Optimizing Aircraft Route Planning Based on Data-Driven and Physics-Informed Wind Field Predictions

Jieying Ma, Pengyu Xiang, Qinghe Yao (), Zichao Jiang (), Jiayao Huang and Hejie Li
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Jieying Ma: Department of Statistics, University of California, Davis, CA 95616, USA
Pengyu Xiang: School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China
Qinghe Yao: School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China
Zichao Jiang: School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China
Jiayao Huang: Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
Hejie Li: Guangzhou Meteorological Public Service Center, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 3, 1-13

Abstract: Upper-air wind fields play a crucial role in aircraft navigation, directly impacting flight safety and operational efficiency. In this study, we propose an advanced route planning framework that integrates wind field predictions derived from a neural network-based approach. Specifically, we leverage the PredRNN Sequence-to-Sequence algorithm to predict wind fields up to 10 h in advance. The model is trained on grid-based wind speed data at an altitude of approximately 5500 m, focusing on major airline routes over China. Our approach demonstrates superior accuracy in wind field forecasting when compared to other neural network architectures. To achieve route planning in dynamic wind environments, we employ the A* algorithm. The results demonstrate that the proposed method effectively identifies routes that approximate the ideal trajectory while successfully avoiding areas with drastic wind speed changes, thereby enhancing both the efficiency and safety of flight operations.

Keywords: wind field prediction; PredRNN; route planning; physics-informed neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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