Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/107295
DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studies-
dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorZhang, L-
dc.creatorXu, M-
dc.creatorWang, S-
dc.date.accessioned2024-06-13T01:05:51Z-
dc.date.available2024-06-13T01:05:51Z-
dc.identifier.issn2210-6707-
dc.identifier.urihttp://hdl.handle.net/10397/107295-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.subjectInterdependent cascading failuresen_US
dc.subjectPublic transiten_US
dc.subjectSustainable megacitiesen_US
dc.subjectSystem emergency capabilityen_US
dc.subjectVulnerability mitigationsen_US
dc.titleMitigating vulnerability of a multimodal public transit system for sustainable megacities : a real-time operational control methoden_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume101-
dc.identifier.doi10.1016/j.scs.2023.105142-
dcterms.abstractThe multimodal public transit system (MPTS) has been recognized as a primary mobility support system for sustainable megacities. However, it is often vulnerable to various service disruptions, and the most severe circumstance is the interdependent cascading failures interacting among urban rail transit, bus transit, and road transit networks. The vulnerability of an MPTS against this severe failure, associated with extreme performances, limits the building of future resilient megacities. In this paper, a real-time operational control method based on the system emergency capability (SEC) is developed to block the dynamic unfolding paths of this severe failure considering network topology characteristics and dynamic evolution characteristics. This method is immediately available for real-time emergency control, while previous studies on qualitative optimization strategies cannot. Remarkably, a three-stage association design process is conducted to explore the most efficient SEC loading strategy, involved with multiple intertwined influential factors, including the target loading stations, loading time-step intervals, and loading interval length and loading strength. Finally, a case simulation is undertaken to indicate the adaptability of the proposed method. This work can provide critical insights into real-world emergency resource allocation and an underlying simulator with search direction knowledge for future intelligent algorithm-based optimal control.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationSustainable cities and society, Feb. 2024, v. 101, 105142-
dcterms.isPartOfSustainable cities and society-
dcterms.issued2024-02-
dc.identifier.scopus2-s2.0-85181769981-
dc.identifier.eissn2210-6715-
dc.identifier.artn105142-
dc.description.validate202406 bcch-
dc.identifier.FolderNumbera2804aen_US
dc.identifier.SubFormID48428en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2026-02-28en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2026-02-28
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