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Article: Transport network design problem under uncertainty: a review and new developments

TitleTransport network design problem under uncertainty: a review and new developments
Authors
KeywordsComputer simulation
Demand-side management
Genetic algorithm
Monte carlo analysis
Network design
Issue Date2011
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01441647.asp
Citation
Transport Reviews, 2011, v. 31 n. 6, p. 743-768 How to Cite?
AbstractThis paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP. © 2011 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/144522
ISSN
2015 Impact Factor: 2.452
2015 SCImago Journal Rankings: 1.635
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Aen_US
dc.contributor.authorZhou, Zen_US
dc.contributor.authorChootinan, Pen_US
dc.contributor.authorRyu, Sen_US
dc.contributor.authorYang, Cen_US
dc.contributor.authorWong, SCen_US
dc.date.accessioned2012-02-03T06:12:13Z-
dc.date.available2012-02-03T06:12:13Z-
dc.date.issued2011en_US
dc.identifier.citationTransport Reviews, 2011, v. 31 n. 6, p. 743-768en_US
dc.identifier.issn0144-1647-
dc.identifier.urihttp://hdl.handle.net/10722/144522-
dc.description.abstractThis paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP. © 2011 Copyright Taylor and Francis Group, LLC.-
dc.languageengen_US
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01441647.aspen_US
dc.relation.ispartofTransport Reviewsen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis Group in Transport Reviews on 17 Aug 2011, available online at: http://www.tandfonline.com/doi/abs/10.1080/01441647.2011.589539-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputer simulation-
dc.subjectDemand-side management-
dc.subjectGenetic algorithm-
dc.subjectMonte carlo analysis-
dc.subjectNetwork design-
dc.titleTransport network design problem under uncertainty: a review and new developmentsen_US
dc.typeArticleen_US
dc.identifier.emailChen, A: anthony.chen@usu.eduen_US
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191en_US
dc.description.naturepostprint-
dc.identifier.doi10.1080/01441647.2011.589539-
dc.identifier.scopuseid_2-s2.0-84863229216-
dc.identifier.hkuros198212en_US
dc.identifier.volume31en_US
dc.identifier.issue6-
dc.identifier.spage743en_US
dc.identifier.epage768en_US
dc.identifier.isiWOS:000299922300004-
dc.publisher.placeUnited Kingdom-

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