{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119524"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119524","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Mobility of the future : typologizing global cities for the simulation of future urban mobility patterns and energy scenarios","abstract":"The MITEI-sponsored Mobility of the Future project sets out to create a viable framework for analyses and predictions of urban transportation behavior in response to inevitable changes such as improved vehicle technologies, emergence of novel transit services, and policy changes motivated by population growth and emission control. In order to feasibly simulate these scenarios on a global scale, we need to first determine a few prototypical cities that best represent the entire world, each exhibiting qualities that encompass the group to which it belongs. Our methodology for accomplishing this is centered around machine learning. After collecting and pruning relevant, up-to-date data, we perform dimension reduction and clustering to ultimately generate appropriate prototype cities. These cities will be used as test beds for future mobility scenario exploration and analyses.","abstract_html":"The MITEI-sponsored Mobility of the Future project sets out to create a viable framework for analyses and predictions of urban transportation behavior in response to inevitable changes such as improved vehicle technologies, emergence of novel transit services, and policy changes motivated by population growth and emission control. In order to feasibly simulate these scenarios on a global scale, we need to first determine a few prototypical cities that best represent the entire world, each exhibiting qualities that encompass the group to which it belongs. Our methodology for accomplishing this is centered around machine learning. After collecting and pruning relevant, up-to-date data, we perform dimension reduction and clustering to ultimately generate appropriate prototype cities. These cities will be used as test beds for future mobility scenario exploration and analyses.","abstract_has_math":false,"creators":["Hua, Sean (Sean X.)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Moshe Ben-Akiva."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:58Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 67-69)."]},{"key":"dc:description.abstract","label":"Abstract","values":["The MITEI-sponsored Mobility of the Future project sets out to create a viable framework for analyses and predictions of urban transportation behavior in response to inevitable changes such as improved vehicle technologies, emergence of novel transit services, and policy changes motivated by population growth and emission control. In order to feasibly simulate these scenarios on a global scale, we need to first determine a few prototypical cities that best represent the entire world, each exhibiting qualities that encompass the group to which it belongs. Our methodology for accomplishing this is centered around machine learning. After collecting and pruning relevant, up-to-date data, we perform dimension reduction and clustering to ultimately generate appropriate prototype cities. These cities will be used as test beds for future mobility scenario exploration and analyses."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Mobility of the future : typologizing global cities for the simulation of future urban mobility patterns and energy scenarios"]}]}],"canonical_facts":{"dc:contributor.advisor":["Moshe Ben-Akiva."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Hua, Sean (Sean X.)"],"dc:date.accessioned":["2018-12-11T20:38:43Z"],"dc:date.available":["2018-12-11T20:38:43Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 67-69)."],"dc:description.abstract":["The MITEI-sponsored Mobility of the Future project sets out to create a viable framework for analyses and predictions of urban transportation behavior in response to inevitable changes such as improved vehicle technologies, emergence of novel transit services, and policy changes motivated by population growth and emission control. In order to feasibly simulate these scenarios on a global scale, we need to first determine a few prototypical cities that best represent the entire world, each exhibiting qualities that encompass the group to which it belongs. Our methodology for accomplishing this is centered around machine learning. After collecting and pruning relevant, up-to-date data, we perform dimension reduction and clustering to ultimately generate appropriate prototype cities. These cities will be used as test beds for future mobility scenario exploration and analyses."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/119524"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT 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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Mobility of the future : typologizing global cities for the simulation of future urban mobility patterns and energy scenarios"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:58Z"}