A Spatiotemporal Flight Trajectory Prediction and Online Learning Framework Based on Integrated Transformer-Bidirectional Gated Recurrent Unit

The development of time-based flow management has significantly enhanced the safety, reliability, and predictability of air traffic control (ATC). Actual flight paths often deviate from these standard terminal arrival routes due to pilots requesting shortcut arrivals or ATC officers implementing holding procedures to alleviate congestion. These deviations exacerbate the dynamic complexity of air traffic management (ATM). To address these challenges, we propose a novel online learning Transformer-bidirectional gated recurrent unit (Transformer-BiGRU) framework for tactical spatiotemporal flight trajectory prediction. BiGRU further obtains bidirectional sequence information to improve the Transformer’s spatiotemporal prediction. The proposed research utilises image processing techniques to produce ATC aeronautical holding instructions from historical automatic dependent surveillance-broadcast data. The framework significantly improves real-time prediction ability and environment adaptability by integrating holding instructions and online learning. Experiment results demonstrate that incorporating holding instructions with the proposed Transformer-BiGRU reduces the mean absolute error by approximately 10% in latitude, 8.9% in longitude, and 13.1% in flight level compared to the best baseline model. Furthermore, the mean deviation error of horizontal distance decreases from 0.49 to 0.42 nautical miles (a 13% improvement). These results confirm that the methodology benefits real-time ATC decision-making in various ATM scenarios and provides valuable insights to assure airspace safety.