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Related Experiment Video

Updated: Oct 13, 2025

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

Nation-wide human mobility prediction based on graph neural networks.

Fernando Terroso-Sáenz¹, Andrés Muñoz¹

¹UCAM, Campus de los Jerónimos, Guadalupe, 30107 Murcia España.

Applied Intelligence (Dordrecht, Netherlands)

|November 12, 2021

Summary

This summary is machine-generated.

This study introduces a novel Graph Neural Network (GNN) for predicting nationwide human mobility. The model accurately forecasts inter-urban travel, requiring only one model for all regions.

Keywords:

Graph-based neural networks Human flow prediction Human mobility Large-scale mobility Mobile phone location data

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Last Updated: Oct 13, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

Area of Science:

Data Science
Urban Planning
Network Science

Background:

Human mobility flow prediction is crucial for urban planning and epidemiology.
Existing methods primarily focus on intra-urban (within-city) travel forecasting.
There is a need for models that can predict inter-urban (between-city) mobility at a national scale.

Purpose of the Study:

To develop a nation-wide mobility predictor for anticipating inter-urban displacements.
To utilize Graph Neural Networks (GNNs) for capturing latent relationships between geographical regions.
To create a single, unified model for processing mobility data across diverse areas.

Main Methods:

A Graph Neural Network (GNN) architecture was employed to model spatial dependencies.
The model was trained and evaluated using an open dataset of nationwide trips in Spain.
Weather conditions were incorporated as a feature in the prediction model.

Main Results:

The proposed GNN predictor demonstrated high accuracy in forecasting the number of inter-urban trips.
The model achieved reliable predictions across multiple time horizons.
A significant advantage was the model's ability to process all mobility areas with a single unified network.

Conclusions:

The developed GNN approach effectively predicts nation-wide human mobility flows.
This method offers a more efficient and scalable solution compared to area-specific models.
The findings support the application of GNNs for large-scale mobility forecasting in diverse domains.