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dc.contributor.advisorMoshe E. Ben-Akiva and Tomer Toledo.en_US
dc.contributor.authorSong, Xiang, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.coverage.spatiala-si---en_US
dc.date.accessioned2013-12-06T20:49:20Z
dc.date.available2013-12-06T20:49:20Z
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/82852
dc.descriptionThesis (S.M. in Transportation)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 99-101).en_US
dc.description.abstractOne of the main challenges of making strategic decisions in transportation is that we always face a set of possible future states due to deep uncertainty in traffic demand. This thesis focuses on exploring the application of model-based decision support techniques which characterize a set of future states that represent the vulnerabilities of the proposed policy. Vulnerabilities here are interpreted as states of the world where the proposed policy fails its performance goal or deviates significantly from the optimum policy due to deep uncertainty in the future. Based on existing literature and data mining techniques, a computational model-based approach known as scenario discovery is described and applied in an empirical problem. We investigated the application of this new approach in a case study based on a proposed transit policy implemented in Marina Bay district of Singapore. Our results showed that the scenario discovery approach performs well in finding the combinations of uncertain input variables that will result in policy failure.en_US
dc.description.statementofresponsibilityby Xiang Song.en_US
dc.format.extent101 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleScenarios discovery : robust transportation policy analysis in Singapore using microscopic traffic simulatoren_US
dc.title.alternativeRobust transportation policy analysis in Singapore using microscopic traffic simulatoren_US
dc.typeThesisen_US
dc.description.degreeS.M.in Transportationen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.oclc863398869en_US


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