Application of merging optimization to an arrival manager algorithm considering trajectory-based operations
Application of merging optimization to an arrival manager algorithm considering trajectory-based operations
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DOI：
10.1016/j.trc.2019.09.015
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发表时间：
2019-12-01
影响因子：
8.3
通讯作者：
Toratani, Daichi
中科院分区：
文献类型：
--
作者：
Toratani, Daichi
到达管理员(AMAN)是机场附近空域的空中交通管制员的支持系统。AMAN协助空中交通管制员确保有效的到达顺序和到达时间，以确保平稳的到达交通。虽然AMAN算法已经研究了优化到达顺序的飞机排序问题，但AMAN算法还必须优化飞机轨迹，以适应基于轨迹的操作。提出了一种新的基于归并优化方法的AMAN算法。虽然合并优化方法可以同时优化到达序列和轨迹，但它不容易应用于现实的AMAN算法。因此，本文采用滚动水平法将归并优化方法应用到AMAN算法中。为了验证所提AMAN算法的性能，进行了数值仿真。实际雷达数据被用来进行真实的空中交通模拟，并对AMAN算法进行评估。为了进行比较，还对一种传统的AMAN算法进行了仿真。仿真结果表明，与传统的AMAN算法相比，提出的AMAN算法减少了所有飞机的总飞行时间和油耗。此外，提出的AMAN算法揭示了最小化飞行时间和燃油消耗之间的权衡关系。
An arrival manager (AMAN) is a support system for air traffic controllers in the airspace near an airport. The AMAN assists the air traffic controller in facilitating efficient arrival sequences and arrival times to ensure smooth arrival traffic. Although the aircraft sequencing problem that optimizes the arrival sequence has already been studied for the AMAN algorithm, the AMAN algorithm must also optimize aircraft trajectories to accommodate trajectory-based operations. This study proposes a novel AMAN algorithm based on the merging optimization method. Although the merging optimization method can simultaneously optimize the arrival sequence and trajectory, it cannot be easily applied to a realistic AMAN algorithm. Thus, the receding horizon approach is employed here to apply the merging optimization method to the AMAN algorithm. To demonstrate the performance of the proposed AMAN algorithm, numerical simulations are conducted. Actual radar data are used to conduct realistic air traffic simulations and evaluate the AMAN algorithm. A conventional AMAN algorithm is also simulated for comparison. Simulation results show that the proposed AMAN algorithm reduces both the total flight time and fuel consumption of all aircraft compared with the conventional AMAN algorithm. Furthermore, the proposed AMAN algorithm reveals the trade-off relationship between the minimization of flight time and fuel consumption.