Unveiling Hidden Dynamics in Air Traffic Networks: An Additional-Symmetry-Inspired Framework for Flight Delay Prediction

Chao Yin, Xinke Du (), Jianyu Duan, Qiang Tang and Li Shen
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Chao Yin: School of Management, Guizhou University, Guiyang 550025, China
Xinke Du: School of Business, Shanghai Normal University Tianhua College, Shanghai 201815, China
Jianyu Duan: School of Transportation Science and Engineering, Beihang University, Beijing 100080, China
Qiang Tang: School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China
Li Shen: School of Information and Electronics, Beijing Institute of Technology, Beijing 100080, China

Mathematics, 2025, vol. 13, issue 14, 1-22

Abstract: Flight delays pose a significant challenge to the modern aviation industry, with prediction difficulties arising from the need to accurately model spatio-temporal dependencies and uncertainties within complex air traffic networks. To address this challenge, this study proposes a novel hybrid predictive framework named DenseNet-LSTM-FBLS. The framework first employs a DenseNet-LSTM module for deep spatio-temporal feature extraction, where DenseNet captures the intricate spatial correlations between airports, and LSTM models the temporal evolution of delays and meteorological conditions. In a key innovation, the extracted features are fed into a Fuzzy Broad Learning System (FBLS)—marking the first application of this method in the field of flight delay prediction. The FBLS component effectively handles data uncertainty through its fuzzy logic, while its “broad” architecture offers greater computational efficiency compared to traditional deep networks. Validated on a large-scale dataset of 198,970 real-world European flights, the proposed model achieves a prediction accuracy of 92.71%, significantly outperforming various baseline models. The results demonstrate that the DenseNet-LSTM-FBLS framework provides a highly accurate and efficient solution for flight delay forecasting, highlighting the considerable potential of Fuzzy Broad Learning Systems for tackling complex real-world prediction tasks.

Keywords: flight delay predicyion; denseNet-LSTM; fuzzy broad learning system (FBLS); spatial-temporal correlations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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