Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options
Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options
复制标题
使用机器学习动态生成操作上可接受的战略改道选项
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Paul U. Lee
中科院分区：
文献类型：
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作者：
A. Evans;Paul U. Lee
新开发的轨迹选择集（TOS）是由飞行运营商提交的一组优先加权备选路线，是美国交通流量管理系统中的一项功能，可实现飞行运营商和空中导航服务提供商之间的自动轨迹协商。本文的目的是描述和演示一种自动生成起飞前和机载TOSs的方法，该方法具有高的操作接受概率。该方法使用历史路由数据的分层聚类来识别候选路由。然后，使用有监督机器学习算法对历史飞行计划修正数据进行训练的预测器来估计操作接受的概率，从而为TOS选择具有最高操作接受概率的路线。所使用的特征描述了历史航线的使用情况、飞行时间的差异以及下游需求对容量不平衡的影响。随机森林是学习操作可接受性的最佳算法，模型精度为0.96。从达拉斯/沃斯堡国际机场到纽瓦克自由国际机场的历史性出发前航班演示了这种方法。
—The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.