PREDICTION OF PASSENGER FLOW ON THE HIGHWAY BASED ON THE LEAST SQUARE SUPPORT VECTOR MACHINE

被引：12
|
作者：
Hu, Yanrong [1 ,2 ]
Wu, Chong [1 ]
Liu, Hongjiu [2 ]
机构：
[1] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China
[2] Changshu Inst Technol, Sch Management, Changshu 215500, Peoples R China
来源：
TRANSPORT | 2011年 / 26卷 / 02期
基金：
中国国家自然科学基金;
关键词：
support vector machine; statistical learning theory; least square support vector machine; regressive model; passenger flow; prediction; TRAFFIC FLOW; PARAMETERS; NETWORK;
D O I：
10.3846/16484142.2011.593121
中图分类号：
U [交通运输];
学科分类号：
08 ; 0823 ;
摘要：
A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000-2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway.
引用
收藏
页码：197 / 203
页数：7
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