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.