A review of urban computing for mobile phone traces
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Frazzoli_A review of urban.pdf
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Author(s)
Jiang, Shan
Fiore, Gaston A.
Yang, Yingxiang
Ferreira, Joseph, Jr.
Frazzoli, Emilio
Gonzalez, Marta C.
Date Issued
August 2013
Journal
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp '13
Publisher
Association for Computing Machinery
Citation
Jiang, Shan, Gaston A. Fiore, Yingxiang Yang, et al. 2013A Review of Urban Computing for Mobile Phone Traces: Current Methods, Challenges and Opportunities. In Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp '13, August 11–14, 2013, Chicago, Illinois, USA. p. 1-9. ACM Press.
Version
Author's final manuscript
Abstract
In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1145/2505821.2505828
