Prediction of Arrival Aircraft Time Spent in TMA with Graph Neural Network

Accurately predicting the time aircraft spend within Terminal Maneuvering Areas (TMA) is crucial for optimizing air traffic management, minimizing delays, and improving operational efficiency. This study introduces two Graph Neural Network (GNN) models—Graph Convolutional Network (GCN) and Graph Attention Network (GAT)—to forecast aircraft TMA duration based on historical Automatic Dependent Surveillance-Broadcast (ADS-B) data collected from Istanbul TMA. The models leverage spatial-temporal patterns and inter-aircraft relationships to generate predictions. The GAT model employs an attention mechanism to assign varying importance to different nodes, dynamically capturing complex relationships within the TMA. In contrast, the GCN model applies uniform weighting to node connections, offering computational efficiency but limited adaptability to dynamic air traffic conditions. Results demonstrate that the GNN-based approach is effective for TMA duration prediction due to its ability to adapt to traffic density variations. This work underscores the potential of GNN-based approaches in air traffic management applications, facilitating better-informed decision-making processes for traffic controllers and operational planners.