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

Ma, Jieying; Xiang, Pengyu; Yao, Qinghe; Jiang, Zichao; Huang, Jiayao; Li, Hejie

doi:10.3390/math13030367

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Open AccessArticle

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

by

Jieying Ma

¹,

Pengyu Xiang

²,

Qinghe Yao

^2,*

,

Zichao Jiang

^2,*

,

Jiayao Huang

³ and

Hejie Li

⁴

¹

Department of Statistics, University of California, Davis, CA 95616, USA

²

School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China

³

Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA

⁴

Guangzhou Meteorological Public Service Center, Guangzhou, China

^*

Authors to whom correspondence should be addressed.

Mathematics 2025, 13(3), 367; https://doi.org/10.3390/math13030367

Submission received: 8 December 2024 / Revised: 8 January 2025 / Accepted: 13 January 2025 / Published: 23 January 2025

(This article belongs to the Special Issue Numerical and Computational Methods in Engineering)

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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 hours 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

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MDPI and ACS Style

Ma, J.; Xiang, P.; Yao, Q.; Jiang, Z.; Huang, J.; Li, H. Optimizing Aircraft Route Planning Based on Data-Driven and Physics-Informed Wind Field Predictions. Mathematics 2025, 13, 367. https://doi.org/10.3390/math13030367

AMA Style

Ma J, Xiang P, Yao Q, Jiang Z, Huang J, Li H. Optimizing Aircraft Route Planning Based on Data-Driven and Physics-Informed Wind Field Predictions. Mathematics. 2025; 13(3):367. https://doi.org/10.3390/math13030367

Chicago/Turabian Style

Ma, Jieying, Pengyu Xiang, Qinghe Yao, Zichao Jiang, Jiayao Huang, and Hejie Li. 2025. "Optimizing Aircraft Route Planning Based on Data-Driven and Physics-Informed Wind Field Predictions" Mathematics 13, no. 3: 367. https://doi.org/10.3390/math13030367

APA Style

Ma, J., Xiang, P., Yao, Q., Jiang, Z., Huang, J., & Li, H. (2025). Optimizing Aircraft Route Planning Based on Data-Driven and Physics-Informed Wind Field Predictions. Mathematics, 13(3), 367. https://doi.org/10.3390/math13030367

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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