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      Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

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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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            Travel and the Built Environment: A Synthesis

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              Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice

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                Author and article information

                Journal
                IEEE Transactions on Big Data
                IEEE Trans. Big Data
                Institute of Electrical and Electronics Engineers (IEEE)
                2332-7790
                June 1 2017
                June 1 2017
                : 3
                : 2
                : 208-219
                Article
                10.1109/TBDATA.2016.2631141
                028d7f15-e853-4a7f-99e5-ec3eb043250b
                © 2017
                History

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