With the advent of the big data era of air traffic control systems, the application of trajectory clustering in the field of air traffic has received widespread attention. This article reviews the trajectory clustering methods proposed in domestic and foreign literature. According to the different similarity measures and clustering evaluation criteria, the application of different clustering methods in trajectory clustering is introduced from several aspects, and the advantages and applicable scenarios of each algorithm are summarized. Then, based on the real flight history radar data information, the performance of the two different clustering algorithms in track clustering is analyzed. It mainly includes two aspects: First, in the data preprocessing part, data cleaning, filtering, and interpolation are performed. After processing, the data is resampled to obtain research data. Then, according to the characteristics of the track data, the two hyperparameters of the DBSCAN algorithm and the value of the K-means algorithm are determined, and the clustering results are visually displayed in combination with the real flight data from the Capital International Airport to Shanghai Hongqiao International Airport.
The existing aircraft center track extraction methods only extract the position information of the trajectory, which cannot meet the requirements of abnormal trajectory detection and trajectory prediction. This paper innovatively proposes a center locus extraction algorithm based on multidimensional hierarchical clustering. Firstly, to solve the problem that trajectory resampling is easy to lose the original trajectory features, an equal arc length interpolation resampling method is proposed to process the original trajectory data. Then, the weighted Euclidean distance matrix of the trajectory set is calculated. The calculation model of the weighted Euclidean distance matrix is novel and takes into account the influence of multidimensional features. Finally, multidimensional hierarchical clustering is used to get the traffic flow distribution and output the center trajectory. 703 departure trajectory data from the terminal area of an airport are used for example verification. The results show that compared with the traditional hierarchical clustering, this method has a significant advantage in accurately dividing traffic flow. Moreover, the extracted center locus can retain the multidimensional features of locus, which has certain practical significance.
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