Articolo in rivista, 2022, ENG, 10.1140/epjds/s13688-022-00372-4

Generating mobility networks with generative adversarial networks

Mauro G.; Luca M.; Longa A.; Lepri B.; Pappalardo L.

University of Pisa, and CNR-ISTI, Pisa, Italy and IMT School for Advanced Studies, Lucca, Italy; Free University of Bolzano, Bolzano and Fondazione Bruno Kessler, Trento, Italy; University of Trento and Fondazione Bruno Kessler, Trento, Italy; Fondazione Bruno Kessler, Trento, Italy; CNR-ISTI, Pisa, Italy

The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.

EPJ 11 (1)

Keywords

Human mobility, Artificial intelligence, Flow generation, GANs

CNR authors

Mauro Giovanni, Pappalardo Luca

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 477667

Year: 2022

Type: Articolo in rivista

Creation: 2023-02-08 14:39:13.000

Last update: 2023-02-08 14:39:13.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:477667

DOI: 10.1140/epjds/s13688-022-00372-4

Scopus: 2-s2.0-85143360745

ISI Web of Science (WOS): 000894436600001