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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. Regular article

    Understanding predictability and exploration in human mobility

    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...

    Andrea Cuttone, Sune Lehmann and Marta C. González

    EPJ Data Science 2018 7:2

    Published on: 11 January 2018