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dc.contributor.authorKung, Kevin S.
dc.contributor.authorGreco, Kael
dc.contributor.authorSobolevsky, Stanislav
dc.contributor.authorRatti, Carlo
dc.date.accessioned2014-09-11T18:57:49Z
dc.date.available2014-09-11T18:57:49Z
dc.date.issued2014-06
dc.date.submitted2013-11
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/89450
dc.description.abstractHome-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0096180en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePublic Library of Scienceen_US
dc.titleExploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Dataen_US
dc.typeArticleen_US
dc.identifier.citationKung, Kevin S., Kael Greco, Stanislav Sobolevsky, and Carlo Ratti. “Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data.” Edited by Jose J. Ramasco. PLoS ONE 9, no. 6 (June 16, 2014): e96180.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planningen_US
dc.contributor.departmentMassachusetts Institute of Technology. SENSEable City Laboratoryen_US
dc.contributor.mitauthorKung, Kevin S.en_US
dc.contributor.mitauthorGreco, Kaelen_US
dc.contributor.mitauthorSobolevsky, Stanislaven_US
dc.contributor.mitauthorRatti, Carloen_US
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsKung, Kevin S.; Greco, Kael; Sobolevsky, Stanislav; Ratti, Carloen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2026-5631
dc.identifier.orcidhttps://orcid.org/0000-0001-6281-0656
dc.identifier.orcidhttps://orcid.org/0000-0001-7080-9196
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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