The current China railway freight transport has always been faced with the situation of limited transport resources. Many relative studies have been done to solve the problem of resource shortage. And railway freight volume prediction is the basis of all these studies. With accurate volume prediction, railway freight transport administrations can precisely allocate the transport resources, such as wagons and locomotives. In order to overcome the limitations of traditional prediction methods, in this study, we design four artificial neural network models for prediction, including BP neural network model, linear neural network model, RBF neural network model and generalized regression neural network model. The results of simulation and comparison show that all these models can reach high prediction accuracy and generalized regression neural network has both higher prediction accuracy and better curve fitting capacity compared with other models.
Prediction Models for Railway Freight Volume Based on Artificial Neural Networks
Applied Mechanics and Materials ; 543-547 ; 2093-2098
2014-03-24
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Railway Freight Volume Prediction Based on Support Vector Regression (SVR)
Trans Tech Publications | 2014
|Modeling freight distribution using artificial neural networks
Elsevier | 2003
|Modeling freight distribution using artificial neural networks
Online Contents | 2004
|