Resumen
Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air traffic complexity in a sector were mostly based on aircraft operational status and lacked comprehensiveness of characterization and were less adaptable in real situations. To settle these issues, a deep learning technique grounded on complex networks was proposed, employing the flight conflict network (FCN) to generate an air traffic situation graph (ATSG), with the air traffic control instruction (ATCOI) received by each aircraft included as an extra node attribute to increase the accuracy of the evaluation. A pooling method with a graph neural network (GNN) was used to analyze the graph-structured air traffic information and produce the sector complexity rank automatically. The model Hierarchical Graph Representing Learning (HGRL) was created to build comprehensive feature representations which involve two parts: graph structure coarsening and graph attribute learning. Structure coarsening reduced the feature map size by choosing an adaptive selection of nodes, while attribute coarsening selected key nodes in the graph-level representation. The experimental findings of a real dataset from the Chinese aviation industry reveal that our proposed model exceeds prior methods in its ability to extract critical information from an ATSG. Moreover, our work could be applied in the two main types of sectors and without extra factor calculations to determine the complexity of the airspace.