Deep embedding approach to classify purpose of trips between cities from GPS data
Author(s)Alhazzani, May,S.M.Massachusetts Institute of Technology.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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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.
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 64-67).
DepartmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)
Massachusetts Institute of Technology
Program in Media Arts and Sciences