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Individual and Collective Human Mobility: Description, Modelling, Prediction

The widespread diffusion of personal electronic devices, like smartphones and GPS handsets, which are capable to collect detailed information about the trajectories and activities of millions of individuals with an unprecedented spatiotemporal resolution, has provided researchers with an accurate probe to quantitatively investigate complex collective phenomena arising from the interactions of many individuals, such as spreading processes, the development of urban systems, traffic congestions. The availability of these large and detailed datasets has generated a new interest for human mobility in various disciplines including applied mathematics, statistical physics, data mining and computer science, transport engineering, urban geography, social sciences and economics.

The body work produced by this interdisciplinary effort can be broadly divided into three main topics that are often intertwined: i) Description: discovery of patterns and general statistical laws in human mobility data, as well as identifying relationships between human mobility, human activity and socio-economic interactions; ii) Modelling: develop generative models of individual and collective human mobility to explain the emergent patterns; iii) Prediction: forecast and nowcast human mobility and related phenomena.

This special issue aims at collecting contributions on the recent advances in human mobility description, modelling and prediction, as well as presenting novel interdisciplinary applications.

Lead guest editor:
Filippo Simini, University of Bristol, UK

Guest editors:
Gourab Ghoshal, University of Rochester, USA
Luca Pappalardo, University of Pisa, Italy
Michael Szell, Hungarian Acad. of Sci., Hungary
Philipp Hövel, Institute of Theoretical Physics of TU Berlin, Germany

  1. Content type: Regular article

    Human mobility always had a great influence on the spreading of cultural, social and technological ideas. Developing realistic models that allow for a better understanding, prediction and control of such coupl...

    Authors: Nataša Djurdjevac Conrad, Luzie Helfmann, Johannes Zonker, Stefanie Winkelmann and Christof Schütte

    Citation: EPJ Data Science 2018 7:24

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  2. Content type: regular article

    In this paper, we follow the short-ranged Syrian refugees’ migration to Lebanon as documented by the UNHCR. We propose a model inspired by the Debye–Hückel theory and show that it properly predicts the refugee...

    Authors: Sara Najem and Ghaleb Faour

    Citation: EPJ Data Science 2018 7:22

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  3. Content type: Regular article

    As users of mobile devices make phone calls, browse the web, or use an app, large volumes of data are routinely generated that are a potentially useful source for investigating human behavior in space. However...

    Authors: Luca Scherrer, Martin Tomko, Peter Ranacher and Robert Weibel

    Citation: EPJ Data Science 2018 7:19

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  4. Content type: Regular article

    Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges....

    Authors: Moritz U. G. Kraemer, D. Bisanzio, R. C. Reiner, R. Zakar, J. B. Hawkins, C. C. Freifeld, D. L. Smith, S. I. Hay, J. S. Brownstein and T. Alex Perkins

    Citation: EPJ Data Science 2018 7:16

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  5. Content type: Regular article

    Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this ...

    Authors: Krittika D’Silva, Anastasios Noulas, Mirco Musolesi, Cecilia Mascolo and Max Sklar

    Citation: EPJ Data Science 2018 7:13

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  6. Content type: Regular article

    Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature...

    Authors: Andrea Cuttone, Sune Lehmann and Marta C. González

    Citation: EPJ Data Science 2018 7:2

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