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      Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

      , , ,
      Landscape and Urban Planning
      Elsevier BV

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          Understanding individual human mobility patterns

          Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.
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            Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis

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              Modeling the scaling properties of human mobility

              While the fat tailed jump size and the waiting time distributions characterizing individual human trajectories strongly suggest the relevance of the continuous time random walk (CTRW) models of human mobility, no one seriously believes that human traces are truly random. Given the importance of human mobility, from epidemic modeling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model not only accounts for the empirically observed scaling laws but also allows us to analytically predict most of the pertinent scaling exponents.
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                Author and article information

                Journal
                Landscape and Urban Planning
                Landscape and Urban Planning
                Elsevier BV
                01692046
                May 2012
                May 2012
                : 106
                : 1
                : 73-87
                Article
                10.1016/j.landurbplan.2012.02.012
                83921cb0-f416-42e3-bca2-97c9261790f8
                © 2012

                http://www.elsevier.com/tdm/userlicense/1.0/

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