疫情下基于GC-rBPNN模型的公路货运量预测方法

doi:10.16088/j.issn.1001-6600.2020102902

广西师范大学学报（自然科学版） ›› 2021, Vol. 39 ›› Issue (6): 24-32.doi: 10.16088/j.issn.1001-6600.2020102902

疫情下基于GC-rBPNN模型的公路货运量预测方法

田晟^1*, 李成伟¹, 黄伟², 王蕾²

1.华南理工大学土木与交通学院, 广东广州 510640;
2.广州市交通设计研究院有限公司, 广东广州 511430

收稿日期:2020-10-29 修回日期:2020-12-13 出版日期:2021-11-25 发布日期:2021-12-08
通讯作者: 田晟(1969—), 男, 江西九江人, 华南理工大学副教授, 博士。E-mail: shitian1@scut.edu.cn
基金资助:
国家留学基金(20170615503); 广东省科技计划(2015A080803001); 广东省自然科学基金(2020A1515010382)

Forecasting Method of Highway Freight Volume Based on GC-rBPNN Model during COVID-19 Epidemic

TIAN Sheng^1*, LI Chengwei¹, HUANG Wei², WANG Lei²

1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Guangdong 510640, China;
2. Guangzhou Transportation Design & Research Institute Co., Ltd., Guangzhou Guangdong 511430, China

Received:2020-10-29 Revised:2020-12-13 Online:2021-11-25 Published:2021-12-08

摘要/Abstract

摘要： 新冠疫情期间,公路货运量明显下滑,公路运营状况变化复杂,亟须科学预测公路货运量。通过灰色关联分析,确定疫情期间公路货运量主要影响因素,构建了基于灰色组合(GC)-修正BP神经网络(rBPNN)模型的公路货运量预测方法。以我国2017年7月—2020年5月的公路货运量统计数据为原始数据,对BP神经网络进行训练和检验,并引入修正系数H_M对预测结果进行修正。以疫情期间近5个月数据为基础,用灰色组合模型预测下月公路货运量各主要影响因素值,再运用修正BP神经网络预测我国2020年6月的公路货运量。将GC-rBPNN模型与其他预测方法进行对比分析,GC-rBPNN模型的PE 和MAPE分别为0.21%和3.21%,结果表明,GC-rBPNN模型的预测精度更高,有一定的可行性和有效性。

关键词: 公路货运量, 疫情, 灰色关联度, BP神经网络, 组合预测模型

Abstract: Under the COVID-19 epidemic situation, the volume of road freight has declined significantly, and road operations have changed complexly. It is urgent to scientifically predict the volume of road freight. Through gray correlation analysis, the main factors affecting road freight volume during the epidemic period are determined, and a road freight volume forecast method based on the gray combination (GC)-revised BP neural network (rBPNN) model is constructed. The BP neural network is trained and tested based on the statistical data of China’s road freight volume from July 2017 to May 2020 as the original data, and the “correction coefficient” H_M is introduced to modify the predicting result. Based on the data of the past five months during the epidemic, the gray combined model is used to predict the value of the main factors affecting the road freight volume in the next month, and the BP neural network is used to predict China’s road freight volume in June 2020. Compared the GC-rBPNN model with other prediction methods, the PE and MAPE of the GC-rBPNN model are 0.21% and 3.21%, respectively. The results show that the prediction accuracy of the GC-rBPNN model is higher, and the method has certain feasibility and effectiveness.

Key words: highway freight volume, epidemic situation, grey correlation degree, BP neural network, combined forecasting model

中图分类号:

U492.313

田晟, 李成伟, 黄伟, 王蕾. 疫情下基于GC-rBPNN模型的公路货运量预测方法[J]. 广西师范大学学报（自然科学版）, 2021, 39(6): 24-32.

TIAN Sheng, LI Chengwei, HUANG Wei, WANG Lei. Forecasting Method of Highway Freight Volume Based on GC-rBPNN Model during COVID-19 Epidemic[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(6): 24-32.

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疫情下基于GC-rBPNN模型的公路货运量预测方法

Forecasting Method of Highway Freight Volume Based on GC-rBPNN Model during COVID-19 Epidemic

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