Deep embedding approach to classify purpose of trips between cities from GPS data
Name
1142235684-MIT.pdf
Size
9.04 MB
Format
Adobe PDF
Checksum (MD5)
fb29ed8c44a34621df04ab1f776cd417
Author(s)
Alhazzani, May,S.M.Massachusetts Institute of Technology.
Advisor(s)
Iyad Rahwan.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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.
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).
Subjects
Program in Media Arts and Sciences
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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