Trajectory clustering for arrival aircraft via new trajectory representation

• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: CONTROL THEORY AND APPLICATION
• 页码范围: 473-486
• 页数: 14页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000040

摘要

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 pa-per presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation. The proposed method consists of four significant steps: represent-ing the trajectories, grouping the trajectories based on the new representation, measuring the similarities between different tra-jectories 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 Inter-national Airport (ZSPD) to carry out the case studies. The corres-ponding results indicate that the proposed method could not only distinguish the particular flight patterns, but also improve the performance of flight time estimation.