{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/124081"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/124081","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Deep embedding approach to classify purpose of trips between cities from GPS data","abstract":"I present a computational framework to identify purpose of trips between cities from GPS traces using a deep embedding approach. I extracted statistical features that captures trips characteristics that includes: temporal features, spatial features and Points of Interests (POI) features. I deployed a deep learning model to extract representative features in a lower dimensional space, which I then feed to a classic clustering algorithm to uncover purpose of trips. I detected six main purposes from trips coming from five different metropolitan areas in the United States to New York city. The trips' purposes detected are: work, which is the most dominating in size, entertainment, shopping, academic, and travelling. I interpret and discuss each cluster in terms of its features. I also compare cities from which trips originated by the distribution of their trips purposes.","abstract_html":"I present a computational framework to identify purpose of trips between cities from GPS traces using a deep embedding approach. I extracted statistical features that captures trips characteristics that includes: temporal features, spatial features and Points of Interests (POI) features. I deployed a deep learning model to extract representative features in a lower dimensional space, which I then feed to a classic clustering algorithm to uncover purpose of trips. I detected six main purposes from trips coming from five different metropolitan areas in the United States to New York city. The trips' purposes detected are: work, which is the most dominating in size, entertainment, shopping, academic, and travelling. I interpret and discuss each cluster in terms of its features. I also compare cities from which trips originated by the distribution of their trips purposes.","abstract_has_math":false,"creators":["Alhazzani, May,S.M.Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Program in Media Arts and Sciences (Massachusetts Institute of Technology)","school":null,"contributors":[],"advisors":["Iyad Rahwan."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:29Z","subjects":["Program in Media Arts and Sciences"],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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I extracted statistical features that captures trips characteristics that includes: temporal features, spatial features and Points of Interests (POI) features. I deployed a deep learning model to extract representative features in a lower dimensional space, which I then feed to a classic clustering algorithm to uncover purpose of trips. I detected six main purposes from trips coming from five different metropolitan areas in the United States to New York city. The trips' purposes detected are: work, which is the most dominating in size, entertainment, shopping, academic, and travelling. I interpret and discuss each cluster in terms of its features. I also compare cities from which trips originated by the distribution of their trips purposes."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Deep embedding approach to classify purpose of trips between cities from GPS data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Iyad Rahwan."],"dc:contributor.department":["Program in Media Arts and Sciences (Massachusetts Institute of Technology)","Media"],"dc:contributor.other":["Program in Media Arts and Sciences (Massachusetts Institute of Technology)"],"dc:creator":["Alhazzani, May,S.M.Massachusetts Institute of Technology."],"dc:date.accessioned":["2020-03-09T18:52:41Z"],"dc:date.available":["2020-03-09T18:52:41Z"],"dc:date.issued":["2019"],"dc:description":["Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 64-67)."],"dc:description.abstract":["I present a computational framework to identify purpose of trips between cities from GPS traces using a deep embedding approach. I extracted statistical features that captures trips characteristics that includes: temporal features, spatial features and Points of Interests (POI) features. I deployed a deep learning model to extract representative features in a lower dimensional space, which I then feed to a classic clustering algorithm to uncover purpose of trips. I detected six main purposes from trips coming from five different metropolitan areas in the United States to New York city. The trips' purposes detected are: work, which is the most dominating in size, entertainment, shopping, academic, and travelling. I interpret and discuss each cluster in terms of its features. I also compare cities from which trips originated by the distribution of their trips purposes."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/124081"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. 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