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Prediction of aircraft estimated time of arrival using machine learning methods

机译：采用机器学习方法预测飞机估计抵达时间

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摘要

In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.
机译：在该研究中，使用机器学习算法提出了对飞机估计的到达时间（ETA）的预测。准确预测ETA对于延迟和空中交通流量，跑道分配，门分配，协作决策（CDM），地面人员和设备协调以及到达序列的优化等机器学习能够从经验中学习并与弱假设或根本没有假设进行预测。在所提出的方法中，总飞行信息，轨迹数据和天气数据以各种格式获得。原始数据被转换为整理数据并插入关系数据库。为了获得培训机器学习模型的功能，将探索，清洁和转化为方便的功能。新功能也来自可用数据。随机森林和深神经网络用于培训机器学习模型。两种型号可以在出发后的平均绝对误差（MAE）的平均误差（MAE）预测，终端操纵区域（TMA）入口少于3分钟。另外，开发了Web应用程序以使用所提出的模型动态预测ETA。

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