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Article: Probabilistic assessment of transport network vulnerability with equilibrium flows

TitleProbabilistic assessment of transport network vulnerability with equilibrium flows
Authors
KeywordsBi-level optimization
clonal selection algorithm
genetic algorithm
transport network vulnerability
Issue Date2021
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/15568318.asp
Citation
International Journal of Sustainable Transportation, 2021, v. 15 n. 7, p. 512-523 How to Cite?
AbstractThis article develops a probabilistic approach for assessing transport network vulnerability. A novel performance measure is proposed to evaluate the expected impact when multiple transport network components fail simultaneously at various degrees. The proposed measure captures both the likelihood and consequence of a combination of transport network component failures. The most critical combination of transport network component failures is obtained by solving a bi-level optimization problem. The upper-level problem is to solve for the combination of transport network components together with their corresponding disruption levels, which induces the maximum reduction in the performance measure. The lower-level problem is to capture the response of travelers to network changes due to network component failures and is formulated as a traffic assignment problem. The clonal selection algorithm (CSA), a biologically inspired approach, is adopted to tackle the proposed bi-level optimization problem. Numerical results indicate that neglecting partial capacity degradation and its probability of occurrence could misestimate the worst scenario, and different vulnerability assessment approaches could identify similar critical components but our approach can discover some components that are not found by other existing approaches. Moreover, it is shown that the CSA outperforms the well-known genetic algorithm in terms of solution quality in a large network.
Persistent Identifierhttp://hdl.handle.net/10722/289660
ISSN
2023 Impact Factor: 3.1
2023 SCImago Journal Rankings: 1.222
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, Y-
dc.contributor.authorWang, Y-
dc.contributor.authorSzeto, WY-
dc.contributor.authorChow, AHF-
dc.contributor.authorNagurney, A-
dc.date.accessioned2020-10-22T08:15:40Z-
dc.date.available2020-10-22T08:15:40Z-
dc.date.issued2021-
dc.identifier.citationInternational Journal of Sustainable Transportation, 2021, v. 15 n. 7, p. 512-523-
dc.identifier.issn1556-8318-
dc.identifier.urihttp://hdl.handle.net/10722/289660-
dc.description.abstractThis article develops a probabilistic approach for assessing transport network vulnerability. A novel performance measure is proposed to evaluate the expected impact when multiple transport network components fail simultaneously at various degrees. The proposed measure captures both the likelihood and consequence of a combination of transport network component failures. The most critical combination of transport network component failures is obtained by solving a bi-level optimization problem. The upper-level problem is to solve for the combination of transport network components together with their corresponding disruption levels, which induces the maximum reduction in the performance measure. The lower-level problem is to capture the response of travelers to network changes due to network component failures and is formulated as a traffic assignment problem. The clonal selection algorithm (CSA), a biologically inspired approach, is adopted to tackle the proposed bi-level optimization problem. Numerical results indicate that neglecting partial capacity degradation and its probability of occurrence could misestimate the worst scenario, and different vulnerability assessment approaches could identify similar critical components but our approach can discover some components that are not found by other existing approaches. Moreover, it is shown that the CSA outperforms the well-known genetic algorithm in terms of solution quality in a large network.-
dc.languageeng-
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/15568318.asp-
dc.relation.ispartofInternational Journal of Sustainable Transportation-
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Sustainable Transportation on 12 Jun 2020, available online: http://www.tandfonline.com/10.1080/15568318.2020.1770904-
dc.subjectBi-level optimization-
dc.subjectclonal selection algorithm-
dc.subjectgenetic algorithm-
dc.subjecttransport network vulnerability-
dc.titleProbabilistic assessment of transport network vulnerability with equilibrium flows-
dc.typeArticle-
dc.identifier.emailSzeto, WY: ceszeto@hku.hk-
dc.identifier.authoritySzeto, WY=rp01377-
dc.description.naturepostprint-
dc.identifier.doi10.1080/15568318.2020.1770904-
dc.identifier.scopuseid_2-s2.0-85087121167-
dc.identifier.hkuros316473-
dc.identifier.volume15-
dc.identifier.issue7-
dc.identifier.spage512-
dc.identifier.epage523-
dc.identifier.isiWOS:000545158500001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1556-8318-

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