Research on key aircraft identification method based on time-effective network

For the problem of identifying key aircraft in air traffic situation, existing research has failed to fully consider the spatiotemporal effects in the actual operation of air traffic. Therefore, a key aircraft identification method based on time-effective network is proposed. The convergence relationship and complexity between aircraft are used to construct a time-effective network model through the neighbor topology overlap coefficient, and the key aircraft is determined based on the eigenvector centrality. A network attack is carried out on the key aircraft nodes to observe the changes in sector complexity, and compared with the attack based on static network indicators, an improved genetic algorithm is used to assign new entry time to the aircraft nodes deleted by the network attack, so as to verify the selection effect of key aircraft. The actual data verification shows that compared with the static network attack, this method is more efficient in reducing the average complexity of the sector when removing key aircraft. The improved genetic algorithm has higher convergence when solving the key aircraft entry time allocation problem, making the sector complexity more stable within a certain period of time. Analysis of the control effect of key aircraft shows that the time-effective network method can more accurately identify aircraft that have a greater impact on sector complexity within a period of time than the static network.