A PSO-GRNN model for railway freight volume prediction: Empirical study from China

Abstract

Purpose: In this study, we aim to design a mathematical model for railway freight volume prediction which can provide an accurate direction for railway freight resource allocation. Based on the precise prediction, railway freight enterprises are able to optimize and integrate the limited resources to organize freight transport more efficiently and economically. Design/methodology/approach: In this study, we design a PSO-GRNN model to predict railway freight volume. In the proposed model, GRNN carries out the nonlinear regression analysis between railway freight volume and its influencing factors which can be described by detailed and measurable indexes and outputs the prediction value. PSO algorithm with time linear decreasing inertia weight and time varying acceleration coefficients is applied to optimize the basic GRNN model by searching the optimal smoothing parameter. Findings: The simulation result in this study indicates that (1) the PSO-GRNN model is able to predict railway freight volume by using the value of other relevant indexes as its input to fit the variation of railway freight volume; (2) Through optimization for GRNN model based on PSO algorithm, the proposed model performs well prediction accuracy; (3) Compared with RBFNN model and BPNN model, the superiority of the proposed model in prediction accuracy and curve fitting capacity is verified. Originality/value: In the empirical study from China, we establish a railway freight volume prediction index system containing seventeen prediction indexes from five influencing factors. Then we construct the PSO-GRNN model to predict the railway freight volume from 2007 to 2011 by using data from 1990 to 2006 as training samples. In order to verify the feasibility and prediction accuracy of the proposed model, we also use two widely applied neural network models, including RBFNN model and BPNN model, to estimate the railway freight volume prediction during the same period. Compared with RBFNN model and BPNN model, the simulation results indicate that the proposed model has higher prediction accuracy and better curve fitting capacity. Therefore, PSO-GRNN model can be further applied in practical railway freight production.