Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 58, Issue -, Pages 240-250Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2015.02.018
Keywords
Mobile phone data; Data mining; Human mobility; Trip production and attraction; Trip distribution; Travel surveys
Categories
Funding
- MIT-Accenture alliance
- BMW-MIT
- Austrian Institute-HuMNet
- Center for Complex Engineering Systems (CCES) at KACST
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In this work, we present methods to estimate average daily origin-destination trips from triangulated mobile phone records of millions of anonymized users. These records are first converted into clustered locations at which users engage in activities for an observed duration. These locations are inferred to be home, work, or other depending on observation frequency, day of week, and time of day, and represent a user's origins and destinations. Since the arrival time and duration at these locations reflect the observed (based on phone usage) rather than true arrival time and duration of a user, we probabilistically infer departure time using survey data on trips in major US cities. Trips are then constructed for each user between two consecutive observations in a day. These trips are multiplied by expansion factors based on the population of a user's home Census Tract and divided by the number of days on which we observed the user, distilling average daily trips. Aggregating individuals' daily trips by Census Tract pair, hour of the day, and trip purpose results in trip matrices that form the basis for much of the analysis and modeling that inform transportation planning and investments. The applicability of the proposed methodology is supported by validation against the temporal and spatial distributions of trips reported in local and national surveys. (C) 2015 Elsevier Ltd. All rights reserved.
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