Inferring modes of transportation using mobile phone data
Date
2018
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Article
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23 p.
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Abstract
Cities are growing at a fast rate, and transportation networks need to adapt
accordingly. To design, plan, and manage transportation networks, domain experts
need data that reflect how people move from one place to another, at what times, for
what purpose, and in what mode(s) of transportation. However, traditional data
collection methods are not cost-effective or timely. For instance, travel surveys are
very expensive, collected every ten years, a period of time that does not cope with
quick city changes, and using a relatively small sample of people. In this paper, we
propose an algorithmic pipeline to infer the distribution of mode of transportation
usage in a city, using mobile phone network data. Our pipeline is based on a
Topic-Supervised Non-Negative Matrix Factorization model, using a Weak-Labeling
strategy on user trajectories with data obtained from open datasets, such as GTFS and
OpenStreetMap. As a case study, we show results for the city of Santiago, Chile, which
has a sophisticated intermodal public transportation system. Importantly, our
pipeline delivers coherent results that are explainable, with interpretable parameters
at each step. Finally, we discuss the potential applications and implications of such a
system in transportation and urban planning.
Description
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Citation
EPJ Data Science, 2018, 7:49
Keywords
Mobile phone networks, Urban informatics, Commuting, Non-negative matrix factorization, Mode of transportation