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      Predicting Taxi–Passenger Demand Using Streaming Data

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          Is Open Access

          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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            Probability Theory

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              Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

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                Author and article information

                Journal
                IEEE Transactions on Intelligent Transportation Systems
                IEEE Trans. Intell. Transport. Syst.
                Institute of Electrical and Electronics Engineers (IEEE)
                1524-9050
                1558-0016
                September 2013
                September 2013
                : 14
                : 3
                : 1393-1402
                Article
                10.1109/TITS.2013.2262376
                f4595b3f-7756-4633-9847-85385ea565ef
                © 2013
                History

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