关键词: 船舶交通流, 糙集, 遗传算法, 支持向量回归, 组合预测

Abstract: Many factors contribute to vessel’s traffic flow, to improve the forecasting accuracy of the traditional method, an integrated model of Rough Sets Theory（RST）, Genetic Algorithm（GA） and Support Vector Regression（SVR）, RST-GA-SVR, for traffic volume forecasting is proposed. ROSETTA software is used in data preprocessing, through the rough set attribute reduction algorithm to find the core impact factor of volume and remove redundant information. The results reveal the No. of in-and-out foreign vessels, foreign trade volume, port GDP, container TEU, port cargo throughput as input vector. GA is used for parameters selection in SVR model and the trained LIBSVM model is used to forecast the volume in 2008 and 2009. Compared with BP neural network and SVM models, empirical results show that RST-GA-SVR model is an efficient and practical tool for vessel volume forecasting.

Key words: vessel traffic flow, rough set, genetic algorithm, support vector regression, combination forecasting