CAJ | 학술논문

影响交通流变化的因素众多，为改进传统的船舶交通流预测精度不高，一种结合粗糙集和支持向量回归智能算法的交通流预测模型提出，通过ROSETTA软件进行属性约简预处理，筛选出影响交通流变化的关键影响因素，剔除冗余信息。筛选结果显示外轮进出艘次、对外贸易总额、港口GDP、集装箱标准箱、港口货物吞吐量为输入变量，运用Libsvm软件构建基于遗传算法参数寻优的支持向量回归模型预测2008年和2009年的交通流。算例结果表明，与BP神经网络和SVM模型相比，组合预测模型是有效和实用的预测工具。
영향교통류변화적인소음다，위개진전통적선박교통류예측정도불고，일충결합조조집화지지향량회귀지능산법적교통류예측모형제출，통과ROSETTA연건진행속성약간예처리，사선출영향교통류변화적관건영향인소，척제용여신식。사선결과현시외륜진출소차、대외무역총액、항구GDP、집장상표준상、항구화물탄토량위수입변량，운용Libsvm연건구건기우유전산법삼수심우적지지향량회귀모형예측2008년화2009년적교통류。산례결과표명，여BP신경망락화SVM모형상비，조합예측모형시유효화실용적예측공구。
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.