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首页> 外文期刊>Journal of Aerospace Computing, Information, and Communication >Predicting Abnormal Runway Occupancy Times and Observing Related Precursors
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Predicting Abnormal Runway Occupancy Times and Observing Related Precursors

机译：预测跑道占用时间异常并观察相关的前兆

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摘要

Accidents on the runway triggered the development and implementation of mitigation strategies. Therefore, the airline industry is moving toward proactive risk management, which aims to identify and predict risk precursors and to mitigate risks before accidents occur. For certain predictions machine learning techniques can be used. Although many studies have explored and applied novel machine learning techniques on different radar and A-SMGCS data, the identification and prediction of abnormal runway occupancy times and the observation of related precursors are not well developed. In our previous papers, three existing methods were introduced, lasso, multi-layer perception, and neural networks, to predict the taxi-out time on the taxiway and the time to fly and true airspeed profile on final approach. This paper presents a new machine learning method where the existing machine learning techniques are combined for predicting the abnormal runway occupancy times of unique radar data patterns. Additionally the regression tree method is used in this study to observe the key related precursors extracted from the top 10 features. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conducted using final approach radar data and A-SMGCS runway data consisting of 78,321 flights at Paris Charles de Gaulle airport and were benchmarked against 500,000 flights at Vienna airport.
机译：跑道上的事故触发了缓解策略的制定和实施。因此，航空业正朝着主动风险管理的方向发展，该目标旨在识别和预测风险前兆，并在事故发生之前减轻风险。对于某些预测，可以使用机器学习技术。尽管许多研究已经在不同的雷达和A-SMGCS数据上探索并应用了新颖的机器学习技术，但是对异常跑道占用时间的识别和预测以及对相关先兆的观察尚不完善。在我们以前的论文中，介绍了三种现有的方法：套索，多层感知和神经网络，以预测滑行道上的滑行时间，最终进近时的飞行时间和真实空速曲线。本文提出了一种新的机器学习方法，该方法结合了现有的机器学习技术来预测独特雷达数据模式的异常跑道占用时间。此外，本研究使用回归树方法观察从前10个特征中提取的关键相关前体。与现有方法相比，新方法不再需要预定义的标准或领域知识。测试使用最终进场雷达数据和A-SMGCS跑道数据进行，该数据由巴黎戴高乐机场的78,321次航班组成，并以维也纳机场的500,000次航班为基准。

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