Human mobility: Models and applications

By Hugo Barbosa, Marc Barthelemy, Gourab Ghoshal, Charlotte James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, José J. Ramasco, Filippo Simini, Marcello Tomasini. 2017

1,033 citations recorded by the source corpus.

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 spatiotem-poral 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.

Subfields: Automotive Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/d7661d1f9115ae39fc0831071c8b559456f64ff0

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In Automotive Engineering

Marta C. González

In Transportation

Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, background): “It is a well-established measure to compute the similarity between real flows, 𝑦𝑟 , and generated flows, 𝑦𝑔: 𝐶𝑃𝐶 = 2 ∑ 𝑖, 𝑗𝑚𝑖𝑛(𝑦𝑔 (𝑙𝑖 , 𝑙 𝑗 ), 𝑦𝑟 (𝑙𝑖 , 𝑙 𝑗 ))∑ 𝑖, 𝑗 𝑦 𝑔 (𝑙𝑖 , 𝑙 𝑗 ) + ∑ 𝑖, 𝑗 𝑦 𝑟 (𝑙𝑖 , 𝑙 𝑗 ) (7) CPC is always positive and contained in the closed”
A Deep Gravity model for mobility flows generation (2020, methodology): “…machine learning models because they are the natural extension of the state-of-the-art model for ﬂ ow generation, i.e., the singly constrained gravity model 15,47 , which corresponds to a multinomial logistic regression that is formally equivalent to a linear neural network with one softmax layer.”
Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, positive): “Human mobility (for a review, see Barbosa et al.Barbosa et al., 2018) is a critical component to understanding various disaster events.”
COVID-19 lockdown induces disease-mitigating structural changes in mobility networks (2020, background): “[39] H.”