Critical aircraft identification method based on temporal network

In view of the problem of critical aircraft identification in air traffic situations, the existing research fails to fully consider the spatiotemporal effect in actual air traffic operation. Therefore, a method of critical aircraft identification based on a temporal network was proposed. Based on the convergence relationship between aircraft and its complexity, the temporal network model was constructed by the neighbor topological overlap coefficient, and the critical aircraft was determined based on the eigenvector centrality. Network attacks on critical aircraft nodes were carried out to observe the changes in sector complexity and compared with attacks based on static network indicators. The improved genetic algorithm was used to assign a new sector entry time to the aircraft node deleted by the network attack, so as to verify the selection effect of the critical aircraft. Actual data verification shows that compared with static network attacks, the proposed method can reduce the average sector complexity more efficiently when removing critical aircraft, and the improved genetic algorithm has higher convergence when solving the time allocation problem of critical aircraft entering the sector, making the sector complexity more stable in a certain period of time. The analysis of the control effect of critical aircraft shows that the temporal network method is more accurate than the static network in identifying the aircraft that has a greater influence on the sector complexity in a period of time.