DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction

2025-03-05

AI Summary

To address the degradation in 4D trajectory prediction accuracy caused by insufficient modeling of multi-aircraft interactions in airport terminal areas and dense airspace, this paper proposes a Dual-Attention-driven Spatio-Temporal Graph Convolutional Network (DA-STGCN). We innovatively design a self-attention-driven dynamic adjacency matrix reconstruction mechanism to jointly model the evolution of topological relationships among aircraft and spatio-temporal dependencies. Furthermore, we integrate Graph Attention Networks (GAT) with STGCN to enable synergistic optimization of node-wise relational and spatio-temporal features. Evaluated on two real-world ADS-B datasets, DA-STGCN achieves 20% and 30% reductions in Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively, significantly outperforming state-of-the-art methods. The proposed approach provides high-precision trajectory prediction support for critical air traffic management tasks, including conflict detection, anomaly monitoring, and fine-grained airspace management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

The importance of four-dimensional (4D) trajectory prediction within air traffic management systems is on the rise. Key operations such as conflict detection and resolution, aircraft anomaly monitoring, and the management of congested flight paths are increasingly reliant on this foundational technology, underscoring the urgent demand for intelligent solutions. The dynamics in airport terminal zones and crowded airspaces are intricate and ever-changing; however, current methodologies do not sufficiently account for the interactions among aircraft. To tackle these challenges, we propose DA-STGCN, an innovative spatiotemporal graph convolutional network that integrates a dual attention mechanism. Our model reconstructs the adjacency matrix through a self-attention approach, enhancing the capture of node correlations, and employs graph attention to distill spatiotemporal characteristics, thereby generating a probabilistic distribution of predicted trajectories. This novel adjacency matrix, reconstructed with the self-attention mechanism, is dynamically optimized throughout the network's training process, offering a more nuanced reflection of the inter-node relationships compared to traditional algorithms. The performance of the model is validated on two ADS-B datasets, one near the airport terminal area and the other in dense airspace. Experimental results demonstrate a notable improvement over current 4D trajectory prediction methods, achieving a 20% and 30% reduction in the Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively. The incorporation of a Dual-Attention module has been shown to significantly enhance the extraction of node correlations, as verified by ablation experiments.

Problem

• Improves 4D trajectory prediction for air traffic management.
• Addresses insufficient aircraft interaction modeling in current methods.
• Reduces prediction errors with a dual-attention spatiotemporal network.

Innovation

• Dual attention mechanism enhances spatiotemporal feature extraction
• Self-attention reconstructs adjacency matrix for better node correlation
• Graph attention distills spatiotemporal characteristics for trajectory prediction