Unveiling Hidden Dynamics in Air Traffic Networks: An Additional-Symmetry-Inspired Framework for Flight Delay Prediction
Chao Yin, Xinke Du, Jianyu Duan, Qiang Tang, Li Shen
Source 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.
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.