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Article: A modeling framework for the dynamic management of free-floating bike-sharing systems

TitleA modeling framework for the dynamic management of free-floating bike-sharing systems
Authors
KeywordsFree-floating bike sharing systems
Spatio-temporal clustering
Non-linear autoregressive neural network forecasting
Decision Support System
Dynamic fleet relocation
Issue Date2018
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trc
Citation
Transportation Research Part C: Emerging Technologies, 2018, v. 87, p. 159-182 How to Cite?
AbstractGiven 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.
Persistent Identifierhttp://hdl.handle.net/10722/259229
ISSN
2023 Impact Factor: 7.6
2023 SCImago Journal Rankings: 2.860
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCaggiani, L-
dc.contributor.authorCamporeale, R-
dc.contributor.authorOttomanelli, M-
dc.contributor.authorSzeto, WY-
dc.date.accessioned2018-09-03T04:03:29Z-
dc.date.available2018-09-03T04:03:29Z-
dc.date.issued2018-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2018, v. 87, p. 159-182-
dc.identifier.issn0968-090X-
dc.identifier.urihttp://hdl.handle.net/10722/259229-
dc.description.abstractGiven 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.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trc-
dc.relation.ispartofTransportation Research Part C: Emerging Technologies-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectFree-floating bike sharing systems-
dc.subjectSpatio-temporal clustering-
dc.subjectNon-linear autoregressive neural network forecasting-
dc.subjectDecision Support System-
dc.subjectDynamic fleet relocation-
dc.titleA modeling framework for the dynamic management of free-floating bike-sharing systems-
dc.typeArticle-
dc.identifier.emailSzeto, WY: ceszeto@hku.hk-
dc.identifier.authoritySzeto, WY=rp01377-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.trc.2018.01.001-
dc.identifier.scopuseid_2-s2.0-85044115523-
dc.identifier.hkuros289317-
dc.identifier.volume87-
dc.identifier.spage159-
dc.identifier.epage182-
dc.identifier.isiWOS:000428496300011-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0968-090X-

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