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Article: The rebalancing of bike-sharing system under flow-type task window

TitleThe rebalancing of bike-sharing system under flow-type task window
Authors
KeywordsDynamic rebalancing
Static rebalancing
Spatial-temporal distribution learning
Bike-sharing system
Inventory threshold
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trc
Citation
Transportation Research Part C: Emerging Technologies, 2020, v. 112, p. 1-27 How to Cite?
AbstractWith the growing importance of bike-sharing systems, this paper designs a new framework to solve rebalancing problem. It contains two aspects: dynamic rebalancing within each station and static rebalancing among stations. Firstly, we give a new flow-type task window (F-window) by defining the consistency index of travelers. It is more suitable as a task window for rebalancing than time-type task window (T-window) based on three aspects analysis. Through three assumptions, the temporal-distribution learning model including task window and station storage configuration, are built to realize new dynamic rebalancing. The spatial-distribution learning method is introduced to divide management areas for static rebalancing. The empirical results show that F-window can better match the strong time-sensitive of demand fluctuation. Compared with traditional rebalancing needs hours, each rebalancing within a station can be completed within average 4 min. By setting the station storage configuration, it makes rebalancing in this paper meets the demand of 28.3 times the hourly rebalancing within one week. And the number of vehicles visiting stations has dropped below 20%.
Persistent Identifierhttp://hdl.handle.net/10722/290134
ISSN
2020 Impact Factor: 8.089
2015 SCImago Journal Rankings: 2.062
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTian, Z-
dc.contributor.authorZhou, J-
dc.contributor.authorSzeto, WY-
dc.contributor.authorTian, L-
dc.contributor.authorZhang, W-
dc.date.accessioned2020-10-22T08:22:34Z-
dc.date.available2020-10-22T08:22:34Z-
dc.date.issued2020-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2020, v. 112, p. 1-27-
dc.identifier.issn0968-090X-
dc.identifier.urihttp://hdl.handle.net/10722/290134-
dc.description.abstractWith the growing importance of bike-sharing systems, this paper designs a new framework to solve rebalancing problem. It contains two aspects: dynamic rebalancing within each station and static rebalancing among stations. Firstly, we give a new flow-type task window (F-window) by defining the consistency index of travelers. It is more suitable as a task window for rebalancing than time-type task window (T-window) based on three aspects analysis. Through three assumptions, the temporal-distribution learning model including task window and station storage configuration, are built to realize new dynamic rebalancing. The spatial-distribution learning method is introduced to divide management areas for static rebalancing. The empirical results show that F-window can better match the strong time-sensitive of demand fluctuation. Compared with traditional rebalancing needs hours, each rebalancing within a station can be completed within average 4 min. By setting the station storage configuration, it makes rebalancing in this paper meets the demand of 28.3 times the hourly rebalancing within one week. And the number of vehicles visiting stations has dropped below 20%.-
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.subjectDynamic rebalancing-
dc.subjectStatic rebalancing-
dc.subjectSpatial-temporal distribution learning-
dc.subjectBike-sharing system-
dc.subjectInventory threshold-
dc.titleThe rebalancing of bike-sharing system under flow-type task window-
dc.typeArticle-
dc.identifier.emailSzeto, WY: ceszeto@hku.hk-
dc.identifier.authoritySzeto, WY=rp01377-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.trc.2020.01.015-
dc.identifier.scopuseid_2-s2.0-85078466176-
dc.identifier.hkuros316477-
dc.identifier.volume112-
dc.identifier.spage1-
dc.identifier.epage27-
dc.identifier.isiWOS:000521511200001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0968-090X-

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