Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Abstract

The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

Impact of Chinese and European Airspace Constraints on Trajectory Optimization

“…The graphical representations are comparable to results from [26][27][28][29]31,35,36]. From this, it follows that internal uncertainties are unlikely.…”

“…This represents a significant step for a controller's decisional support [35]. Zeng et al [36] additionally highlighted benefits of the deep autoencoder and Gaussian mixture models to cluster aircraft trajectories in the Terminal Maneuvering Area (TMA).…”

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

“…To improve the accuracy of prediction tasks, the combination of clustering and machine learning prediction methods can significantly improve the prediction accuracy of large-scale clusterable data sets. Therefore, the application of machine learning and clustering to track prediction is a valuable and meaningful research topic [84,85]. For example Barratt et al [82] studied a probabilistic trajectory generation model in the terminal airspace, first using K-means to cluster the trajectory, and then constructing a Gaussian mixture model from the clustering to achieve accurate trajectory inference.…”

Aircraft Trajectory Prediction Based on Bayesian Optimised Temporal Convolutional Network–Bidirectional Gated Recurrent Unit Hybrid Neural Network

“…In general, a data mining approach can improve the prediction accuracy using different clustering algorithms, but it also generates problems concerning data storage and computational overhead to different degrees Deep learning models: the development of neural networks in deep learning has provided new ideas for nonlinear models, especially those good at managing time series. Thus, such networks have been implemented in the study of trajectory prediction [14][15][16]. Zhou et al [17] reconstructed and combined the predictive capabilities of multiple neural networks over different time spans to improve prediction accuracy.…”