[1]张英贵,杨蕙瑜,雷定猷.基于组合输入ES-GA-BP的中欧班列货运量预测[J].深圳大学学报理工版,2022,39(2):168-176.[doi:10.3724/SP.J.1249.2022.02168]
　ZHANG Yinggui,YANG Huiyu,and LEI Dingyou.Freight volume forecast of China Railway Express based on ES-GA-BP with combined input[J].Journal of Shenzhen University Science and Engineering,2022,39(2):168-176.[doi:10.3724/SP.J.1249.2022.02168]
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基于组合输入ES-GA-BP的中欧班列货运量预测()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第2期
页码:
168-176
栏目:
交通物流
出版日期:
2022-03-15

文章信息/Info

Title:
Freight volume forecast of China Railway Express based on ES-GA-BP with combined input
文章编号:
202202008
作者:
张英贵1; 2; 杨蕙瑜1; 2; 雷定猷1; 2
1）中南大学交通运输工程学院，湖南长沙 410075
2）中南大学轨道交通大数据湖南省重点实验室，湖南长沙 410075
Author(s):
ZHANG Yinggui1; 2; YANG Huiyu1; 2; and LEI Dingyou1; 2
1) School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan Province, P. R. China
2) Rail Data Research and Application Key Laboratory of Hunan Province, Central South University, Changsha 410075, Hunan Province, P. R. China
关键词:
铁路运输管理; 中欧班列; 指数平滑-遗传算法-反向传播神经网络; 组合预测; 货运量; 一带一路
Keywords:
railway transportation management;  China Railway Express;  exponential smoothing and genetic algorithm-optimized back propagation (ES-GA-BP) neural network;  combined forecasting;  freight volume;  the Belt and Road
分类号:
U294.13
DOI:
10.3724/SP.J.1249.2022.02168
文献标志码:
A
摘要:
中欧班列是推动“一带一路”倡议的重要支撑，科学合理地预测班列需求，对中欧班列运输方案的制定具有重要意义．以中欧班列铁路货运量预测为对象，统筹考虑中欧班列货运量的波动性和影响因素，提出一种基于组合输入指数平滑-遗传算法-反向传播（ES-GA-BP）神经网络的货运量预测方法．分析中欧班列货运现状，选取相关性高的影响因素作为神经网络输入；采用指数平滑法对波动较大的中欧班列货运量历史数据进行单项拟合预测，以优化神经网络输入；利用遗传算法优化反向传播神经网络参数，进一步提高预测精度；以“湘欧快线”国际运输通道货运集装箱数量预测为实例，验证方法的有效性．结果表明，组合输入ES-GA-BP方法适于解决波动较大的货运量预测问题，预测精度较好，有助于制定合理的中欧班列运输方案．
Abstract:
China Railway Express is an important support that promotes the Belt and Road Initiative. Scientific and reasonable forecast of the train demand is of great significance to the formulation of China Railway Express transportation scheme. Taking the forecast of freight volume of China Railway Express as an object, considering the fluctuation and influence factors, we propose a freight volume forecasting method based on exponential smoothing, genetic algorithm and optimized back propagation (ES-GA-BP) neural network with combined input. Firstly, we analyze the current status of China Railway Express freight transport, and select influencing factors with high correlation as the input of neural network. Then, the exponential smoothing method is used to fit and forecast the historical data of China Railway Express freight volume, so as to optimize the input of neural network. Genetic algorithm is used to optimize the parameters of back propagation neural network to further improve the prediction accuracy. Finally, the prediction of freight container numbers in the international transport channel of ’Hunan Europe Express’ is taken as an example to verify the effectiveness of the method. The calculation results show that the combined input ES-GA-BP method is suitable for solving the problem of freight volume forecasting with large fluctuation, and the prediction accuracy is good, which is conducive to the formulation of a reasonable China Railway Express transportation scheme.

参考文献/References:

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备注/Memo

备注/Memo:
Received: 2020-10-12; Accepted: 2021-01-21; Online (CNKI): 2021-05-17
Foundation: National Natural Science Foundation of China (71971220,71771218); Natural Science Foundation of Hunan Province (2019JJ50829); Outstanding Youth Project of Hunan Provincial Department of Education (20B597)
Corresponding author: Professor ZHANG Yinggui. E-mail: ygzhang@csu.edu.cn
Citation: ZHANG Yinggui, YANG Huiyu, LEI Dingyou. Freight volume forecast of China Railway Express based on ES-GA-BP with combined input [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(2): 168-176.(in Chinese)
基金项目：国家自然科学基金资助项目（71971220，71771218）；湖南省自然科学基金资助项目（2019JJ50829）；湖南省教育厅优秀青年基金资助项目（20B597）
作者简介：张英贵（1984—），中南大学教授、博士生导师. 研究方向：交通运输运营管理及信息化、运输组织.E-mail: ygzhang@csu.edu.cn
引 文：张英贵，杨蕙瑜，雷定猷.基于组合输入ES-GA-BP的中欧班列货运量预测［J］.深圳大学学报理工版，2022，39（2）：168-176.
更新日期/Last Update: 2022-03-30