摘要
为了提高短期交通流量预测的收敛速度、预测精度,提出一种核主成分分析(kernel principal component analysis,KPCA)与核极限学习机(kernel extreme learning machine,KELM)相结合的KPCA-KELM方法.KPCA方法可对数据进行预处理,在特征空间中有效提取模型输入的非线性主元;而KELM方法无须设定网络隐含层节点的数目、通过正则化最小二乘算法计算网络的输出权值,能以极快的学习速度获得良好的推广性;新方法融合了KPCA与KELM的优点.采用西雅图华盛顿大学ITS研究组以及北京交通管理局实测的交通流量预测数据进行了试验,并将KPCA-KELM方法与单一的KELM,LSSVM,SVM以及KPCA-LSSVM,KPCA-SVM等预测方法进行比较.试验结果表明:新方法的收敛速度以及预测精度均优于对比方法;对北京交通管理局实测交通量数据的单步预测中,KPCA-KELM方法的预测精度比KELM方法提高了1.991 3.
To improve convergence speed and accuracy of short-term traffic flow prediction,a method of KPCA-KELM combining kernel principal component analysis(KPCA)and kernel extreme learning machine(KELM)was proposed.By the KPCA method,the nonlinear principal elements of model input in the feature space were effectively extracted to realize the data pre-processing.In the KELM method,the nodes number of network hidden layer was not set,and the output weight of network was calculated by the regularized least squares algorithm to achieve good promotion with extremely fast learning speed.The advantages of KPCA and KELM were combined in the proposed method.The traffic flow prediction data measured by the ITS Research Group of the University of Washington in Seattle and the Beijing Traffic Administration were tested,and the KPCA-KELM method was compared with single KELM,LSSVM,SVM,KPCA-LSSVM,KPCA-SVM and other prediction methods.The experimental results show that the convergence speed and prediction accuracy of the proposed method are better than those of other comparison methods.For the single-step prediction of the measured traffic volume data of Beijing Transportation Administration,the prediction accuracy of the KPCA-KELM method is 1.991 3 higher than that of the KELM method.
作者
李军
王秋莉
LI Jun;WANG Qiuli(School of Automation&Electrical Engineering,Lanzhou Jiao Tong University,Lanzhou,Gansu 730070,China)
出处
《江苏大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2018年第5期570-575,共6页
Journal of Jiangsu University：Natural Science Edition
基金
国家自然科学基金资助项目(51467008)
关键词
智能交通系统
交通流量
预测
核主成分分析
核极限学习机
intelligent transportation system
traffic flow
prediction
kernel principal component analysis
kernel extreme learning machine
