A Data-Driven Framework for Flight Delay Propagation Forecasting During Extreme Weather

Jiuxia Guo (), Jingyuan Li, Jiang Yuan, Yungui Yang and Zihao Ren
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Jiuxia Guo: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China
Jingyuan Li: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China
Jiang Yuan: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China
Yungui Yang: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China
Zihao Ren: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China

Mathematics, 2025, vol. 13, issue 21, 1-23

Abstract: Flight delays during extreme weather events exhibit spatio-temporal propagation and cascading effects, posing serious challenges to the resilience of aviation systems. Existing prediction approaches often neglect dynamic dependencies across flight chains and struggle to model sparse extreme events. This study develops a data-driven framework that explicitly models delay propagation paths, incorporates historical scenario retrieval to capture rare disruption patterns, and integrates meteorological, airport operational, and flight-specific information through multi-source fusion. Using U.S. flight operations and weather records, the framework demonstrates clear advantages over established baselines in extreme-delay scenarios, achieving a MAE of 3.23 min, an RMSE of 6.25 min, and an R 2 of 0.92—improving by 8.8%, 26.0%, and 5.75% compared to the best benchmark. Ablation studies confirm the contribution of the propagation modeling, historical retrieval, and multi-source integration modules, while cross-airport evaluations reveal consistent accuracy at both major hubs (e.g., Atlanta, Chicago O’Hare) and regional airports (e.g., Kona, Anchorage). These findings demonstrate that the proposed framework enables reliable forecasting of delay propagation under complex weather conditions, providing valuable support for proactive departure management and enhancing the resilience of aviation operations.

Keywords: flight delay prediction; extreme delays; flight chain propagation; Temporal Fusion Transformer; data sparsity; aviation operations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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