Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103040
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorBababeik, Men_US
dc.creatorKhademi, Nen_US
dc.creatorChen, Aen_US
dc.creatorNasiri, MMen_US
dc.date.accessioned2023-11-27T06:04:01Z-
dc.date.available2023-11-27T06:04:01Z-
dc.identifier.urihttp://hdl.handle.net/10397/103040-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2017 The Authors. Published by Elsevier B.V.en_US
dc.rightsThis is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.rightsThe following publication Bababeik, M., Khademi, N., Chen, A., & Nasiri, M. M. (2017). Vulnerability analysis of railway networks in case of multi-link blockage. Transportation Research Procedia, 22, 275-284 is available at https://doi.org/10.1016/j.trpro.2017.03.034.en_US
dc.subjectCombinatorial optimizationen_US
dc.subjectRailway networken_US
dc.subjectSchedulingen_US
dc.subjectVulnerabilityen_US
dc.titleVulnerability analysis of railway networks in case of multi-link blockageen_US
dc.typeConference Paperen_US
dc.identifier.spage275en_US
dc.identifier.epage284en_US
dc.identifier.volume22en_US
dc.identifier.doi10.1016/j.trpro.2017.03.034en_US
dcterms.abstractIn this paper, we propose a methodology to analyze the most critical links of a railway network based on flow interdiction. Our strategy for network interdiction is to maximize network disruption by removing the links with the greatest impact to the system. For this purpose, we first introduce our primary model to determine vulnerable links based on routing costs, which are based on the minimum cost model. Next, we propose a heuristic approach to solve this model with partial enumeration of network components to assess the most vulnerable parts. Since an important factor in system vulnerability is flow, we introduce the time-space network flow model as the second model to simulate train flow in the network. After interdicting critical links in the railway network, the trains are scheduled in the residual network with considerations of various factors including customer demand, track and station capacities, and time planning horizon. The paper includes a computational instance which has been analyzed by the proposed models under various disruption scenarios, and the results are compared with full enumeration of network components using a network scan method. The accuracy of obtained results indicates the effectiveness of the proposed method in addition to fast computational time compared to the enumeration method.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research procedia, 2017, v. 22, p. 275-284en_US
dcterms.isPartOfTransportation research procediaen_US
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85019441741-
dc.identifier.eissn2352-1465en_US
dc.description.validate202311 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Others-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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