论文标题
到达跑道占用时间预测的模型概括通过特征等价
Model Generalization in Arrival Runway Occupancy Time Prediction by Feature Equivalences
论文作者
论文摘要
多个机场的一般实时跑道占用时间预测建模是当前的研究差距。本文通过其数值等价替换分类特征来介绍了来概括到达跑道占用时间（AROT）的实时预测模型（AROT）的尝试。这项工作已使用了三天的数据，这些数据是从美国三个机场的Saab Sensis的Aerobahn系统收集的。三种基于树的机器学习算法：决策树，随机森林和梯度提升用于使用数值等效特征评估模型的普遍性。我们已经表明，经过数字等效功能训练的模型，不仅具有至少与经过分类功能训练的模型相当的表演，而且可以对其他机场的看不见数据进行预测。
General real-time runway occupancy time prediction modelling for multiple airports is a current research gap. An attempt to generalize a real-time prediction model for Arrival Runway Occupancy Time (AROT) is presented in this paper by substituting categorical features by their numerical equivalences. Three days of data, collected from Saab Sensis' Aerobahn system at three US airports, has been used for this work. Three tree-based machine learning algorithms: Decision Tree, Random Forest and Gradient Boosting are used to assess the generalizability of the model using numerical equivalent features. We have shown that the model trained on numerical equivalent features not only have performances at least on par with models trained on categorical features but also can make predictions on unseen data from other airports.
