基于机器学习的航班到达延误时间预测

Ziyu Wang, Hu Liu, Fengguo Chu
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引用次数: 0

摘要

准确的航班延误预测是完善航空公司业务的重要基础。最新的研究致力于应用机器学习和深度学习方法来预测航班延误。在之前的航班预测中,大部分的预测任务都是出发航班是否延误。本文探讨了出发延误时间对到达延误时间的影响。为了预测到达延迟,建立了双层模型来完成任务。第一层预测到达延迟级别(0-5,15分钟)。根据预测的延迟级别,将数据输入到第二层,以预测到达延迟的具体持续时间。与单方法预测延迟时间相比,双层模型具有更高的预测精度和效率。
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Flight Arrival Delay Time Prediction Based on Machine Learning
Precise flight delay prediction is an important foundation to make airline business perfect. Up-to-date research have been devoted to applying machine learning and deep learning methods to predict the flight delay. The predicted task of a majority of the previous flight prediction are whether the leaving flight is delayed. The paper explores the effect of the duration of leaving delay to the arriving delay. To predict the arriving delay, a double layer model is established to complete the mission. The first layer predict the arriving delay level(0-5,in 15 minutes). With the predicted delay level, the data is input to the second layer to predict the concrete duration of the arriving delay. Compared with a single method applied to predict the duration of delay, the double layer model can contain higher prediction accuracy and efficiency.
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