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


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.


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



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.


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© 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

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