摘要
将主成分分析和支持向量机回归相结合,进行道路网多断面的短时交通流量预测研究。首先,整理分析路网中多个断面交通流量数据进行主成分分析,得到主成分数据序列;其次,根据主成分数据序列建立训练集训练支持向量机,并利用遗传算法优化参数;最后,输入支持向量机所需数据,得到主成分预测结果,转化为断面交通流量数据,从而预测道路网短时交通流量。采用城市快速路多断面数据进行实例分析,结果表明,该模型比单一断面预测方法的效果更好。
A scheme to forecast short-term traffic volumes of a road network is presented. This scheme combines the principal component analysis and the support vector regression. First, the traffic volume data of multi-road-cross-sections acquired from a road network are turned into several time series data by the principal component analysis. Then, these principal component data are used to train the support vector machines, and a genetic algorithm is applied to optimize the parameters of the support vector machines. After the training and optimization, by inputting required data to the support vector machines, the principal components are obtained as the outputs. These outputs are then transformed into the forecasting data of the short-term traffic volumes. A case study is carried out to validate the scheme. The traffic volume data from seven road cross-sections of an urban express-way are used. The forecasting results by this scheme are much better than that by the schemes of single road cross-section.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2008年第1期48-52,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(50578009)
'973'国家重点基础研究发展规划项目(2006CB705500)
关键词
智能交通系统
短时交通流量预测
支持向量机
主成分分析
道路网
intelligent transportation systems
short-term traffic volume forecasting
support vector machine
principal component analysis
road network
作者简介
姚智胜（1979-），男，博士研究生．研究方向：智能交通系统，城市交通规划．E-mail：yzhisheng@163．com
通讯联系人：邵春福（1957-），男，教授，博士生导师．研究方向：交通规划，智能交通系统，交通安全．E-mail：cfshao@center．njtu．edu．cn
