基于K-means和Adam-LSTM的机场进场航迹预测研究

doi:10.16088/j.issn.1001-6600.2024061404

摘要/Abstract

摘要： 航班机组按照空中交通管制员的指令飞行,是当前空中交通的管理模式。随着航班量增加,为有效提高空中交通运行效率,同时缓解管制员工作负荷,开展基于航迹预测的智慧空管成为新课题。本文针对航班的航迹预测问题提出先分类后预测的航迹预测法。采用某机场进场航班数据,首先应用K-means对航迹进行聚类和分类,接着针对每类进场航迹,构建Adam-LSTM深度学习模型,实现较高质量的航迹预测。研究结果表明,相比传统预测模型,本文方法航迹预测效果具有较大提升,研究成果可为智慧空中交通管理、异常航迹识别等提供技术支撑。

关键词: 空中交通, 工作负荷, 航迹预测, 深度学习

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.

Key words: air traffic, working load, track prediction, deep learning

中图分类号: V355

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.

LI Zongxiao, ZHANG Jian, LUO Xinyue, ZHAO Yifei, LU Fei. Research on Arrival Trajectory Prediction Based on K-means and Adam-LSTM[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(4): 15-23.

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