Transactions of Nanjing University of Aeronautics & Astronautics
A Deep Learning-Based Approach for Terminal Area Flight Flow Operational Safety Situation Awareness
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Abstract:
Safety is the cornerstone of the civil aviation industry and the enduring focus of civil aviation. This paper uses air traffic complexity and potential aircraft conflict relationships as entry points to study the operational safety level of terminal area flight flows and proposes a deep learning-based method for safety situation awareness in terminal area aircraft operations. Firstly， a more comprehensive and precise safety situation assessment features are constructed. Secondly， a deep clustering situation recognition model with added safety situation information capture layer is proposed. Finally， a spatiotemporal graph convolutional neural network based on attention mechanism is constructed for predicting safety situations. Experimental results from a real dataset show that： （1） The proposed model surpasses traditional models across all evaluated dimensions； （2） the recognition model ensures that the encoded features capture distinctive safety situation information， thereby enhancing model interpretability and task alignment； （3） the prediction model demonstrates superior integrated modeling capabilities in both spatial and temporal dimensions. Ultimately， this paper elucidates the spatiotemporal evolution characteristics of air traffic safety situation levels， offering valuable insights for air traffic safety management.
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Project Supported:
The work was supported by the Chinese Special Research Project for Civil Aircraft （No.MJZ1-7N22） and the National Natural Science Foundation of China （No.U2133207）.
Clc Number:
U8
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