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A Prediction Method based on Neural Network for Flight Turnaround Time at Airport

机译:基于机场飞行周转时间的神经网络预测方法

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The present flight on-time performance is continuously deteriorating. During the flight turnaround at a given airport, the delay of former flight will affect its subsequence flight and even be propagated to other flights in the airport, which counts against the normal operation of airport. To effectively control the influence, prediction of flight turnaround time at the airport has become an urgent problem to be addressed. To predict a reasonable turnaround time for flights, which have insufficient turnaround time in the airport, this paper firstly analyzes the critical factors that affect the flight turnaround time quantitatively and qualitatively, and then establishes a flight turnaround time prediction model based on neural network. The established model is validated through the case study using real data set collected from Beijing Capital International Airport.
机译:目前的航班准时性能不断恶化。在特定机场的出现周转过程中,前航班的延误将影响其随后飞行,甚至宣传到机场的其他航班,这与机场的正常运营相比。为了有效控制影响,机场飞行周转时间的预测已成为亟待解决的问题。为了预测机场上周转时间不足的航班的合理周转时间,本文首先分析了定量和定性地影响飞行周转时间的关键因素,然后建立基于神经网络的飞行周转时间预测模型。通过使用从北京资本国际机场收集的真实数据集进行案例研究,验证了建立的模型。

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