赵娅倩1,
屈景怡1,,,
高爱国2,
陈文秀2
1.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
2.中国民用航空华东地区空中交通管理局 上海 200335
基金项目:国家自然科学基金联合基金(U1833105),天津市自然科学基金(19JCYBJC15900)
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
赵娅倩: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
高爱国:男,1965年生,教授级高级工程师,硕士,研究方向为空中交通控制技术和运行
陈文秀:女,1970年生,高级工程师,硕士,研究方向为空管自动化系统
通讯作者:屈景怡 qujingyicauc@163.com
中图分类号:TN911.7; TP391.4计量
文章访问数:1000
HTML全文浏览量:466
PDF下载量:73
被引次数:0
出版历程
收稿日期:2019-10-16
修回日期:2020-06-17
网络出版日期:2020-07-19
刊出日期:2021-01-15
Flight Delay Propagation Prediction Model Based on CBAM-CondenseNet
Renbiao WU1,Yaqian ZHAO1,
Jingyi QU1,,,
Aiguo GAO2,
Wenxiu CHEN2
1. Tianjin Key Laboratory of Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
2. East China Regional Administration, Civil Aviation Administration of China, Shanghai 200335, China
Funds:The National Natural Science Foundation of China (U1833105), The Tianjin Natural Science Foundation(19JCYBJC15900)
摘要
摘要:针对航班延误衍生的航班延误波及问题,该文提出一种基于CBAM-CondenseNet的航班延误波及预测模型。首先,通过分析航班延误在航空网络内产生的延误波及现象,确定会受前序延误航班影响的航班链;其次,对选定的航班链数据进行清洗,将航班信息与机场信息进行数据融合;最后,提出改进的CBAM-CondenseNet算法对融合后的数据进行特征提取,构建Softmax分类器对首班离港航班延误波及的后续离港航班延误等级进行预测。该文提出的CBAM-CondenseNet算法融合了CondenseNet和CBAM的优势,采用通道和空间注意力机制来加强网络结构深层信息的传递。实验结果表明,算法改进后有效提升网络性能,预测准确率可达97.55%。
关键词:航班延误波及预测/
CBAM-CondenseNet/
数据融合/
注意力机制
Abstract:For the problem of flight delay propagation caused by flight delay, a flight delay wave prediction model based on CBAM-CondenseNet is presented. Firstly, by analyzing the delays propagation in the aviation network caused by flight delays, the flight chain affected by the pre-order delays is determined; Secondly, the selected flight chain data is cleaned and the flight information and airport information are fused; Finally, an improved CBAM-CondenseNet algorithm is proposed to extract the number of fused flights. According to feature extraction, a Softmax classifier is constructed to predict the delays of the first departure flights and the subsequent flights. The CBAM-CondenseNet algorithm proposed in this paper combines the advantages of CondenseNet and CBAM, and uses channel and spatial attention mechanism to enhance the transmission of deep information in network structure. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 97.55%.
Key words:Flight delay propagation prediction/
CBAM-CondenseNet/
Data fusion/
Attention mechanism
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