广西师范大学学报（自然科学版）2025，Vol.43Issue(4)：15-23,9.DOI:10.16088/j.issn.1001-6600.2024061404
基于K-means和Adam-LSTM的机场进场航迹预测研究
Research on Arrival Trajectory Prediction Based on K-means and Adam-LSTM
摘要
Abstract
It is the current mode of air traffic management that flight crews fly according to the instructions from air traffic controllers.With the increase of the number of flights,in order to effectively improve the efficiency of air traffic operation and reduce the workload of controllers,the development of intelligent air traffic management based on track prediction has become a new topic.Aiming at the research of flight track prediction technology,a two-stage flight track prediction method is put forward innovatively in this paper,which includes classification and then prediction.Firstly,K-means is used to cluster and classify the flight track based on the data of a certain airport.Next,Adam-LSTM deep learning model is constructed for each type of approach track,and high quality track prediction is realized.The results show that,compared with the traditional prediction model,the track prediction effect is greatly improved.The research results can provide technical support for intelligent air traffic management and abnormal track recognition.关键词
空中交通/工作负荷/航迹预测/深度学习Key words
air traffic/working load/track prediction/deep learning分类
航空航天引用本文复制引用
黎宗孝,张健,罗鑫悦,赵嶷飞,卢飞..基于K-means和Adam-LSTM的机场进场航迹预测研究[J].广西师范大学学报（自然科学版）,2025,43(4):15-23,9.基金项目
国家自然科学基金(52272356) （52272356）
中国民航大学创新训练项目(202310059166) （202310059166）
中央高校基本科研业务费项目(3122022095) （3122022095）
