An Inverted Transformer Framework for Aviation Trajectory Prediction with Multi-Flight Mode Fusion

As globalization and rapid economic development drive a surge in air transportation demand, the need for enhanced efficiency and safety in flight operations has become increasingly critical. However, the exponential growth in flight numbers has exacerbated airspace congestion, creating a stark contrast with the limited availability of airspace resources. This imbalance poses significant challenges to flight punctuality and operational efficiency. To mitigate these issues, existing models often rely solely on individual flight data, which restricts the breadth and depth of feature learning. In this study, we propose an innovative Inverted Transformer framework for aviation trajectory prediction enhanced by multi-flight mode fusion. This framework leverages multi-flight inputs and inverted data processing to enrich feature representation and optimize the modeling of multi-variate time series. By treating the entire time series of each variable as an independent token, our model effectively captures global temporal dependencies and enhances correlation analysis among multiple variables. Extensive experiments on real-world aviation trajectory datasets demonstrate the superiority of our proposed framework. The results show significant improvements in prediction accuracy. Moreover, the integration of multi-flight data enables the model to learn more comprehensive flight patterns, leading to robust performance across varying flight conditions. This research provides a novel perspective and methodology for aviation trajectory prediction, contributing to the efficient and safe development of air transportation systems.