Aircraft Trajectory Dataset Augmentation in Latent Space
This work addresses dataset augmentation for aircraft trajectory modeling, which is incremental as it applies existing techniques like Transformers and GMMs to a specific domain problem.
The paper tackles the problem of insufficient and unbalanced aircraft trajectory datasets for air traffic management by proposing ATRADA, a framework that generates high-quality synthetic trajectory data using a Transformer encoder, PCA, and Gaussian mixture models, with experiments showing it outperforms several baselines.
Aircraft trajectory modeling plays a crucial role in air traffic management (ATM) and is important for various downstream tasks, including conflict detection and landing time prediction. Dataset augmentation by adding synthetically generated trajectory data is necessary to develop a more robust aircraft trajectory model and ensure that the trajectory dataset is sufficient and balanced. We propose a novel framework called ATRADA for aircraft trajectory dataset augmentation. In the proposed framework, a Transformer encoder learns the underlying patterns in the original trajectory dataset and converts each data point into a context vector in the learned latent space. The converted dataset is projected to reduced dimensions using principal component analysis (PCA), and a Gaussian mixture model (GMM) is applied to fit the probability distribution of the data points in the reduced-dimensional space. Finally, new samples are drawn from the fitted GMM, the dimension of the samples is reverted to the original dimension, and the samples are decoded with a multi-layer perceptron (MLP). Several experiments demonstrate that the framework effectively generates new, high-quality synthetic aircraft trajectory data, which were compared to the results of several baselines.