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Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information. (arXiv:2012.00489v5 [cs.LG] UPDATED)
Jan. 24, 2022, 2:10 a.m. | Filippo Simini, Gianni Barlacchi, Massimiliano Luca, Luca Pappalardo
cs.LG updates on arXiv.org arxiv.org
The movements of individuals within and among cities influence critical
aspects of our society, such as well-being, the spreading of epidemics, and the
quality of the environment. When information about mobility flows is not
available for a particular region of interest, we must rely on mathematical
models to generate them. In this work, we propose the Deep Gravity model, an
effective method to generate flow probabilities that exploits many variables
(e.g., land use, road network, transport, food, health facilities) extracted …
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