Flight Trajectory Change Prediction Via Patched Spatial-Temporal Transformer

The change prediction of flight traces is a long-range multi-variant forecasting task aiming at detecting abnormal behavior in the airway. Directly implementing two-dimensional attention in Transformer-based models faces unaffordable computation costs when they handle long-range trajectories. This study proposes a patched spatial-temporal attention framework based on the Transformer architecture to reduce memory consumption. The propensity for error propagation in auto-regressive models limits the accuracy of remote predictions. To address this problem, a single-step decoder is designed to mitigate the accumulation of errors. Experiments prove that this approach enhances the precision of predictions compared to an AR model while ensuring a modest uptick in memory usage across two distinct routes from an ADS-B trace dataset.