Forecasting of freight volume based on support vector regression optimized by genetic algorithm

Author

Gao, Yan

Author_Institution

Gengdan Inst., Beijing Univ. of Technol., Beijing, China

fYear

2009

fDate

8-11 Aug. 2009

Firstpage

550

Lastpage

553

Abstract

Freight volume forecasting is significant to highway web plan. Here, support vector regression optimized by genetic algorithm (G-SVR) is proposed to forecast freight volume. We adopt genetic algorithm (GA) to seek the optimal parameters of SVR in order to improve the efficiency of prediction. The data of freight volume in a certain port from 1998 to 2007 is used as a case study. The experimental results indicate that the proposed G-SVR model has higher forecasting accuracy than grey model, artificial neural network.

Keywords

freight handling; genetic algorithms; goods distribution; regression analysis; support vector machines; artificial neural network; freight volume forecasting; genetic algorithm; grey model; highway web plan; support vector regression; Artificial neural networks; Convergence; Genetic algorithms; Linear regression; Predictive models; Risk management; Road transportation; Technology forecasting; Upper bound; Vectors; freight volume; support vector regression; training parameters;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4519-6

Electronic_ISBN

978-1-4244-4520-2

Type

conf

DOI

10.1109/ICCSIT.2009.5234798

Filename

5234798