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dc.contributor.authorPeng, Chengbin
dc.contributor.authorJin, Xiaogang
dc.contributor.authorWong, Ka-Chun
dc.contributor.authorShi, Meixia
dc.contributor.authorLiò, Pietro
dc.date.accessioned2014-08-27T09:46:00Z
dc.date.available2014-08-27T09:46:00Z
dc.date.issued2012-04-18
dc.identifier.citationPeng C, Jin X, Wong K-C, Shi M, Liò P (2012) Collective Human Mobility Pattern from Taxi Trips in Urban Area. PLoS ONE 7: e34487. doi:10.1371/journal.pone.0034487.
dc.identifier.issn19326203
dc.identifier.pmid22529917
dc.identifier.doi10.1371/journal.pone.0034487
dc.identifier.urihttp://hdl.handle.net/10754/325304
dc.description.abstractWe analyze the passengers' traffic pattern for 1.58 million taxi trips of Shanghai, China. By employing the non-negative matrix factorization and optimization methods, we find that, people travel on workdays mainly for three purposes: commuting between home and workplace, traveling from workplace to workplace, and others such as leisure activities. Therefore, traffic flow in one area or between any pair of locations can be approximated by a linear combination of three basis flows, corresponding to the three purposes respectively. We name the coefficients in the linear combination as traffic powers, each of which indicates the strength of each basis flow. The traffic powers on different days are typically different even for the same location, due to the uncertainty of the human motion. Therefore, we provide a probability distribution function for the relative deviation of the traffic power. This distribution function is in terms of a series of functions for normalized binomial distributions. It can be well explained by statistical theories and is verified by empirical data. These findings are applicable in predicting the road traffic, tracing the traffic pattern and diagnosing the traffic related abnormal events. These results can also be used to infer land uses of urban area quite parsimoniously. 2012 Peng et al.
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.rightsPeng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rightsArchived with thanks to PLoS ONE
dc.subjectChina
dc.subjecthome
dc.subjectland use
dc.subjectleisure
dc.subjectmotor vehicle
dc.subjectprediction
dc.subjectprobability
dc.subjectstatistical analysis
dc.subjectstatistical distribution
dc.subjecttraffic
dc.subjecttravel
dc.subjecturban area
dc.subjectworkplace
dc.subjectAlgorithms
dc.subjectChina
dc.subjectModels, Statistical
dc.subjectTransportation
dc.subjectUrban Population
dc.titleCollective human mobility pattern from taxi trips in urban area
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalPLoS ONE
dc.identifier.pmcidPMC3329492
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInstitute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou, China
dc.contributor.institutionCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
dc.contributor.institutionComputer Laboratory, Cambridge University, Cambridge, United Kingdom
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personPeng, Chengbin
kaust.personWong, Ka Chun
refterms.dateFOA2018-06-13T14:51:53Z


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