Generating mobility networks with generative adversarial networks

EPJ Data Science

Regular Article

Bruno Lepri¹  Massimiliano Luca²  Luca Pappalardo³  Giovanni Mauro⁴  Antonio Longa⁵ 

[1] Fondazione Bruno Kessler, Trento, Italy;Free University of Bolzano, Bolzano, Italy;Fondazione Bruno Kessler, Trento, Italy;Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy;Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy;IMT School for Advanced Studies, Lucca, Italy;University of Pisa, Pisa, Italy;University of Trento, Trento, Italy;Fondazione Bruno Kessler, Trento, Italy;

关键词: Human mobility;    Artificial intelligence;    Flow generation;    GANs;   

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

 received in 2022-02-22, accepted in 2022-11-16,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

Fig. 1

Fig. 5

12951_2022_1749_Article_IEq3.gif

Fig. 4

Fig. 3

Fig. 4

Fig. 3

12864_2022_9026_Article_IEq187.gif

Fig. 1

Fig. 1

Fig. 2

Fig. 7

Fig. 1

12951_2022_1749_Article_IEq1.gif

Fig. 2

12888_2022_4322_Article_IEq11.gif

12888_2022_4322_Article_IEq10.gif

Fig. 1

12888_2022_4322_Article_IEq9.gif

12888_2022_4322_Article_IEq8.gif

12888_2022_4322_Article_IEq7.gif

12888_2022_4322_Article_IEq6.gif

12888_2022_4322_Article_IEq5.gif

12888_2022_4322_Article_IEq4.gif

12888_2022_4322_Article_IEq3.gif

12864_2022_9026_Article_IEq170.gif

Fig. 1

Fig. 1

Fig. 1

Fig. 5

12936_2022_4393_Article_IEq6.gif

Fig. 1

Fig. 13

12888_2022_4322_Article_IEq1.gif

Fig. 1

Fig. 3

Fig. 3

Fig. 6

Fig. 1

Fig. 1

Fig. 4

Fig. 1

Fig. 3

Fig. 1

Fig. 2

Fig. 1

Fig. 3

Fig. 1

Fig. 1

Fig. 2

Fig. 2

Fig. 4

Fig. 3

Fig. 1

Fig. 1

Fig. 5

Fig. 4

Fig. 9

Fig. 8

Fig. 7

Fig. 4

Fig. 3

Fig. 7

Fig. 4

Fig. 1

Fig. 1

Fig. 3

Fig. 8

Fig. 2

Fig. 5

Fig. 2

Fig. 1

Fig. 5

Fig. 3

Fig. 2

12864_2022_9026_Article_IEq102.gif

Fig. 2

Fig. 2

Fig. 7

Fig. 8

Fig. 2

Fig. 1

Fig. 1

Fig. 2

Fig. 2

Fig. 3

Fig. 3

Fig. 2

12864_2022_9026_Article_IEq78.gif

Fig. 1

Fig. 2

Fig. 2

Fig. 1

Fig. 1

Fig. 3

Fig. 4

Fig. 2

Fig. 1