Predicting flight delay propagation in the Brazilian aviation network with Heterogeneous Graph Neural Networks - Wittmann, Eduardo Zantut; Cazzolato, Mirela Teixeira (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2026)
This work takes advantage of a Graph Neural Network (GNN) model to predict flight delay propagation within the Brazilian aviation network. We propose SUBAERO, a method with an architecture that comprises a diverse data ingestion pipeline, integrating weather, airport, flight, and aircraft data via APIs. These datasets are structured in a graph-oriented database (Neo4j) to map complex spatial-temporal dependencies. A Heterogeneous Graph Neural Network (HGT) approach was employed, achieving a Mean Absolute Error (MAE) of 6.7 minutes in delay predictions. This study applies advanced Data Science techniques to optimize urban and regional mobility, providing a timely decision-support tool to mitigate operational bottlenecks and enhance transport infrastructure for social good.