Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 87, Issue -, Pages 159-182Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2018.01.001
Keywords
Free-floating bike sharing systems; Spatio-temporal clustering; Non-linear autoregressive neural network forecasting; Decision Support System; Dynamic fleet relocation
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Funding
- Fondo di Sviluppo e Coesione - APQ Ricerca Regione Puglia, Programma regionale a sostegno della specializzazione intelligente e della sostenibilita sociale ed ambientale - FutureInResearch - Italy [3T23CY7]
- National Natural Science Foundation of China - China [71771194]
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Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems.
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