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Neural Computing and Applications

, Volume 31, Issue 12, pp 8139–8145 | Cite as

Construction of prediction model of neural network railway bulk cargo floating price based on random forest regression algorithm

  • Jingwei Guo
  • Jian WangEmail author
  • Qinglin Li
  • Baohua Guo
Machine Learning - Applications & Techniques in Cyber Intelligence

Abstract

In order to improve the prediction accuracy and modeling speed of railway freight volume, this paper combines the cargo floating price prediction model with the neural network algorithm (hereinafter referred to as NNA) to establish a prediction model. The railway cargo floating price based on neural network (hereinafter referred to as PMBCFP) relies on the random forest regression algorithm (hereinafter referred to as RFRA). Through the neural network operator in the prediction model of the floating price of goods, the randomness of the original sequence is weakened, and the implicit rules in the series are mined. The characteristics of neural networks are computationally very simple. In addition, a random forest regression algorithm is applied to the optimization, RFRA’s choice. Case studies of China’s rail freight volume show that RMSE and other indicators are faster. The MAE, MPE and Tell inequality coefficients obtained from this model were 0.0628, 0.0523, 0.0162 and 0.0107, respectively. This model has good prediction results. The time to search for the best parameters of RFRA using the NNA algorithm is 55.656 s, which is 10.462 s less than the time required for traditional cross-validation methods. Therefore, it is suitable for short-term forecasting of railway freight volume.

Keywords

Prediction of the volume of railway freight Prediction model of cargo floating price Neural network Random forest regression algorithm 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61803147), the Key Scientific and Technological Project of Henan Province (No. 182102310799) and the Foundation of Henan Educational Committee (No. 18A580003).

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Jingwei Guo
    • 1
  • Jian Wang
    • 2
    Email author
  • Qinglin Li
    • 3
  • Baohua Guo
    • 1
  1. 1.School of Energy Science and EngineeringHenan Polytechnic UniversityJiaozuoChina
  2. 2.School of Economics and ManagementJiangsu University of Science and TechnologyZhenjiangChina
  3. 3.School of Transportation and LogisticsSouthwest Jiaotong UniversityChengduChina

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