Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (6): 24-32.doi: 10.16088/j.issn.1001-6600.2020102902

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

TIAN Sheng1*, LI Chengwei1, HUANG Wei2, WANG Lei2   

  1. 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

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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” HM 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

CLC Number: 

  • U492.313
[1] 王晖,贾元华. 交通量与经济量弹性关系的系统分析[J]. 交通运输系统工程与信息,2004(1):54-61. DOI:10.16097/j.cnki.1009-6744.2004.01.012.
[2] 赵鹏,李璐. 基于ARIMA模型的城市轨道交通进站量预测研究[J]. 重庆交通大学学报(自然科学版),2020,39(1):40-44. DOI:10.3969/j.issn.1674-0696.2020.01.07.
[3] XIAO M T,LI C. Fuzzy regression prediction and application based on multi-dimensional factors of freight volume[C]//2018 IOP Conference Series:Earth and Environmental Science. Bristol:IOP,2018,108(3):032071. DOI:10.1088/1755-1315/108/3/032071.
[4] 徐莉,薛锋. 基于GM(1,1)残差二次修正的铁路货运量预测[J]. 交通运输工程与信息学报,2019,17(2):44-50. DOI:10.3969/j.issn.1672-4747.2019.02.006.
[5] 李守军,李光宇,马小平. 基于马尔科夫滚动优化的锂电池寿命灰色预测研究[J]. 合肥工业大学学报(自然科学版),2019,42(6):763-769. DOI:10.3969/j.issn.1003-5060.2019.06.009.
[6] 徐森,崔舒为. 基于Softplus函数的双隐含层BP神经网络的公路客运量预测[J]. 南华大学学报(自然科学版), 2020, 34(1):88-92. DOI:10.19431/j.cnki.1673-0062.2020.01.014.
[7] GUO Z D,FU J Y. Prediction method of railway freight volume based on genetic algorithm improved general regression neural network[J]. Journal of Intelligent Systems,2018,27(2):291-302. DOI:10.1515/jisys-2016-0172.
[8] POLSON N G,SOKOLOV V O. Deep learning for short-term traffic flow prediction[J]. Transportation Research Part C: Emerging Technologies,2017,79:1-17. DOI:10.1016/j.trc.2017.02.024.
[9] YANG D,CHEN K R,YANG M N,et al. Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features[J]. IET Intelligent Transport Systems,2019,13(10):1475-1482. DOI:10.1049/iet-its.2018.5511.
[10] YIN S,JIANG Y C,TIAN Y,et al. A data-driven fuzzy information granulation approach for freight volume forecasting[J]. IEEE Transactions on Industrial Electronics,2017,64(2):1447-1456. DOI:10.1109/TIE.2016.2613974.
[11] CHENG M L,SHI G J,HAN Y. An extended grey model GM(1,1,exp(bk)) and its application in Chinese civil air passenger volume prediction[J]. Journal of Systems Science and Information,2019,7(5):486-496. DOI:10.21078/JSSI-2019-486-11.
[12] ZHAO Y,FAN Z Z,ZHAO C. Combined forecasting model of water traffic accidents based on gray-BP neural network[C]//2019 4th International Conference on Intelligent Transportation Engineering (ICITE). Piscataway,NJ:IEEE,2019:34-38. DOI:10.1109/ICITE.2019.8880206.
[13] GUO Z Q,ZHAO X,CHEN Y X,et al. Short-term passenger flow forecast of urban rail transit based on GPR and KRR[J]. IET Intelligent Transport Systems,2019:1-9. DOI:10.1049/iet-its.2018.5530.
[14] 赵怀鑫,孙星星,徐倩倩,等.基于灰熵法的公路货运量和货物周转量关联因素分析[J].交通运输工程学报,2018,18(4):160-170. DOI:10.19818/j.cnki.1671-1637.2018.04.017.
[15] 谢超. 基于主成分分析和极限学习机的公路货运量预测研究[J]. 交通与运输(学术版),2018(1):64-67.
[16] DONG J Y,WANG S P. Max-min and min-max gray association degree-based method for multiattribute decision making[J]. Journal of Intelligent Systems,2015,24(4):525-531. DOI:10.1515/jisys-2014-0091.
[17] 郑丽霞,林芳,杨善朝. 学科领域高被引与高下载论文的灰色关联分析:以概率论与数理统计学科为例[J]. 广西师范大学学报(自然科学版),2018,36(4):76-83. DOI:10.16088/j.issn.1001-6600.2018.04.010.
[18] ZENG B,DUAN H M,ZHOU Y F. A new multivariable grey prediction model with structure compatibility[J]. Applied Mathematical Modelling,2019,75:385-397. DOI:10.1016/j.apm.2019.05.044.
[19] LV MM,WANG L,HOU Y L,et al. Mean shift tracker with grey prediction for visual object tracking[J]. Canadian Journal of Electrical and Computer Engineering,2018,41(4):172-178.
[20] 杨程,郑兰香,李春光. 基于灰色模型与三次样条插值的预测研究[J]. 湖北农业科学,2015,54(22):5729-5731. DOI:10.14088/j.cnki.issn0439-8114.2015.22.061.
[21] 许伦辉,陈凯勋. 基于改进萤火虫算法优化BP神经网络的路网速度分布预测[J]. 广西师范大学学报(自然科学版),2019,37(2):1-8. DOI:10.16088/j.issn.1001-6600.2019.02.001.
[22] WU J,LI Z B,ZHU L,et al. Optimized BP neural network for dissolved oxygen prediction[J]. IFAC-PapersOnLine, 2018,51(17):596-601. DOI:10.1016/j.ifacol.2018.08.132.
[23] YANG H,LI X Q,WU B. Scale forecast method for regional highway network based on BPNN-MOP[J]. Transportation Research Procedia,2017,25:3840-3854. DOI:10.1016/j.trpro.2017.05.284.
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