Center trajectory extraction algorithm based on multidimensional hierarchical clustering

Abstract

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.