Trajectory clustering can identify the flight patterns of the air traffic, which in turn contributes to the airspace planning, air traffic flow management, and flight time estimation. This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation. The proposed method consists of four significant steps: representing the trajectories, grouping the trajectories based on the new representation, measuring the similarities between different trajectories through dynamic time warping (DTW) in each group, and clustering the trajectories based on k-means and density-based spatial clustering of applications with noise (DBSCAN). We take the inbound trajectories toward Shanghai Pudong International Airport (ZSPD) to carry out the case studies. The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns, but also improve the performance of flight time estimation.

中文翻译：

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通过新的轨迹表示法对到达飞机进行轨迹聚类

轨迹聚类可以识别空中交通的飞行模式，进而有助于空域规划，空中交通流量管理和飞行时间估计。通过一种新的轨迹表示方法，提出了一种基于语义的进场飞机轨迹聚类方法。所提出的方法包括四个重要步骤：表示轨迹，基于新表示对轨迹进行分组，通过每组中的动态时间规整（DTW）测量不同轨迹之间的相似性以及基于k均值和密度对轨迹进行聚类应用的基于空间的空间聚类（DBSCAN）。我们以往上海浦东国际机场（ZSPD）的进场轨迹进行案例研究。