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February 12, 2026AerospaceOpen Access

Edge-Based GNN for Network Delay Prediction Enhanced by Flight Connectivity

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Authors

ZTZhixing TangZNZhaolun NiuXCXuanting Chen

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Overview

Novel edge-based GNN predicts network delay in air traffic, indicating improved accuracy for hub airports.

Key Points

  • The study aims to develop an edge-based Graph Neural Network (GNN) for accurate network delay prediction in air traffic management.
  • Developed an edge-based GNN model for delay prediction.
  • Introduced metrics: delay width and delay strength informed by flight connectivity.
  • Utilized a message-passing mechanism for learning delay dynamics along air routes.
  • Conducted experiments on real-world datasets to evaluate performance.
  • Achieved lowest RMSE, MAE, and MSE compared to state-of-the-art models.
  • Demonstrated improved accuracy at major hub airports and maintained precision at smaller airports.
  • Revealed effective capturing of delay propagation dynamics across the air traffic network.

Cite This Study

Tang et al. (2026) studied this question.

synapsesocial.com/papers/698d6d9f5be6419ac0d529behttps://doi.org/10.3390/aerospace13020161
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