Abstract:

With the rapid development of the civil aviation industry, the noise pollution problem of airport is more and more serious. Research on flight tracks clustering is important for prevention and control of airport noise. The flight track point pair chose method which is the existing flight track clustering common method, could not achieve one to one match in the space, and it has a strong influence on the clustering results. In order to solve this problem, a flight track similarity measure model is proposed based on the vertical distance of track points. It can ignore the effects of flight’s speed on flight tracks similarity. According to the daily tracks data, flight tracks can be clustered. 2D and 3D clustering of the flight tracks can be achieved by the K-medoids clustering algorithm. And Davies Bouldin (DB) index and the Dunn index are used to evaluate the clustering results. Experimental results show that the proposed model can measure the similarity more profitably between the tracks and the results of flight tracks clustering are better. Therefore, the rationality and availability of the model, have been verified.