南京航空航天大学学报（英文版）2024，Vol.41Issue(6)：783-805,23.DOI:10.16356/j.1005-1120.2024.06.009
基于深度学习的终端区航班流运行安全态势感知方法
A Deep Learning-Based Approach for Terminal Area Flight Flow Operational Safety Situation Awareness
摘要
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.关键词
空中交通/安全态势感知/深度学习/安全管理Key words
air traffic/safety situation awareness/deep learning/safety management分类
交通工程引用本文复制引用
邓成,张启钱,张洪海,万俊强,李靖宇..基于深度学习的终端区航班流运行安全态势感知方法[J].南京航空航天大学学报（英文版）,2024,41(6):783-805,23.基金项目
The work was supported by the Chi-nese Special Research Project for Civil Aircraft(No.MJZ1-7N22)and the National Natural Science Foundation of Chi-na(No.U2133207). （No.MJZ1-7N22）
