Forecasting of freight volume based on support vector regression optimized by genetic algorithm
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.
support vector regression training parameters freight volume
Yan Gao
Gengdan Institute of Beijing University of Technology Beijing China
国际会议
北京
英文
1211-1214
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）