Iterative machine and deep learning approach for aviation delay prediction

V. Venkatesh, Arti Arya, Pooja Agarwal, S. Lakshmi, Sanjay Balana
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引用次数: 19

Abstract

In the aviation industry, flight arrival delays cause approximately 18 billion of loss to customers as stated in the literature. So, it becomes inevitable on the part of the aviation authorities to predict such delays and take necessary action to fix this loss for customer satisfaction. In this paper, an approach based on machine learning techniques is proposed that predicts the flight arrival delays considering input parameters ranging from distance to their corresponding weather details to make a decision of whether the specific flight is delayed or not. It makes use of neural networks and deep learning concepts to estimate flight delay. The proposed approach is tested on real world flight big dataset that gives an accuracy of 77% using deep nets and 89% using neural nets. This approach can achieve reliable prediction with respect to if flight arrival delay is to be expected or not, moving forward the use of such a model can come in handy not only for airline administrators but also the passengers who can rearrange their schedules and arrange accommodation.
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航空延误预测的迭代机与深度学习方法
如文献所述,在航空业,航班到达延误给客户造成的损失约为180亿美元。因此,航空当局不可避免地要预测这种延误,并采取必要的行动来弥补这一损失,以使客户满意。本文提出了一种基于机器学习技术的方法,该方法考虑从距离到相应天气细节的输入参数来预测航班到达延误,从而决定特定航班是否延误。它利用神经网络和深度学习的概念来估计航班延误。该方法在真实世界的飞行大数据集上进行了测试,使用深度网络的准确率为77%,使用神经网络的准确率为89%。这种方法可以可靠地预测航班到达是否会延误,推进这种模型的使用不仅对航空公司的管理人员有用,而且对乘客也有用,他们可以重新安排他们的时间表和安排住宿。
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