A PSO-GRNN Model for Railway Freight Volume Prediction: Empirical Study from China
Abstract: The purpose of this
paper is to propose a mathematical model for the prediction of railway freight
volume, and therefore provide railway freight resource allocation with an
accurate direction. With an accurate railway freight volume prediction, railway
freight enterprises can integrate the limited resources and organize transport
more reasonably.
Design/methodology/approach: In this paper, a PSO-GRNN model is proposed
to predict the railway freight volume. In this model, GRNN is applied to carry
out the nonlinear regression analysis and output the prediction value, PSO
algorithm is applied to optimize the GRNN model by searching the best smoothing
parameter. In order to improve the performance of PSO algorithm, time linear
decreasing inertia weight algorithm and time varying acceleration coefficient
algorithm are applied in the paper.
Originality/value: A railway freight volume prediction index system
containing seventeen indexes from five aspects is established in this paper.
And PSO-GRNN model constructed in this paper are applied to predict the railway
freight volume from 2007 to 2011. Finally, an empirical study is given to
verify the feasibility and accuracy of the PSO-GRNN model by comparing with
RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good
performance in reducing the prediction error, and can be applied in actual
production easily.
Keywords: railway freight
volume, prediction model, neural network, GRNN, smoothing parameter, PSO
algorithm
Author: Yan Sun, Maoxiang
Lang, Danzhu Wang, Linyun Liu
Journal Code: jptindustrigg140051
