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dc.contributor.advisorJimi Oke and Moshe E. Ben-Akiva.en_US
dc.contributor.authorGross, Eytanen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2019-03-01T19:33:22Z
dc.date.available2019-03-01T19:33:22Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/120604
dc.descriptionThesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 113-116).en_US
dc.description.abstractFuture uncertainty has always been a hindrance in the field of transportation planning. It is difficult to make robust decisions regarding optimal transportation policies when uncertainty is so wide. This project presents a novel approach for applying scenario discovery to agentbased simulation. In scenario discovery we define a space of uncertainty, and seek to find sub-spaces where strategies fail. Since scenario discovery requires running multiple simulations under different conditions of uncertainty, we can produce compelling narrative as to why certain strategies fail in the space where they do. Our two main performance measures are individual accessibility and overall petroleum-based energy consumption. We apply the Patient Rule Induction Method (PRIM), a method for clustering points within a hyper-space that fail to meet certain criteria, to both of these outputs. The strategies that were tested were: the current state; a strategy where automated mobility on-demand replaces current forms of mobility on-demand; a strategy where the frequency of all public transportation lines is doubled; a strategy where automated mobility on-demand are used only to solve the first-last mile problem for public transportation; and a strategy where all private modes are banned from entering the city's central business district (CBD). The strategy which produced the best overall performance taking into account both accessibility and energy consumption was the strategy by which the CBD was restricted. This framework of scenario discovery applied to agent-based simulation can be applied to additional modeled cities in the future.en_US
dc.description.statementofresponsibilityby Eytan Gross.en_US
dc.format.extent163 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleScenario discovery for a future of automated mobility on-demand in the urban environmenten_US
dc.typeThesisen_US
dc.description.degreeS.M. in Transportationen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.identifier.oclc1088407166en_US


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