基于区域残差和LSTM网络的机场延误预测模型

屈景怡; 叶萌; 渠星

doi:10.11959/j.issn.1000-436x.2019091

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基于区域残差和LSTM网络的机场延误预测模型

学术论文 | 更新时间：2024-06-05

- 基于区域残差和LSTM网络的机场延误预测模型
- Airport delay prediction model based on regional residual and LSTM network
- 通信学报 2019年40卷第4期页码：149-159
- 作者机构：
  
  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
- 作者简介：
  
  [ "屈景怡（1978- ），女，天津人，博士，中国民航大学副教授，主要研究方向为航空运输大数据、神经网络、深度学习。" ]
  [ "叶萌（1994- ），女，河南长葛人，中国民航大学硕士生，主要研究方向为航空运输大数据、深度学习。" ]
  [ "渠星（1995- ），女，河北定州人，中国民航大学硕士生，主要研究方向为航空运输大数据、深度学习。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1833105);天津市智能信号与图像处理重点实验室开放基金项目(2017ASP-TJ01);中央高校基本科研业务费基金资助项目(3122018D006)
- DOI：10.11959/j.issn.1000-436x.2019091
  中图分类号： TP183
- 网络出版日期：2019-04，
  
  纸质出版日期：2019-04-25
- 稿件说明：
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屈景怡, 叶萌, 渠星. 基于区域残差和LSTM网络的机场延误预测模型[J]. 通信学报, 2019,40(4):149-159.

Jingyi QU, Meng YE, Xing QU. Airport delay prediction model based on regional residual and LSTM network[J]. Journal on communications, 2019, 40(4): 149-159.
屈景怡, 叶萌, 渠星. 基于区域残差和LSTM网络的机场延误预测模型[J]. 通信学报, 2019,40(4):149-159. DOI： 10.11959/j.issn.1000-436x.2019091.

Jingyi QU, Meng YE, Xing QU. Airport delay prediction model based on regional residual and LSTM network[J]. Journal on communications, 2019, 40(4): 149-159. DOI： 10.11959/j.issn.1000-436x.2019091.

摘要

针对目前民航运输业对机场延误预测高精度的要求，提出一种基于区域残差和长短时记忆（RR-LSTM）网络的机场延误预测模型。首先，将机场的属性信息、气象信息和相关运行航班信息进行融合；然后，利用RR-LSTM 网络对融合后的机场数据集进行特征提取；最后，构建 Softmax 分类器对机场延误分类预测。所提RR-LSTM网络模型既能有效提取机场延误数据的时间相关性，又能避免深层LSTM网络的梯度消失问题。实验结果表明，RR-LSTM网络模型预测准确率可达95.52%，取得了比传统网络模型更好的预测效果。其中，融合机场的气象信息和相关运行航班信息后，预测准确率可提高约11%。

Abstract

Nowadays

the civil aviation industry has a high precision requirement of airport delay prediction

so an airport delay prediction model based on the RR-LSTM network was proposed.Firstly

the airport information

meteorological information and related flight information were integrated.Then

the RR-LSTM network was used to extract the features of the fused airport data set.Finally

the Softmax classifier was adopted to classify and predict the airport delay.The proposed RR-LSTM network model can not only extract the time correlation of airport delay data effectively

but also avoid the gradient disappearance problem of deep LSTM network.The experimental results indicate that the RR-LSTM network model has a prediction accuracy of 95.52%

which achieves better prediction results than the traditional network model.The prediction accuracy can be improved about 11% by fusing the weather information and the flight information of the airport.

关键词

Keywords

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