Abstract: Aiming at the problem that traditional clustering algorithms cannot capture the implicit relationship of high-dimen-sional trajectory data in low-dimensional space,and it is difficult to define appropriate similarity measures to consider both local and global features of trajectories,a multivariate trajectory deep clustering(MTDC) framework based on deep neural network(DNN) is proposed and used for air traffic flow recognition and anomaly detection.The framework mainly includes an asymmetric autoencoder and a custom trajectory clustering layer.The autoencoder is mainly composed of 1D convolutional neural network and bi-directional long short-term memory to learn the feature representation of the original input in the low-dimensional latent space.The trajectory clustering layer realizes clustering by calculating the Q distribution of samples in the hidden space.Combined with reconstruction loss of autoencoder and trajectory clustering Q distribution,a new anomaly score is defined for anomaly trajectory detection.The results of experiments using real trajectory data based on automatic dependent surveillance-broadcast(ADS-B) show that the proposed framework is effective for air traffic flow recognition and can detect anomaly trajectories that are mea-ningful and interpretable.

Key words: Trajectory clustering, Anomaly detection, Deep neural network, Autoencoder, ADS-B