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2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) (2018)
Volos, Greece
Nov 5, 2018 to Nov 7, 2018
ISSN: 2375-0197
ISBN: 978-1-5386-7449-9
pp: 854-858
ABSTRACT
This article presents the first step of a project focusing on enhancing the management of bike-sharing systems. The objective of the project is to optimize the daily rebalancing operations that need to be performed by operators of bike-sharing systems using machine-learning algorithms and constraint programming. This study presents an evaluation of machine learning algorithms developed for forecasting the availability of bikes on three Swiss bike-sharing networks. The results demonstrate the superiority of the Multi-Layer Perceptron algorithm for forecasting available bikes at station-level for different prediction horizons and its applicability for real-time prediction generation.
INDEX TERMS
bicycles, learning (artificial intelligence), multilayer perceptrons, optimisation, public transport, traffic engineering computing
CITATION

S. Ruffieux, E. Mugellini and O. Abou Khaled, "Bike Usage Forecasting for Optimal Rebalancing Operations in Bike-Sharing Systems," 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, 2019, pp. 854-858.
doi:10.1109/ICTAI.2018.00133
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