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    Collective human mobility pattern from taxi trips in urban area

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    Type
    Article
    Authors
    Peng, Chengbin cc
    Jin, Xiaogang
    Wong, Ka Chun
    Shi, Meixia cc
    Liò, Pietro
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2012-04-18
    Permanent link to this record
    http://hdl.handle.net/10754/325304
    
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    Abstract
    We 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.
    Citation
    Peng 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.
    Publisher
    Public Library of Science (PLoS)
    Journal
    PLoS ONE
    DOI
    10.1371/journal.pone.0034487
    PubMed ID
    22529917
    PubMed Central ID
    PMC3329492
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0034487
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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