Extraction of Speed-Control Strategy in En-Route Air Traffic using Multi- Objective Optimization and Decision Tree
Extraction of Speed-Control Strategy in En-Route Air Traffic using Multi- Objective Optimization and Decision Tree
复制标题
使用多目标优化和决策树提取航路空中交通中的速度控制策略
DOI：
10.11394/tjpnsec.13.10
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
伊藤 恵理
中科院分区：
文献类型：
--
作者：
関根 將弘;立川 智章;藤井 孝藏;伊藤 恵理
提出了一种求取空中交通管制员最优速度控制策略的新方法。未来几十年，空中交通需求预计将增长，导致大型机场运力过剩。延长到达管理(E-AMAN)正成为适应这种日益增长的需求的潜在运营理念之一。在E-AMAN中，上游ATCO指示飞行员提高或降低巡航速度，以有效减少大型机场周围的预期延误时间和燃油消耗。E-AMAN系统是根据目标区域进行设计的，因为最优速度控制取决于目标机场、空域和空中交通流量的特征。因此，本研究建立了一种分析影响速度控制的特性的方法。首先，将基于规则的模拟器和多目标优化相结合来搜索每架飞机的最优速度。从东京国际机场出发的150海里指示的减速速度被用作设计变量，以最大限度地减少起飞-入境和邮轮-入境航班的飞行时间。最后，利用获得的非支配解和速度控制过程中获得的18个局部信息，构建了两个主要路径簇的决策树。作为结果，得到了显著减少两个飞行时间的非支配解。决策树明确了特征及其阈值，这些特征及其阈值有助于每个路径簇的速度控制决策。结果表明，根据机场和空域的不同，最佳速度控制策略可能会有所不同。这项研究将有助于扩大对不同空域ATCO在速度控制方面的共同点和差异性的理解。
A new approach to extract the optimal speed-control strategy for air traffic controllers (ATCOs) is proposed. Air traffic demand is expected to grow in the next decades, causing the overcapacity of large-scale airports. Extended Arrival Management (E-AMAN) is becoming one of the potential operational concepts to accommodate this increasing demand. In E-AMAN, upstream ATCOs instruct pilots to increase or decrease cruise speed to effectively reduce the expected delay times and fuel consumption around large-scale airports. The E-AMAN system is designed according to the target area because the optimal speed-control depends on the characteristics of the target airport, airspace, and air traffic flow. Therefore, this study establishes a method to analyze the characteristics contributing to speed control. First, a rule-based simulator and multi-objective optimization are combined to search for the optimal speed for each aircraft. The deceleration speed instructed at the 150NM from Tokyo International Airport is used as the design variable to minimize the flight time of both takeoff-inflow and cruise-inflow flights. Finally, the decision trees are constructed for the two major route-clusters by using the obtained non-dominated solutions and 18 local information obtained during the speed control. As the results, the non-dominated solutions significantly reducing the both flight times are obtained. The decision trees clarify the features and their thresholds which contribute to the decision-making of speed control for each route-cluster. The results imply that the optimal speed-control strategy could vary depending on the airport and airspace. This research will contribute to expanding the understanding of common points and differences in the speed control by ATCOs with different airspaces.