气候变化领域集成服务门户(CCPortal): A Deep Gravity model for mobility flows generation

CCPortal

DOI	10.1038/s41467-021-26752-4
	A Deep Gravity model for mobility flows generation
	Simini F.; Barlacchi G.; Luca M.; Pappalardo L.
发表日期	2021
ISSN	2041-1723
卷号	12 期号:1
英文摘要	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. © 2021, The Author(s).
语种	英语
scopus关键词	detection method; gravity; prediction; article; data availability; deep neural network; England; gravity model; health care facility; Italy; land use; New York; prediction; probability; England; Italy; New York [United States]; United Kingdom; United States
来源期刊	Nature Communications
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/251306
作者单位	University of Bristol, Department of Engineering Mathematics, Bristol, United Kingdom; The Alan Turing Institute, London, United Kingdom; Argonne Leadership Computing Facility, Argonne National Laboratory Lemont, Lemont, IL, United States; Amazon Alexa, Berlin, Germany; Fondazione Bruno Kessler, Trento, Italy; Free University of Bolzano, Bolzano, Italy; Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
推荐引用方式 GB/T 7714	Simini F.,Barlacchi G.,Luca M.,et al. A Deep Gravity model for mobility flows generation[J],2021,12(1).
APA	Simini F.,Barlacchi G.,Luca M.,&Pappalardo L..(2021).A Deep Gravity model for mobility flows generation.Nature Communications,12(1).
MLA	Simini F.,et al."A Deep Gravity model for mobility flows generation".Nature Communications 12.1(2021).