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Related Experiment Video

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Generating mobility networks with generative adversarial networks.

Giovanni Mauro^1,2,3, Massimiliano Luca^4,5, Antonio Longa^6,5

¹Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.

EPJ Data Science

|December 19, 2022

Summary

This summary is machine-generated.

We developed MoGAN, a Generative Adversarial Network model, to create realistic urban mobility networks. This approach accurately simulates human movement patterns, outperforming traditional models for traffic and epidemic analysis.

Keywords:

Artificial intelligence Flow generation GANs Human mobility

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Area of Science:

Computational social science
Network science
Urban planning

Background:

Human displacement significantly impacts urban dynamics, influencing traffic, segregation, and disease spread.
Understanding and modeling these movements is vital for addressing complex societal challenges.

Purpose of the Study:

To introduce MoGAN, a novel Generative Adversarial Network (GAN)-based model for generating realistic urban mobility networks.
To represent city-wide human movement as a weighted directed graph, capturing flow dynamics.

Main Methods:

MoGAN utilizes Generative Adversarial Networks (GANs) for mobility network generation.
The model constructs a weighted directed graph where nodes are locations and edges represent movement flows.
Extensive experiments were conducted on public bike and taxi ride datasets.

Main Results:

MoGAN demonstrated superior performance compared to traditional Gravity and Radiation models in generating realistic mobility networks.
The generated networks accurately reflect real-world human movement patterns.

Conclusions:

MoGAN offers a powerful tool for simulating and analyzing urban mobility.
The model supports applications in data augmentation, urban planning simulations, and "what-if" scenario analysis for policy-making.