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dc.contributor.authorHolleczek, Thomas
dc.contributor.authorYu, Liang
dc.contributor.authorLee, Joseph Kang
dc.contributor.authorSenn, Oliver
dc.contributor.authorRatti, Carlo
dc.contributor.authorJaillet, Patrick
dc.date.accessioned2016-03-11T15:23:34Z
dc.date.available2016-03-11T15:23:34Z
dc.date.issued2014-08
dc.identifier.isbn9781450328913
dc.identifier.urihttp://hdl.handle.net/1721.1/101682
dc.description.abstractMany modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed that there are 3.5 million public and 4.3 million private inter-district trips (HITS: 3.5 million and 4.4 million, respectively). Along with classifying which transportation connections are weak, the analysis shows that the mode share of public transport use increases from 38% in the morning to 44% around mid-day and 52% in the evening.en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2640087.2644164en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleDetecting weak public transport connections from cellphone and public transport dataen_US
dc.typeArticleen_US
dc.identifier.citationThomas Holleczek, Liang Yu, Joseph Kang Lee, Oliver Senn, Carlo Ratti, and Patrick Jaillet. 2014. Detecting weak public transport connections from cellphone and public transport data. In Proceedings of the 2014 International Conference on Big Data Science and Computing (BigDataScience '14). ACM, New York, NY, USA, 8 pages.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planningen_US
dc.contributor.mitauthorRatti, Carloen_US
dc.contributor.mitauthorJaillet, Patricken_US
dc.relation.journalProceedings of the 2014 International Conference on Big Data Science and Computing (BigDataScience '14)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsHolleczek, Thomas; Yu, Liang; Lee, Joseph Kang; Senn, Oliver; Ratti, Carlo; Jaillet, Patricken_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2026-5631
dc.identifier.orcidhttps://orcid.org/0000-0002-8585-6566
mit.licenseOPEN_ACCESS_POLICYen_US


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