Trajectory clustering for arrival aircraft via new trajectory representation

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.