MTDA-STGCN: Modern Temporal and Dual-Attention-Based Spatiotemporal Graph Convolutional Network for 4D Trajectory Prediction

Yuheng Kuang, Shuxuan Yuan, Xiang Zou, Jianping Zhang, Zhenyu Shi, Yuding Zhang, Fanman Meng, Zhengning Wang

Abstract: Four-dimensional (4D) trajectory prediction plays a critical role in modern air traffic management, enabling applications such as conflict detection, anomaly monitoring, and congestion mitigation. However, existing methods have limited information sources when modeling potential spatial correlations between aircraft in complex airspace scenarios, and their final trajectory inference ability is weak, resulting in lower prediction accuracy. Faced with these challenges, we propose Modern Temporal and Dual Attention based Spatiotemporal Graph Convolutional Network (MTDA-STGCN), which employs a self-attention mechanism to reconstruct the adjacency matrix to enhance the ability of capturing global node correlations. This adjacency matrix reconstructed with the self-attention mechanism is dynamically optimized throughout the training process of network, offering a more nuanced reflection of the inter-node relationships compared to traditional algorithms. Subsequently, our model uses graph attention to extract additional global features for modeling accuracy interactions between aircraft. Finally, the output is input into the Modern Temporal Prediction Network (MTPN) to obtain the predicted trajectory probability distribution. The experiments on real-world ADS-B datasets demonstrate that MTDA-STGCN outperforms existing 4D trajectory prediction algorithms on all datasets. The proposed dual-attention framework significantly enhances the capture of node spatial correlations, while the MTPN module effectively improves the accuracy of the predicted results.

External IDs:doi:10.1109/tits.2025.3605670