摘要
浮动车在低速情况下存在两种行驶模式,如不能对上述模式进行准确区分,将严重影响浮动车实时路况计算的精度和效率.研究和设计了一个基于支持向量机(SVM,Support Vector Machine)的浮动车行驶模式判断模型,并针对性地提出了一种简单的基于隶属度矩阵的特征评价和选择方法.实验表明通过上述方法选择的特征子集所训练的分类器在测试样本集上具有92.6%的分类准确性;经过行驶模式分析后,浮动车系统的准确性有显著提升.
There are two kinds of driving modes of float car at low speed. The misjudgement of these modes will affect the accuracy and efficiency of the calculation of float ear real-time traffic conditions seriously. A SVM（ support vector machine） based float ear driving mode classification model was studied and designed, and a novel membership matrix-based feature evaluation and selection method was proposed. The classifier whose features are selected through this method made a great classification accuracy of 92.6% in test samples. The float ear driving mode analysis enhances the accuracy of exiting system evidently.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2008年第8期976-980,共5页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家863基金资助项目(2006AA12Z315)
关键词
浮动车
采样区间
支持向量机
特征提取
隶属度矩阵
float car
sampling interval
support vector machine
feature selectlon
membership matrix
