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Aerospace
Volume 13
Issue 3
10.3390/aerospace13030208
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Article

Air Traffic Noise Prediction Method Based on Machine Learning Driven by Quick Access Recorder

by
Zhixing Tang
1,*,
Yijie Fan
1,*,
Xuanting Chen
1,
Xinyan Shi
1,
Zhaolun Niu
1,
Yuming Zhong
2,
Meng Jia
3 and
Xiaowei Tang
4
1
College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China
2
China Academy of Civil Aviation Science and Technology, Beijing 100028, China
3
School of Transportation Engineering, Nanjing Institute of Technology, Nanjing 211167, China
4
Key Laboratory of Civil Aviation Emergency Science and Technology, Nanjing 211106, China
*
Authors to whom correspondence should be addressed.
Aerospace 2026, 13(3), 208; https://doi.org/10.3390/aerospace13030208
Submission received: 30 December 2025 / Revised: 10 February 2026 / Accepted: 21 February 2026 / Published: 24 February 2026
(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))
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Abstract

Accurate prediction of air traffic noise is critical for advancing environmentally sustainable operations in high density terminal areas. Conventional noise prediction models often exhibit significant limitations due to discrepancies between actual and nominal flight trajectories. To overcome this challenge, this study introduces a probabilistic framework that integrates real air-traffic-flow data to generate realistic flight trajectory distributions. The proposed methodology extracts key operational features—including trajectory distribution probabilities, and essential trajectory operation features—within a machine learning architecture. Furthermore, we develop a dedicated air traffic noise prediction model for clustered flight paths that explicitly incorporates traffic flow patterns, enabling high-fidelity simulation of noise propagation under actual air traffic operation. The framework is validated using a QAR (Quick Access Recorder) dataset from the terminal area of Changsha Huanghua International Airport. Experimental results demonstrate the model’s high predictive accuracy for both air traffic noise distribution and its influence, coupled with computational efficiency and practical applicability. The findings indicate that the proposed approach successfully addresses the challenge of predicting air traffic noise from divergent, real-world flight trajectories, offering a robust method for supporting noise-abatement strategies and sustainable aviation-planning initiatives.
Keywords: air traffic noise prediction; probabilistic traffic trajectory; trajectory operation features; machine learning; QAR air traffic noise prediction; probabilistic traffic trajectory; trajectory operation features; machine learning; QAR

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MDPI and ACS Style

Tang, Z.; Fan, Y.; Chen, X.; Shi, X.; Niu, Z.; Zhong, Y.; Jia, M.; Tang, X. Air Traffic Noise Prediction Method Based on Machine Learning Driven by Quick Access Recorder. Aerospace 2026, 13, 208. https://doi.org/10.3390/aerospace13030208

AMA Style

Tang Z, Fan Y, Chen X, Shi X, Niu Z, Zhong Y, Jia M, Tang X. Air Traffic Noise Prediction Method Based on Machine Learning Driven by Quick Access Recorder. Aerospace. 2026; 13(3):208. https://doi.org/10.3390/aerospace13030208

Chicago/Turabian Style

Tang, Zhixing, Yijie Fan, Xuanting Chen, Xinyan Shi, Zhaolun Niu, Yuming Zhong, Meng Jia, and Xiaowei Tang. 2026. "Air Traffic Noise Prediction Method Based on Machine Learning Driven by Quick Access Recorder" Aerospace 13, no. 3: 208. https://doi.org/10.3390/aerospace13030208

APA Style

Tang, Z., Fan, Y., Chen, X., Shi, X., Niu, Z., Zhong, Y., Jia, M., & Tang, X. (2026). Air Traffic Noise Prediction Method Based on Machine Learning Driven by Quick Access Recorder. Aerospace, 13(3), 208. https://doi.org/10.3390/aerospace13030208

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