中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2021, Vol. 40 ›› Issue (01): 1-6.DOI: 10.3969/j.issn.1674-0696.2021.01.01

• 交通+大数据人工智能 •    下一篇

基于轨迹压缩的航空器飞行轨迹聚类研究

李楠,强懿耕,樊瑞   

  1. (中国民航大学 空中交通管理学院,天津 300300)
  • 收稿日期:2019-07-01 修回日期:2020-02-22 出版日期:2021-01-11 发布日期:2021-01-11
  • 作者简介:李楠(1978—),女(满族),辽宁抚顺人,副教授,博士,主要从事空中交通运输规划与管理及仿真技术方面的研究。E-mail:lily_cauc@163.com 通信作者:强懿耕(1994—),男,陕西西安人,硕士研究生,主要从事空中交通运输规划与管理方面的研究。E-mail:1040078707@qq.com
  • 基金资助:
    国家重点研发项目(2016YFB0502405);国家自然科学基金项目(71801215)

Aircraft Flight Trajectory Clustering Based on Trajectory Compression

LI Nan, QIANG Yigeng, FAN Rui   

  1. (College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China)
  • Received:2019-07-01 Revised:2020-02-22 Online:2021-01-11 Published:2021-01-11
  • Supported by:
     

摘要: 为准确掌握终端区航空器飞行模式,有效评估、优化飞行程序,首先,针对飞行轨迹点的时空特性,提出基于时间比的自上向下算法压缩轨迹;其次,结合轨迹点的速度和航向特征,建立基于多维属性特征的轨迹相似性模型;最后,应用禁忌粒子群(TSPSO)算法改进和优化模糊C-均值聚类(FCM)算法,并结合终端区的真实飞行轨迹数据对改进聚类算法进行验证。结果表明:轨迹压缩技术极大地降低了计算开销;与传统的FCM算法相比,改进后的聚类算法可以得到更优的满意解,提高飞行轨迹聚类效果。

 

关键词: 交通运输工程, 航空运输, 改进聚类算法, 禁忌粒子群算法, 飞行轨迹, 轨迹压缩, 模糊C-均值聚类

Abstract: In order to accurately grasp the flight mode of aircraft in terminal area and effectively evaluate and optimize flight procedures, firstly, according to the spatio-temporal characteristics of flight trajectory points, a top-down algorithm based on time ratio was proposed to compress the flight path. Secondly, combined with the velocity and heading characteristics of the trajectory points, the trajectory similarity model based on multi-dimensional features was established. Finally, the tabu search particle swarm optimization (TSPSO) algorithm was applied to improve and optimize the fuzzy C-means clustering (FCM) algorithm, and the improved clustering algorithm was verified by combining with the real flight trajectory data of the terminal area. The results show that the trajectory compression technology greatly reduces the computational cost. Compared with the traditional FCM algorithm, the improved clustering algorithm can obtain a better satisfactory solution and improve the flight trajectory clustering effect.

Key words: traffic and transportation engineering, air transportation, improved clustering algorithm, the tabu search particle swarm optimization (TSPSO) algorithm, flight trajectory, trajectory compression, the fuzzy C-means clustering

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