赵婷,
屈景怡,
中国民航大学天津市智能信号与图像处理重点实验室 ??天津 ??300300
基金项目:国家自然科学基金联合基金(U1833105)，天津市智能信号与图像处理重点实验室开放项目(2017ASP-TJ01)
详细信息
作者简介:吴仁彪：男，1966年生，教授，博士，研究方向为自适应信号处理、现代谱分析及其在雷达、卫星导航、空管中的应用
赵婷：女，1994年生，硕士生，研究方向为航空运输大数据、深度学习
屈景怡：女，1978年生，副教授，博士，研究方向为航空运输大数据、深度学习、神经网络
通讯作者:屈景怡 qujingyicauc@163.com
中图分类号:TP391计量
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被引次数:0
出版历程
收稿日期:2018-07-02
修回日期:2018-11-16
网络出版日期:2018-12-04
刊出日期:2019-06-01
Flight Delay Prediction Model Based on Deep SE-DenseNet
Renbiao WU,Ting ZHAO,
Jingyi QU,
Tianjin Key Laboratory of Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
Funds:The National Natural Science Foundation of China (U1833105), The Tianjin Key Laboratory of Advanced Signal Processing Open Project (2017ASP-TJ01)
摘要
摘要:针对目前民航运输业对航班延误高精度预测的需求，该文提出一种基于深度SE-DenseNet的航班延误预测模型。该模型首先将航班信息、相关机场延误信息和天气信息进行数据融合；其次，利用改进后的SE-DenseNet算法对融合后的航班数据集进行自动特征提取；最后，构建Softmax分类器进行航班离港延误等级的预测。该文提出的SE-DenseNet结构融合了DenseNet和SENet二者的优势，既能加强深层信息的传递，避免梯度消失，又可以实现特征提取过程中的特征重标定。实验结果表明，数据融合后，预测准确率较只考虑航班属性提高约1.8%；算法改进后可以有效提升网络性能，模型最终准确率达93.19%。
关键词:航班延误预测/
SE-DenseNet/
数据融合/
特征重标定
Abstract:Nowadays, the civil aviation industry has a high-precision prediction demand of flight delays, thus a flight delay prediction model based on the deep SE-DenseNet is proposed. Firstly, flight data, associated airport delay information and meteorological data are fused in the model. Then, the improved SE-DenseNet algorithm is used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is used to predict the delay level of flight. The proposed SE-DenseNet, combing the advantages of DenseNet and SENet, can not only enhance the transmission of deep information, avoid the problem of vanishing gradients, but also achieve feature recalibration by the feature extraction process. The results indicate that after data fusion, the accuracy of the model is improved 1.8% than only considering the characteristics of the flight itself. The improved algorithm can effectively improve the network performance. The final accuracy of the model reaches 93.19%.
Key words:Flight delay prediction/
SE-DenseNet/
Data fusion/
Feature recalibration
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