[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]
点击复制

基于组合输入ES-GA-BP的中欧班列货运量预测()
分享到:

《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12杨蕙瑜12雷定猷12
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 Yinggui12YANG Huiyu12and LEI Dingyou12
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:

[1] 王菲. 面向“一带一路”的国际铁路通道布局研究[J]. 铁道运输与经济,2018,40(4):13-17.
WANG Fei. A study on the general layout of an international railway channel along "One Belt and One Road" [J]. Railway Transport and Economy, 2018, 40(4): 13-17.(in Chinese)
[2] 冯芬玲. 基于IPSO-Capsule-NN模型的中欧班列出口需求量预测[J]. 中国铁道科学,2020,41(2):147-156.
FENG Fenling. Prediction of export demand of China Railway Express based on IPSO-Capsule-NN model [J]. China Railway Science, 2020, 41(2): 147-156.(in Chinese)
[3] 张玥,帅斌. 基于改进灰色模型的东北地区铁路货运量预测[J]. 铁道科学与工程学报,2012,9(5):125-128.
ZHANG Yue, SHUAI Bin. Railway freight volume forecast in northeast region based on the improved gray model [J]. Journal of Railway Science and Engineering, 2012, 9(5): 125-128.(in Chinese)
[4] 邵梦汝,程天伦,马晓晨. 基于灰色神经网络的铁路货运量组合预测[J]. 交通运输工程与信息学报,2016,14(3):129-135.
SHAO Mengru, CHENG Tianlun, MA Xiaochen. Combination prediction of railway freight volume based on grey model and neural network [J]. Journal of Transportation Engineering and Information, 2016, 14(3): 129-135.(in Chinese)
[5] RUIZ-AGUILAR J?J, URDA D, MOSCOSO-L?PEZ J A, et al. A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models [J]. Neurocomputing, 2019, 391: 282-291.
[6] TSIOUMAS V, PAPADIMITRIOU S, SMIRLIS Y, et al. A novel approach to forecasting the bulk freight market [J]. Neurocomputing, 2017, 33(1): 33-41.
[7] UYAR K, ILHAN?, ?LHAN A. Long term dry cargo freight rates forecasting by using recurrent fuzzy neural networks [J]. The Asian Journal of Shipping and Logistics, 2016, 102: 642-647.
[8] WANG Youan, CHEN Xumei, HANYanhui, et al. Forecast of passenger and freight traffic volume based on elasticity coefficient method and grey model [J]. Procedia-Social and Behavioral Sciences, 2013, 96: 136-147.
[9] 雷定猷,马强,徐新平,等. 基于非线性主成分分析和GA-RBF的高速公路交通量预测方法[J].交通运输工程学报,2018,18(3):210-217.
LEI Dingyou, MA Qiang, XU Xinping, et al. Forecasting method of expressway traffic volume based on NPCA and GA-RBF [J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 210-217. (in Chinese)
[10] YANG Hongjun, HU Xu. Wavelet neural network with improved genetic algorithm for traffic flow time series prediction [J]. Optik, 2016, 127(19): 8103-8110.
[11] XU Yongbin, XIE Haihong, WU Liuyi. Analysis and forecast of railway coal transportation volume based on BP neural network combined forecasting model [C]// The 6th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation (CDMMS). Busan, South Korea: AIP: 2018, 1967: 136-147.
[12] MOSCOSO-L?PEZ J A, TURIAS I J, COME M J, et al. Short-term forecasting of intermodal freight using ANNs and SVR: case of the port of Algeciras Bay [J]. Transportation Research Procedia, 2016, 18: 108-114.
[13] ZHOU Cheng, TAO Juncheng. Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network [J]. Kybernetes, 2015, 44(4): 646-666.
[14] HASSAN L A H, MAHMASSANI H S, CHEN Ying. Reinforcement learning framework for freight demand forecasting to support operational planning decisions [J]. Transportation Research Part E, 2020, 137: 1-20.
[15] YANG Yandong, YU Congzhou. Prediction models based on multivariate statistical methods and their applications for predicting railway freight volume [J]. Neurocomputing, 2015, 158: 210-215.
[16] 梁建伟.中欧班列的运行现状及建议[J]. 广东经济,2019(3):32-35.
LIANG Jianwei. Operation status and suggestions of China Europe train [J]. Guangdong Economy, 2019(3): 32-35.(in Chinese)

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