Trajectory Pattern Recognition in a Multi-Airport Systems Based on a New 3D Multi-Feature Trajectory Compression

With the rapid development of the global aviation industry, multi-airport systems have emerged as a critical component of large urban clusters and regional aviation networks. However, the complexity and uncertainty of air traffic flows in such systems are significantly increased by factors such as weather conditions, emergencies and the intricate interplay of arrival and departure routes across multiple airports, compounded by the complex structure of airspace. To address the challenges posed by the complex and dynamic air traffic flows within multi-airport systems, in this paper, we have introduced a trajectory recognition method based on a new 3D multi-feature trajectory compression (3D-MFTC) representation and clustering. First, a grid sparsity-based approach is proposed to detect and remove abnormal trajectories in multi-airport systems. Then, a novel 3D-MFTC is developed, which employs normalised Euclidean distance to compress 3D trajectory data and adjusts trajectory feature points based on a normal distribution. Then the fast-DTW algorithm is applied to calculate the trajectory similarity of the compressed data. Finally, DBSCAN is utilised to cluster the trajectory within the multi-airport system, with the optimal parameter combinations determined through K-distance graph analysis and grid search. Experimental results demonstrate that the proposed method significantly enhances the accuracy of trajectory similarity computation, enables fine-grained identification of trajectory patterns in multi-airport systems and outperforms traditional clustering algorithms in terms of both clustering performance and visualisation quality.