1 Department of Physics & Astronomy and Goergen Institute of Data Science, University of Rochester, Rochester, NY, USA
2Institut de Physique Théorique, CEA, CNRS-URA 2306, F-91191, Gif-sur-Yvette, France
3Centre d'Etudes et de Mathématique Sociales, EHESS, Paris 75006, France
4Department of Engineering Mathematics, University of Bristol, UK
5Irstea, UMR TETIS, 500 rue JF Breton, 34093 Montpellier, France
6CNRS, UMR 8504 Géographie-cités, 13 rue du four, F-75006 Paris, France
7BioComplex Laboratory, School of Computing, Florida Institute of Technology, Melbourne, USA
8Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain
(October 2017)
| Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications. |