prediction models based on machine learning approaches to improve the bike sharing re-balancing in a small city of Portugal. The algorithm aims to improve three metrics, namely (1) increase the number of utilizations, (2) reduce the number of stations without bikes, (3) reduce the time without available bikes in the stations. The relocation operations are validated using real data. Our findings show that (a) the estimated number of utilizations created by this system is substantially higher than the current system by 223%, (b) our model allows the correct identification of more 70%, 165%, 249% empty stations with the same or substantially higher precision than the existing approach, (c) the total time of bike unavailability reduced by the predictive model is 283% higher than the time reduced by current approach (1,394,454 vs 363,971 minutes). (More)

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Paper citation in several formats:
Oliveira, N.; Nistor, M. and Dias, A. (2019). Prediction of Bike Mobility in Cascais’s Sharing System. In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-374-2; ISSN 2184-495X, SciTePress, pages 181-192. DOI: 10.5220/0007724401810192

@conference{vehits19,
author={Nuno Oliveira. and Maricica Nistor. and André Dias.},
title={Prediction of Bike Mobility in Cascais’s Sharing System},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2019},
pages={181-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007724401810192},
isbn={978-989-758-374-2},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Prediction of Bike Mobility in Cascais’s Sharing System
SN - 978-989-758-374-2
IS - 2184-495X
AU - Oliveira, N.
AU - Nistor, M.
AU - Dias, A.
PY - 2019
SP - 181
EP - 192
DO - 10.5220/0007724401810192
PB - SciTePress