SVM based float car driving mode classification model
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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.Abstract: 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 car real-time traffic conditions seriously. A SVM(support vector machine) based float car 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 car driving mode analysis enhances the accuracy of exiting system evidently.
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Key words:
- float car /
- sampling interval /
- support vector machine /
- feature selection /
- membership matrix
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