Vessel Traffic Flow Forecasting Model Study Based on Support Vector Machine

被引：0

作者：

Feng, Hongxiang ^{[1
]}

Kong, Fancun ^{[1
]}

Xiao, Yingjie ^{[1
]}

机构：

[1] Shanghai Maritime Univ, Engn Res Ctr Shipping Simulat, Minist Educ, Shanghai 200135, Peoples R China

来源：

ADVANCED RESEARCH ON ELECTRONIC COMMERCE, WEB APPLICATION, AND COMMUNICATION, PT 1 | 2011年 / 143卷

关键词：

support vector machine (SVM); regress analysis; vessel traffic flow; forecasting; matlab; SPSS regress;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Based on vessel traffic flow data and Support Vector Machine theory, SVM regression model for short-term vessel traffic flow forecasting was presented. The forecasted vessel traffic flow and abserved ones, which by SVM regression model, coincide properly, and the forecasting results show that mean absolute percentage error of forecasting are smaller than that by SPSS regress model, which validates the feasibility of SVM regression model in the vessel traffic flow forecasting.

引用

页码：446 / 451

页数：6