Airport delay prediction model based on regional residual and LSTM network

Jingyi QU; Meng YE; Xing QU

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

您当前的位置：

首页 >

文章列表页 >

Airport delay prediction model based on regional residual and LSTM network

Papers | 更新时间：2024-06-05

- Airport delay prediction model based on regional residual and LSTM network
- Journal on Communications Vol. 40, Issue 4, Pages: 149-159(2019)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(U1833105);The Open Foundation of Tianjin Key Laboratory of Intelligent Signal and Image Processing(2017ASP-TJ01);The Fundamental Research Fund for the Central Universities(3122018D006)
- DOI：10.11959/j.issn.1000-436x.2019091
  CLC： TP183
- Online First：2019-04，
  
  Published：25 April 2019
- 稿件说明：
移动端阅览
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：

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

references

吴薇薇 , 孟亭婷 , 张皓瑜 . 基于机场延误预测的航班计划优化研究 [J ] . 交通运输系统工程与信息 , 2016 , 16 ( 6 ): 189 - 195 .

WU W W , MENG T T , ZHANG H Y . Flight plan optimization based on airport delay prediction [J ] . Journal of Transportation Systems En-gineering and Information Technology , 2016 , 16 ( 6 ): 189 - 195 .

NAYAK N , ZHANG Y . Estimation and comparison of impact of single airport delay on national airspace system with multivariate simultaneous models [J ] . Transportation Research Record , 2011 , 2206 ( 1 ): 52 - 60 .

EADS G , KIEFER M , MEHNDIRATTA S . Short-term delay mitigation strategies for san francisco international airport [J ] . Transportation Research Record Journal of the Transportation Research Board , 2001 , 1744 ( 1 ): 44 - 51 .

韩淑敏 . 机场运行可预测性分析与优化 [D ] . 天津:中国民航大学 , 2016 .

HAN S M . Analysis and optimization of the airport operational pre-dictability [D ] . Tianjing:Civil Aviation University of China , 2016 .

罗谦 , 张永辉 , 程华 , 等 . 基于航空信息网络的枢纽机场航班延误预测模型 [J ] . 系统工程理论与实践 , 2014 , 34 ( S1 ): 143 - 150 .

LUO Q , ZHANG Y H , CHENG H , et al . Study on flight delay predic-tion model based on flight networks [J ] . Systems Engineer-ing-Theory＆Practice , 2014 , 34 ( S1 ): 143 - 150 .

DEUTSCHMANN A , . Prediction of airport delays based on non-linear considerations of airport systems [C ] // The 28th International Congress of the Aeronautical Sciences . 2012 : 1 - 5 .

徐涛 , 丁建立 , 顾彬 , 等 . 基于增量式排列支持向量机的机场航班延误预警 [J ] . 航空学报 , 2009 , 30 ( 7 ): 1256 - 1263 .

XU T , DING J L , GU B , et al . Forecast warning level of flight delays based on incremental ranking support vector machine [J ] . Acta Aeronautica et Astronautica Sinica , 2009 , 30 ( 7 ): 1256 - 1263 .

张静 , 徐肖豪 , 王飞 , 等 . 基于模糊线性回归模型的机场延误性能评估 [J ] . 交通运输工程学报 , 2010 , 10 ( 4 ): 109 - 114 .

ZHANG J , XU X H , WANG F , et al . Airport delay performance evalu-ation based on fuzzy linear regression model [J ] . Journal of Traffic and Transportation Engineering , 2010 , 10 ( 4 ): 109 - 114 .

MUKHERJEE A , GRABBE S , SRIDHAR B . Predicting ground delay program at an airport based on meteorological conditions [C ] // The 14th AIAA Aviation Technology,Integration,and Operations Conference . 2014 .

郭野晨风 , 李杰 , 胡明华 , 等 . 基于简化WITI指标的机场延误预测方法 [J ] . 交通运输系统工程与信息 , 2017 , 17 ( 5 ): 207 - 213 .

GUO Y C F , LI J , HU M H , et al . Airport delay prediction method based on simplified WITI index [J ] . Journal of Transportation Systems Engineering and Information Technology , 2017 , 17 ( 5 ): 207 - 213 .

NOBORU T , RYOSUKE K , AKIHIDE S , et al . Prediction of delay due to air traffic control by machine learning [C ] // AIAA Modeling and Simulation Technologies Conference . 2017 : 191 - 199

BASPINAR B , URE N K , KOYUNCU E , et al . Analysis of delay characteristics of european air traffic through a data-driven airport-centric queuing network model [J ] . IFAC-PapersOnLine , 2016 , 49 ( 3 ): 359 - 364 .

KIM Y J , CHOI S , BRICENO S , et al . A deep learning approach to flight delay prediction [C ] // The 35th Digital Avionics Systems Conference . 2016 : 67 - 72 .

KHANMOHAMMADI S , TUTUN S , KUCUK Y . A new multilevel input layer artificial neural network for predicting flight delays at JFK airport [J ] . Procedia Computer Science , 2016 , 95 : 237 - 244 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 .

TSOI C A , SHAOHUA T . Recurrent neural networks:a constructive algorithm,and its properties [J ] . Neuro computing , 1997 , 15 ( 3-4 ): 309 - 326 .

HOCHREITER S , SCHMMIDHUBER J . Long short-term memory [J ] . Neural Computation , 1997 , 9 ( 8 ): 1735 - 1780 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // The 26th IEEE Conference on Computer Vision and Pattern Recognition . 2016 : 770 - 778 .

WANG J , YANG Y , MAO J , et al . CNN-RNN:a unified framework for multi-label image classication [C ] // The 29th IEEE Conference on Computer Vision and Pattern Recognition . 2016 : 2285 - 2294 .

IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift [C ] // The 32nd International Conference on Machine Learning . 2015 : 448 - 456 .

NAIR V , HINTON G E . Rectified linear units improve restricted boltzmann machines [C ] // The 27th International Conference on Machine Learning . 2010 : 807 - 814 .

屈景怡 , 朱威 , 吴仁彪 . 基于衰减因子的双通道神经网络图像分类算法 [J ] . 系统工程与电子技术 , 2017 , 39 ( 6 ): 1391 - 1399 .

QU J Y , ZHU W , WU R B . Image classification for dual-channel neural networks based on attenuation factor [J ] . Systems Engineering and Electronics , 2017 , 39 ( 6 ): 1391 - 1399 .

黄文坚 , 唐源 . TensorFlow 实战 [M ] . 北京 : 电子工业出版社 , 2017 .

HUANG W J , TANG Y . TensorFlow practice [M ] . Beijing : Publishing House of Electronics IndustryPress , 2017 .

吴仁彪 , 李佳怡 , 屈景怡 . 基于双通道卷积神经网络的航班延误预测模型 [J ] . 计算机应用 , 2018 , 38 ( 7 ): 2100 - 2112 .

WU R B , LI J Y , QU J Y . Flight delay prediction based on du-al-channel convolutional neural networks [J ] . Journal of Computer Ap-plications , 2018 , 38 ( 7 ): 2100 - 2112 .

MICCI-BARRECA D . A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems [J ] . ACM SIGKDD Explorations Newsletter , 2001 , 3 ( 1 ): 27 - 32 .

DUAN K , KEERTHI S S , CHU W , et al . Multi-category classification by soft-max combination of binary classifiers [C ] // The International workshop on multiple classifier systems . 2003 : 125 - 134 .

Views

2202

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Unsupervised dimensionality reduction method for multivariate time series based on global and local scatter

Fast panoramic image stitching algorithm based on parameter regression

Deep image semantic communication model for 6G

DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network

Review on autoencoder and its application

Related Author

Gang HU

Fengming ZHANG

Xiaofeng ZHANG

Yongmei ZHAO

Zhengxin LI

Jin TANG

Wentao LIU

Xiaohu LI

Related Institution

School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University

Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University

School of Automation, Central South University

Xiang Jiang Laboratory

School of Information Science and Engineering, Hunan Normal University

AI问答

⁰