Aircraft trajectory clustering in terminal area and anomaly recognition based on density peak

To effectively solve the problem that it is difficult to automatically separate the standard flight, non-standard flight and anomalous flight patterns in high-traffic terminal areas, an aircraft trajectory clustering model was established based on the robust deep auto-encoder (RDAE) and clustering by fast search and find of density peaks (CFSFDP) using the widely recorded automatic dependent surveillance-broadcast (ADS-B) data. The RDAE was designed to reduce the dimensionality and extract nonlinear features from the aircraft trajectory dataset of terminal areas, while various regularization methods were adopted to constrain the internal low-dimensional manifolds to reconstruct a denser aircraft trajectory, and the aircraft trajectory was input to the CFSFDP algorithm. The silhouette coefficient was used to select the cluster centers for flight patterns with different densities and recognize anomalous trajectories by adjusting the edge density parameter. Two widely used aircraft trajectory clustering models, namely principal component analysis (PCA) combined with density-based spatial clustering of applications with noise (DBSCAN) as well as dynamic time wrapping (DTW) combined with DBSCAN, were taken as comparisons. Experiments were conducted on a small data of 1 d and a large data of 45 d of Guangzhou Baiyun Airport. Analysis results demonstrate that the model combining DTW and CFSFDP provides the best aircraft trajectory clustering performance on the small data, and the silhouette coefficient is 62% and 28% higher than those of comparisons, respectively. The DTW/CFSFDP model can automatically recognize standard flights following the area navigation procedures and non-standard flights that reflect controllers' preferences in specific environments, and the accuracies for identifying anomalous aircraft trajectories also improve by 57% and 10%, respectively. For the large data, the clustering performance of the proposed RDAE/CFSFDP model improves by 13% compared to that of the classical PCA/DBSCAN algorithm. Further, the proposed model exhibits acceptable time complexity. In summary, the established flight pattern discrimination model for terminal areas can provide a data extraction platform for the airspace-level traffic flow performance evaluation and the flight-level aircraft trajectory prediction and optimization. 2 tabs, 10 figs, 31 refs.