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摘要:
为平衡基于航迹运行模式下的整体空域态势，提出一种基于空中交通复杂度的大规模航迹优化方法，并利用真实运行数据仿真验证其有效性与优化效果。基于航班间潜在交互关系构建空中交通复杂度计算模型；基于空中交通复杂度计算模型，构建符合空管运行要求的多目标大规模航迹优化模型，并提出优质基因遗传求解算法；利用2019年6月的全国航班运行数据进行基于空中交通复杂度的航迹优化仿真模拟，并将其与无冲突航迹优化进行了对比分析。仿真结果表明：所提方法可以解决93.74%的潜在冲突；与无冲突航迹优化相比，所提方法在面对航路点等待和区域禁行的环境扰动时，表现出较少的空中交通复杂度波动。通过调整21.11%航班，可使各时段平均复杂度平均下降24.98%，全天总体平均复杂度从120.52降低到72.82。
Abstract: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.
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图 9 受到扰动后不同优化方式冲突值变化
Figure 9. Conflict value variations of different optimization methods after perturbation
图 10 不同优化算法的航迹优化迭代
Figure 10. Iterative plot of trajectory optimization with different optimization algorithms
图 11 优化前后平均复杂度对比
Figure 11. Comparison of average complexity before and after optimization
图 12 优化前后PAVTU复杂度变化图
Figure 12. Plot of PAVTU complexity change before and after optimization
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