Advertisement

Cluster Computing

, Volume 22, Supplement 2, pp 4239–4248 | Cite as

Prediction model for railway freight volume with GCA-genetic algorithm-generalized neural network: empirical analysis of China

  • Pei WangEmail author
  • Xiaodong Zhang
  • Boling Han
  • Maoxiang Lang
Article
  • 147 Downloads

Abstract

Reasonable and scientific prediction for railway freight volume has an important impact on railway network planning and railway transportation resources allocation. However, to predict the future railway freight volume is complicated and difficult, because it is influenced by many factors, such as macro economy, industrial structure, and supply capacity, etc. In this paper, an improved prediction model is proposed, named as GCA-GA-GNN. GCA is short for grey correlation analysis, which was adopted to select the key factors which have great influence on railway freight volume instead of subjective factors. GNN is the main body of the prediction model, which combines grey prediction model and neural networks to take the advantages of linear and nonlinear modeling capabilities. Moreover, genetic algorithm is used in GNN to improve calculating speed. Then, the validity of the model was verified by the empirical case of China. The results of five different prediction models showed that the model proposed in this paper has faster convergence speed and higher prediction accuracy. Moreover, according to the downward trend of China railway freight volume from the year 2017 to 2020, some suggestions are proposed to reverse the downward trend and increase railway corporation’s profits.

Keywords

Railway freight volume Prediction model Grey correlation analysis Genetic algorithm Grey neural networks GCA-GA-GNN 

Notes

Acknowledgements

This study was supported by Scientific Research Project from Railway Construction Office of Huai’an, Jiangsu Province (No. T15L00290), and Projects for Science and Technology from China Railway Corporation (Nos. 2016X007-B and 2016X007-D).

Compliance with ethical standards

Conflict of interest

The authors declare that they do not have any commercial or associative interests that represent a conflict of interests in connection with this work.

References

  1. 1.
    Yang, Y.D.: Development of the regional freight transportation demand prediction models based on the regression analysis methods. Neurocomputing 158, 42–47 (2015)Google Scholar
  2. 2.
    Zhao, J.Y., Zhou, S.F., Cui, X.J., Wang, G.Q.: Predictive method of highway freight volume based on fuzzy linear regression model. J. Traff. Transp. Eng. 12(3), 80–85 (2012)Google Scholar
  3. 3.
    Xue, R., Sun, J., Chen, S.K.: Short-term bus passenger demand prediction based on time series model and interactive multiple model approach. Discrete Dynam. Nat. Soc. 2015(682390), 11 (2015)Google Scholar
  4. 4.
    Garrido, R.A., Mahmassani, H.S.: Forecasting freight transportation demand with the space-time multinomial probit model. Transp. Res. B 34(5), 403–418 (2000)Google Scholar
  5. 5.
    Zeng, B., Meng, W., Liu, S.F.: Research on prediction model of oscillatory sequence based on GM (1,1) and its application in electricity demand prediction. J. Grey Syst. 25(4), 31–40 (2013)Google Scholar
  6. 6.
    Valente, G.F.S., Mendonca, R.C.S., Pereira, J.A.M.: Artificial neural network prediction of chemical oxygen demand in dairy industry effluent treated by electrocoagulation. Sep. Purif. Technol. 132, 627–633 (2014)Google Scholar
  7. 7.
    Behboudian, S., Tabesh, M., Falahnezhad, M., Ghavanini, F.A.: A long-term prediction of domestic water demand using preprocessing in artificial neural network. J. Water Supply Res. Technol. AQUA 63, 31–42 (2014)Google Scholar
  8. 8.
    Babel, M.S., Shinde, V.R.: Identifying prominent explanatory variables for water demand prediction using artificial neural networks: a case study of Bangkok. Water Res. Manag. 25(6), 1653–1676 (2011)Google Scholar
  9. 9.
    Sun, Y., Lang, M.X., Wang, D.Z., Liu, L.Y.: Prediction models for railway freight volume based on artificial neural networks. Appl. Mech. Mater. 2014, 2093–2098 (2014)Google Scholar
  10. 10.
    Guo, Y.H., Chen, Z.Y., Feng, F.L., Chang, B.: Railway freight volume forecasting of neural network based on economic cycles. J. China Railw. Soc. 32(5), 1–6 (2010)Google Scholar
  11. 11.
    Zhao, C., Liu, K., Li, D.S.: Research on application of the support vector machine in freight volume forecast. J. China Railw. Soc. 24(4), 10–14 (2004)Google Scholar
  12. 12.
    Wei, Y., Ni, N., Liu, D.Y., Chen, H.L., Wang, M.J., Li, Q., Cui, X.J., Ye, H.P.: An improved grey wolf optimization strategy enhanced SVM and its application in predicting the second major. Math. Probl. Eng. 2017(9316713), 12 (2017)Google Scholar
  13. 13.
    Shen, Q., Liu, C.J., Zou, H.L., Zhou, S.S.: A method of image classification with optimized BP neural network by genetic algorithm. In: Proceedings of 2015 International Conference on Intelligent Networking and Collaborative Systems, pp. 123–129 (2015)Google Scholar
  14. 14.
    Prabhu, M.V., Karthikeyan, R., Shanmugaprakash, M.: Modeling and optimization by response surface methodology and neural network-genetic algorithm for decolorization of real textile dye effluent using Pleurotus ostreatus: a comparison study. Desalin. Water Treat. 57(28), 1–15 (2015)Google Scholar
  15. 15.
    Chen, B., Wu, H.: Research on grey neural network based on genetic algorithm used in the air pollution index model. In: Proceedings of 2015 International Conference on Electric, Electronic and Control Engineering (ICEECE 2015), pp. 561–565 (2015)Google Scholar
  16. 16.
    Geng, L.Y., Liang, Y.G.: Prediction of railway freight volumes based on grey adaptive particle swarm least squares support vector machine model. J. Southwest Jiaotong Univ 47(1), 144–150 (2012)Google Scholar
  17. 17.
    Al-Zahrani, M.A., Abo-Monasar, A.: Urban residential water demand prediction based on artificial neural networks and time series models. Water Res. Manag. 29(10), 3651–3662 (2015)Google Scholar
  18. 18.
    Qiu, Y., Lu, H., Wang, H.: Prediction method for regional logistics. Tsinghua Sci. Technol. 13(5), 660–668 (2008)Google Scholar
  19. 19.
    Najaf, P., Famili, S.: Application of an intelligent fuzzy regression algorithm in road freight transportation modeling. Promet-Traffic-Traffico 25(4), 311–322 (2013)Google Scholar
  20. 20.
    Young, C.C., Liu, W.C., Hsieh, W.I.: Predicting the water level fluctuation in an alpine lake using physically based, artificial neural network, and time series forecasting models. Math. Probl. Eng. 2015(708204), 11 (2015)Google Scholar
  21. 21.
    Sun, Y., Lang, M.X., Wang, D.Z., Liu, L.Y.: A PSO-GRNN model for railway freight volume prediction: empirical study from China. J. Ind. Eng. Manag. 7, 413–433 (2014)Google Scholar
  22. 22.
    Geng, L.Y., Zhang, T.W., Zhao, P.: Forecast of railway freight volumes based on LS-SVM with grey coreelation analysis. J. China Railw. Soc. 34(3), 1–6 (2012)Google Scholar
  23. 23.
    Jiang, J.: BP neural network algorithm optimized by genetic algorithm and its simulation. Int. J. Comput. Sci. Issues 10(2), 516–520 (2013)Google Scholar
  24. 24.
    Xing, H.H., Lin, H.Y.: An intelligent method optimizing BP neural network model. Adv. Mat. Res. 2012, 2470–2474 (2012)Google Scholar
  25. 25.
    Tayal, D.K., Saxena, P.C.: Multivalued dependencies in fuzzy multivalued relational databases using fuzzy functions. Int. J. Uncertain. Fuzz. Knowl. Syst. 23(4), 589–626 (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Pei Wang
    • 1
    Email author
  • Xiaodong Zhang
    • 1
  • Boling Han
    • 2
  • Maoxiang Lang
    • 1
  1. 1.School of Traffic and TransportationBeijing Jiaotong UniversityBeijingPeople’s Republic of China
  2. 2.Transportation Bureau, China Railway CorporationBeijingPeople’s Republic of China

Personalised recommendations