Aircraft taxi time prediction: Feature importance and their implications
Aircraft taxi time prediction: Feature importance and their implications
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DOI：
10.1016/j.trc.2020.102892
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Xinwei Wang;A. Brownlee;J. Woodward;Michal Weiszer;M. Mahfouf;Jun Chen
中科院分区：
文献类型：
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作者：
Xinwei Wang;A. Brownlee;J. Woodward;Michal Weiszer;M. Mahfouf;Jun Chen
滑行仍然是许多机场的主要瓶颈。最近，已经提出了几种为滑行飞机分配有效路线的方法。支持这些方法的路由算法依赖于对穿越滑行道各段所需时间的准确预测。许多因素都会影响出租车时间，包括所采取的路线、飞机类型、机场的运营模式、交通拥堵信息和当地天气状况。我们使用几个国际机场的真实数据，比较了多种预测模型并研究了这些特征的影响，得出了准确建模滑行时间的最重要特征的结论。我们证明，可以通过一小部分特征来实现高精度，这些特征包括所有机场中普遍重要的特征（出发/到达、距离、总转弯、平均速度和最近飞机的数量），以及特定目标机场特有的少量特征。从所有功能转移到这个小子集会导致 1、3 和 5 分钟内正确预测的运动下降不到 1 个百分点。
Taxiing remains a major bottleneck at many airports. Recently, several approaches to allocating efficient routes for taxiing aircraft have been proposed. The routing algorithms underpinning these approaches rely on accurate prediction of the time taken to traverse each segment of the taxiways. Many features impact on taxi time, including the route taken, aircraft category, operational mode of the airport, traffic congestion information, and local weather conditions. Working with real-world data for several international airports, we compare multiple prediction models and investigate the impact of these features, drawing conclusions on the most important features for accurately modelling taxi times. We show that high accuracy can be achieved with a small subset of the features consisting of those generally important across all airports (departure/arrival, distance, total turns, average speed and numbers of recent aircraft), and a small number of features specific to particular target airports. Moving from all features to this small subset results in less than a 1 percentage-point drop in movements correctly predicted within 1, 3 and 5 min.