A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Deep embedding approach to classify purpose of trips between cities from GPS data
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). ; 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. ; by May Alhazzani. ; S.M. ; S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences
Deep embedding approach to classify purpose of trips between cities from GPS data
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). ; 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. ; by May Alhazzani. ; S.M. ; S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences
Deep embedding approach to classify purpose of trips between cities from GPS data
2019-01-01
1142235684
Theses
Electronic Resource
English
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