利用特征等价性进行到达跑道占用时间预测的模型推广
Model Generalization in Arrival Runway Occupancy Time Prediction by Feature Equivalences
摘要：
通用的多机场跑道占用时间实时预测模型是目前的研究空白。本文提出了一种通用的到达跑道占用时间（AROT）的实时预测模型，该模型通过将类别特征替换为它们的数值等价来实现。三天的数据，从萨博Sensis的Aerobahn系统在三个美国机场收集，已被用于这项工作。三个基于树的机器学习算法：决策树，随机森林和梯度提升使用数值等价特征来评估模型的泛化能力。我们已经证明，在数值等价特征上训练的模型不仅具有至少与在分类特征上训练的模型相当的性能，而且还可以对来自其他机场的不可见数据进行预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
