Aircraft taxi time prediction: Comparisons and insights
Aircraft taxi time prediction: Comparisons and insights
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DOI：
10.1016/j.asoc.2013.10.004
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Ravizza;Jun Chen;J. Atkin;Paul Stewart;E. Burke
中科院分区：
文献类型：
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作者：
S. Ravizza;Jun Chen;J. Atkin;Paul Stewart;E. Burke
航空运输的预期增长，以及欧盟委员会提出的在1分钟内实现航班准点率的雄心勃勃的目标，使得机场高效和可预测的地面作业不可或缺。准确预测到达和起飞的滑行时间是跑道排序、登机口分配和地面运动本身的重要关键任务。本研究测试了不同的统计回归方法，以及属于软计算领域的各种回归方法，以更准确地预测出租车时间。来自欧洲两个主要机场的历史数据被用于交叉验证。详细的比较表明，基于TSK模糊规则的系统在预测精度方面优于其他方法。然后提出了这种方法的见解，重点分析了打车时间，这在文献中很少讨论。这项研究的目的是释放软计算方法的力量，特别是基于模糊规则的系统，来解决出租车时间预测问题。此外，我们的目标是表明，虽然这些方法只是最近才应用于机场问题，但它们为此类问题提供了有希望和潜在的特征。
The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1 min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.