A Spatiotemporal Hybrid Model for Airspace Complexity Prediction

Airspace complexity is a key indicator that reflects the safety of airspace operations in air traffic management systems. Furthermore, to achieve efficient air traffic control, it is necessary to accurately predict the airspace complexity. In this article, the authors propose a novel spatiotemporal hybrid deep learning model for airspace complexity prediction to efficiently capture spatial correlations as well as temporal dependencies pertaining to the airspace complexity data. Specifically, the authors apply convolutional networks to discover the short-term temporal patterns and skip long short-term memory networks to model the long-term temporal patterns of airspace complexity data. Furthermore, it is observed that the graph attention network in the authors' proposed model, which emphasizes capturing the spatial correlations of the airspace sectors, can significantly improve the prediction accuracy. Extensive experiments are conducted on the real data of six airspace sectors in Southwest China. The experimental results show that the authors' spatiotemporal deep learning approach is superior to state-of-the-art methods.


  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01897779
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 30 2023 8:53AM