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.
［1］ ZHANG D Z, LEE K, LEE L. Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories［J］. Expert Systems with Applications,
2019, 122:85-101.
［2］ 江艺羡, 张岐山. 基于灰色方法与结构距离的飓风轨迹聚类算法［J］. 系统工程理论与实践, 2017, 37(4):1046-1055.
JIANG Yixian, ZHANG Qishan. The clustering algorithm of cyclone track based on grey theory and structure distance［J］. Systems Enigneering—Theory ＆ Practice,
2017, 37(4):1046-1055.
［3］ 朱小陆. 基于移动出行数据挖掘的城市交通结构研究［D］. 北京:北京邮电大学, 2018.
ZHU Xiaolu. Research on Urban Traffic Structure Based on Mobile Arrival Data Mining［D］. Beijing: Beijing University of Posts and Telecommunications,2018.
［4］ WANG C, HAN Z H. Trajectory clustering method research and applica-tion［C］∥International Conference on Transportation, Mechanical, and Electrical
Engineering.Changchun:［s.n.］,2011:97-100.
［5］ FRANK R. Clustering of flight tracks［C］∥ Proceedings of the American Institute of Aeronautics and Astronautics. Atlanta:［s.n.］, 2010: 1-9.
［6］ YU Q Y, LUO Y L, CHEN C M, et al. Trajectory similarity clustering based on multi-feature distance measurement［J］.Applied Intelligence, 2019，49:2315-2338.
［7］ SALAPOUR A, KHOTANLOU H. Direction-based similarity measure to trajectory clustering［C］∥The Institution of Engineering and Technology. Institution of
Engineering and Technology:［s.l.］, 2019,13(1):70-76.
［8］ 李楠, 强懿耕, 孙瑜, 等. 终端区航空器异常轨迹识别研究［J］. 中国安全科学学报, 2018, 28(11):21-27.
LI Nan, QIANG Yigeng, SUN Yu, et al. Research on identification of aircraft abnormal identification in terminal area［J］. China Safety Science Journal, 2018, 28
(11):21-27.
［9］ 郭威, 唐慧丰. 基于轨迹聚类的航空器轨迹模式挖掘研究［J］. 计算机应用研究, 2020, 37(2):416-420.
GUO Wei, TANG Huifeng. Research on trajectory pattern mining based on trajectory clustering［J］. Application Research of Computers, 2020, 37(2):416-420.
［10］ 徐涛, 陈雪蕊, 吕宗平. 基于航迹聚类的终端区飞行程序轨迹表示［J］. 四川大学学报(工程科学版), 2016, 48(6):188-196.
XU Tao, CHEN Xuerui, LYU Zongping. Flight procedure representation of terminal area based on flight tracks clustering. ［J］ Journal of Sichuan University
(Engineering Science Edition), 2016, 48(6):188-196.
［11］ 赵元棣, 王超, 李善梅, 等. 基于重采样的终端区飞行轨迹可信聚类方法［J］. 西南交通大学学报, 2017, 52(4):817-825.
ZHAO Yuandi, WANG Chao, LI Shanmei, et al. Dependable clustering method of flight trajectory in terminal area based on resampling［J］. Journal of Southwest
Jiaotong University, 2017, 52(4):817-825.
［12］ 王涛波, 黄宝军. 改进模糊聚类算法在航迹分析中的应用［J］. 中国安全科学学报, 2014, 24(2):23-27.
WANG Taobo, HUANG Baojun. Application of improved fuzzy clustering algorithm to analysis of trajectory［J］. China Safety Science Journal, 2014, 24(2):23-27.
［13］ 吴青华. 基于相对同步欧氏距离筛选的在线GPS轨迹数据压缩算法［J］. 计算机应用与软件,2018, 35(3):282-288.
WU Qinghua. Online GPS trajectory data compression algorithm based on relative synchronous Euclidean distance filtering［J］. Computer Applications and Software,
2018, 35(3):282-288.
［14］ 周英, 卓金武, 卞月青. 大数据挖掘:系统方法与实例分析［M］. 北京:机械工业出版社, 2016:192-218.
ZHOU Ying, ZHUO Jinwu, BIAN Yueqing. Big Data Mining: System Methods and Case Analysis ［M］. Beijing: Mechanical Industry Press, 2016:192-218.
2021, Vol. 40 
