Year: 2025
Type: article
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 R2 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.
Source: Mathematics
DOI: 10.3390/math13213551
Authors: Jiuxia Guo, Jingyuan Li, Jiang Yuan, Yungui Yang, Zihao Ren
Institution: Civil Aviation Flight University of China
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415930195