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Predicting the operational acceptance of airborne flight reroute requests using data mining

机译：使用数据挖掘来预测机载航班改航请求的运营接受度

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For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.
机译：对于产生更有效的飞行路线或路线变更咨询的工具，重要的是确保自动化和自主决策与人类目标的兼容性，以确保人类操作员的可接受性。在本文中，作者开发了一种概念证明，可以预测飞行过程中航线变更的操作可接受性。这种功能可能会在自动化工具中得到应用，这些工​​具可以识别出受天气或拥挤影响的空域周围更有效的航线，并更好地满足航空公司的偏好。该预测器基于将逻辑回归，决策树，支持向量机，随机森林和Adaptive Boost等数据挖掘技术应用于运营和现场实验期间报告的历史飞行计划修正数据的基础。交叉验证用于模型开发，而嵌套交叉验证用于验证模型。使用沃思堡空中交通管制中心及其相邻中心的可用数据，该模型在预测空中交通管制员接受或拒绝航线变更方面具有最佳性能，其随机森林为F值0.77。该结果表明，重路由请求的操作接受确实具有一定程度的可预测性，并且可以使用适当的数据训练模型以预测重路由请求的操作可接受性。此类模型最终可用于通过决策支持工具通知路线选择，从而有助于开发越来越自治的系统，该系统能够以比目前更少的人工输入来路由飞机。

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