Inferring individual daily activities from mobile phone traces: A Boston example
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Author(s)
Ratti, Carlo
Zhu, Yi
Ferreira Jr, Joseph
Diao, Mi, Ph. D. Massachusetts Institute of Technology
Date Issued
July 2016
Journal
Environment and Planning B: Planning and Design
Publisher
SAGE Publications
Citation
Diao, Mi et al. “Inferring Individual Daily Activities from Mobile Phone Traces: A Boston Example.” Environment and Planning B: Planning and Design 43, 5 (July 2016): 920–940 © 2015 The Author(s)
Version
Author's final manuscript
Abstract
Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals’ activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals’ activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new “big data” such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management. Keywords: Individual activity detection; urban sensing; mobile phone traces; travel survey
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. SENSEable City Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1177/0265813515600896
