基于简化WITI 指标的机场延误预测方法

交通运输系统工程与信息 ›› 2017, Vol. 17 ›› Issue (5): 207-213.

基于简化WITI 指标的机场延误预测方法

郭野晨风，李杰，胡明华*，袁立罡

南京航空航天大学民航学院，南京211106

收稿日期:2017-03-27 修回日期:2017-06-06 出版日期:2017-10-25 发布日期:2017-10-30
作者简介:郭野晨风（1991-），男，江苏海门人，博士生.
基金资助:
国家自然科学基金/National Natural Science Foundation of China（61573181, U1333202, 51608268, 61671237）

Airport Delay Prediction Method Based on Simplified Weather Impacted Traffic Index

GUO Ye-chen-feng, LI Jie, HU Ming-hua, YUAN Li-gang

School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2017-03-27 Revised:2017-06-06 Online:2017-10-25 Published:2017-10-30

摘要/Abstract

摘要：

为实现在战略或预战术阶段对恶劣天气条件下的机场延误进行有效预测，本文引入简化的天气影响交通指标(WITI)，采用灰色关联分析的方法，验证该指标与机场延误之间的关联性，再分别以WITI指标和传统指标构建多元线性回归模型和BP神经网络预测模型，对广州白云国际机场(ZGGG)和深圳宝安国际机场(ZGSZ)的离场延误进行预测.结果显示，WITI 指标与机场离场延误之间的关联度明显高于传统指标，基于WITI 指标比基于传统指标构建的多元线性回归模型，在预测准确度上高出14.09%(ZGGG)和 9.79%(ZGSZ)，同样在BP神经网络模型中则高出8.00%(ZGGG)和6.41%(ZGSZ)，由此认为WITI指标在机场延误预测中具有更好的应用效果.

关键词: 航空运输, 机场延误预测, WITI指标, 机场延误, 灰色关联分析, 多元线性回归模型, BP神经网络预测模型

Abstract:

This paper aims at predicting airport delay accurately in the strategic or pre- tactical stage. We introduce the simplified Weather Impacted Traffic Index (WITI), and use grey incidence analysis method to verify the correlation between the WITI and actual airport delay. Then we use WITIs and traditional indexes to develop multiple linear regression model and BP neural network predictive model respectively, and we chose the data of Guangzhou Baiyun International Airport (ZGGG) and Shenzhen Baoan International Airport (ZGSZ) as research samples. According to the result, WITIs are more closely related to airport departure delay than traditional indexes. Furthermore, the predicted accuracy of the multiple linear regression model based on WITIs increases 14.09% (ZGGG) and 9.79% (ZGSZ), compared with the model based on traditional indexes. When the multiple linear regression model was replaced by the BP neural network predictive model, the results increase 8.00% (ZGGG) and 6.41% (ZGSZ) as well. Thus the WITI has a comparatively satisfactory feedback in airport delay forecast.

Key words: air transportation, airport delay prediction, WITI, airport delay, grey incidence analysis, multiple linear regression model, BP neural network predictive model

中图分类号:

郭野晨风，李杰，胡明华，袁立罡. 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 Ye-chen-feng, LI Jie, HU Ming-hua, YUAN Li-gang. Airport Delay Prediction Method Based on Simplified Weather Impacted Traffic Index[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(5): 207-213.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19505.shtml

http://www.tseit.org.cn/CN/Y2017/V17/I5/207

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