This work provides a large-scale trajectory optimization approach based on air traffic complexity to balance the overall airspace situation under the trajectory-based operation mode. It uses real operation data simulation to verify its effectiveness and optimization effect. Firstly, an air traffic complexity calculation model is constructed based on the potential interaction relationship between flights. Secondly, a multi-objective large-scale trajectory optimization model that meets the operational requirements of air traffic control is constructed based on the air traffic complexity calculation model, and a high-quality genetic solution algorithm is proposed. Finally, using the national flight operation data from June 2019, a simulation simulation of air traffic complexity-based trajectory optimization is performed, and a comparison between conflict-free trajectory optimization and air traffic complexity-based trajectory optimization is conducted. Conflict trajectory optimization is compared and analyzed. The simulation results show that the proposed method can resolve 93.74% of potential conflicts. Compared with conflict-free trajectory optimization, its optimization scheme exhibits less air traffic complexity fluctuations when facing environmental perturbations such as waypoint waiting and area bans. By adjusting 21.11% of the flights, it can reduce the average complexity of each time period by 24.98% on average, and the overall average complexity of the whole day is reduced from 120.52 to 72.82.

To solve the problem of autonomous path planning for aircraft, this article proposes a game coordination method for aircraft autonomous conflict resolution, and discusses the game efficiency of this method under various game strategy settings for aircraft. First, based on evolutionary Game theory, a game model of aircraft conflict resolution is constructed. By calculating the replication dynamic equation between adjacent game rounds, the aircraft iterative evolution of its game selection preference is promoted, and the game system is accelerated to reach Partial equilibrium; Construct a Jacobian matrix and determinant to analyze the stability of each Partial equilibrium solution in the game, and prove that there is one and only one Partial equilibrium solution in the game system is stable, and all aircraft participating in the game will tend to this equilibrium solution; Conduct simulation experiments using ZSSSAR01 sector airspace data, set multiple aircraft game strategies, and analyze the game time required for each strategy to reach an equilibrium state. The simulation results show that rational gamers have high game efficiency and can reach equilibrium on average within 5.31 rounds of the game; Radical and conservative gamers will accelerate and slow down the equilibrium process of the game, respectively; Non cooperative gamers will significantly slow down the game equilibrium process, requiring an average of 110.53 rounds of gaming. The operating cost compensation strategy based on non-cooperative gamers will accelerate this process (with an average of 86.87 rounds).

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.

With the gradual development of the aircraft self-separation operation and the continuous climbing operation (CCO) mode, it can effectively solve the problem that the departure path of aircraft in the current terminal area is fixed and single, which leads to the low operational efficiency of airspace. Therefore, an autonomous path planning method based on artificial potential field-particle swarm optimization（APF-PSO） algorithm was proposed in this paper. To guarantee operation safety, the airspace environment was first rasterized, the aircraft autonomous operation mode was taken into consideration, and the airspace complexity of each grid was computed. This prevented departing aircraft from flying into high-complexity grids. The aircraft climbing performance constraint model was constructed based on the BADA database and reduced force climbing mode. Then the path planning was carried out by using the APF-PSO algorithm of artificial potential field（APF） and particle swarm optimization（PSO） algorithm, and the local extremum-target unreachable problem inherent in the artificial potential field method was solved by using the region search algorithm of particle swarm optimization. The Bessel curve method was used to optimize the path planning and the concept of sliding time window was introduced to optimize the departure time of aircraft. Finally, using the actual structure and operation data of Shanghai terminal airspace, the proposed method was applied to simulate. The simulation test results show that the APF-PSO algorithm can effectively generate the aircraft conflict-free departure path and avoid busy airspace. The optimized path satisfies the aircraft climbing performance constraints and is better than the actual path (path length reduced by 23.78%, maximum turning rate reduced by 55.73%, maximum climbing rate reduced by 9.94%). Additionally, the autonomous operation mode of departing aircraft results in a more balanced airspace operation condition than the actual operating mode (a reduction of 3.92% in peak grid complexity), which can significantly increase the airspace utilization rate.