A Model for Aircraft Surface Taxiing Trajectory Prediction Base on Transformer-TCN-GRU

For aircraft taxiing trajectory prediction, existing methods exhibit low accuracy in real-time estimation of future positions over medium-term time horizons. To enhance prediction precision within this temporal scope while maintaining computational efficiency, this study proposes a taxiing trajectory prediction model integrating transformer networks, cross-attention mechanisms, temporal convolutional networks (TCN), and gated recurrent units (GRU) to generate multiple candidate trajectories. The Transformer encoder captures temporal dependencies and motion patterns from historical trajectory data to derive global feature representations. Airport vector maps and taxiing route instructions from air traffic control systems are utilized to compute planned future taxiing path coordinates. A cross-attention mechanism then aligns the global trajectory features (as Query) with critical positions in the planned path sequence, mapping the fused path-enhanced features into multimodal representations corresponding to candidate trajectories. The TCN-GRU decoder processes each modality to capture long-term temporal dependencies and outputs multiple predicted trajectories with associated probabilities. Validation on real taxiing trajectories from a major Chinese airport demonstrates minimum average displacement error (minADE) of 1.932 m and minimum final displacement error (minFDE) of 1.811 m for 8-second predictions. Compared to individual GRU and TCN models, the proposed approach reduces minADE/minFDE by 14.10%/30.88% and 16.62%/34.72% respectively, while maintain an average runtime of 17.70 milliseconds per sample. The proposed method achieves accurate and efficient trajectory prediction, supporting enhanced safety management in airport maneuvering areas.