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Volume 25 Issue 1
Mar.  2012
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Journal of Southwest Jiaotong University  > 2012  >  25(1): 144-150.
GENG Liyan, LIANG Yigang. Prediction of Railway Freight Volumes Based on Grey Adaptive Particle Swarm Least Squares Support Vector Machine Model[J]. Journal of Southwest Jiaotong University, 2012, 25(1): 144-150. doi: 10.3969/j.issn.0258-2724.2012.021.01.024
Citation: GENG Liyan, LIANG Yigang. Prediction of Railway Freight Volumes Based on Grey Adaptive Particle Swarm Least Squares Support Vector Machine Model[J]. Journal of Southwest Jiaotong University, 2012, 25(1): 144-150. doi: 10.3969/j.issn.0258-2724.2012.021.01.024
shu

Prediction of Railway Freight Volumes Based on Grey Adaptive Particle Swarm Least Squares Support Vector Machine Model

doi: 10.3969/j.issn.0258-2724.2012.021.01.024
  • Received Date: 20 Apr 2011
  • Publish Date: 25 Feb 2012
    Abstract
  • In order to improve the forecasting accuracy and the modeling speed for railway freight volumes, the grey forecasting model GM (1, 1) and the adaptive particle swarm optimization (APSO) were both introduced into the least squares support vector machines (LSSVMs). Thus, a new model, the grey APSO least squares support vector machine (GM-APSO-LSSVM) model, was built. The new model weakens the stochastic factor in the original sequence and exploits the regularity of data using the grey sequence operator of the grey model in the first stage. Then, the new data are forecasted with the LSSVM featured by simple calculation, fast solving speed, and powerful non-linear mapping ability. At the same time, the parameters of LSSVM are optimized by the APSO. An empirical analysis was performed to verify the proposed model using the freight volumes data in China. The results show that the proposed model has a superior prediction performance to the existing models, and its performance indices RMSE, MAE, MPE, and Theil are 0.062 8, 0.052 3, 0.016 2, and 0.010 7, respectively, all less than those of the other models. The searching time for the optimal LSSVM parameters using the APSO is 55.656 s, which is 10.462 s less than the time spent by the conventional cross-validation method. The relative prediction errors of the model in predicting the railway freight volumes from 2006 to 2009 are 0.39%, -1.67%, 1.44% and 4.75%, respectively; therefore, the proposed model is more suitable for short-term railway freight volumes forecasting.

     

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