Study on Flight Trajectory Clustering Method Based on Self-Attention Mechanism and Gaussian Mixture Variational Autoencoder
ZHANG Zhaoyue
LI Sha
BAO Shuida
Abstract：In order to accurately identify flight trajectory patterns,a flight trajectory clustering method based on Self-Attention mechanism(SA)and Gaussian Mixture Variational Autoencoder(GMVAE)is proposed.SA-GMVAE is an end-to-end deep clustering method.GMVAE uses variational inference to estimate the potential distribution of each trajectory,maps the input flight trajectory data to the potential space composed of multiple Gaussian distributions,and performs clustering according to the trajectory distribution characteristics.Considering that GMVAE cannot take into account the global key information of potential features,the Self-Attention mechanism is embedded in the encoder to capture global dependencies and automatically assign weights during feature extraction,so as to highlight key features and improve trajectory clustering effect.Finally,the approach flight trajectory data set of Tianjin Binhai International Airport is taken as an example to verify the effectiveness of the model.The experimental results show that:Compared with K-means,DBSCAN,DTW+HDBSCAN,AE+DP and AE+GMM,the contour coefficients of SA-GMVAE are increased by 27.6%,20.2%,18.2%,18.6%and 15.7%,respectively.Compared with the GMVAE clustering model without Self-Attention mechanism,the profile coefficient is increased by 9.5%,which can cluster the flight trajectory more accurately.
Keywords：flight trajectorypattern recognitionvariational autoencoderself-attention mechanism
Publication Date：2025-02-24
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 25-33 )