Dynamic Prediction of Air Traffic Situation in Large-Scale Airspace

Air traffic situation prediction is critical for traffic flow management and the optimal allocation of airspace resources. In this study, the multi-sector airspace scenario is abstracted into an undirected graph. A spatiotemporal graph convolutional network (STGCN) model is developed to portray the spatiotemporal correlation between the sector operational situation changes. The model can predict multi-sector operational situations using time series data such as sector operational situation data and traffic volume within the sector. Experimenting on the air traffic situation dataset of 30 area sectors in the Shanghai control area revealed that the STGCN model has a prediction accuracy of above 90%, and it outperforms the benchmark method of traditional traffic prediction. This proves the effectiveness of the proposed situation prediction model.