Özet:

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 K 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.

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