A Deep Gravity model for mobility flows generation

TLDR

Mobility flows shape well‑being, epidemic spread, and environmental quality, and when data are unavailable, mathematical models are required to generate them. This work proposes Deep Gravity to generate mobility flow probabilities using a wide range of geographic features. Deep Gravity leverages deep neural networks to learn non‑linear relationships among these features. Experiments on England, Italy, and New York State demonstrate that Deep Gravity outperforms classic gravity models and other baselines, especially in densely populated areas, generalizes to regions without training data, and its predictions can be interpreted with explainable AI to reveal country‑specific differences.

Abstract

Abstract 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 Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.