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首页> 外文会议>Coat international conference of transportation professionals >Application of Factor Analysis and SVM Technique in Expressway Condition Pattern Recognition
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Application of Factor Analysis and SVM Technique in Expressway Condition Pattern Recognition

机译：因子分析和支持向量机技术在高速公路状态模式识别中的应用

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摘要

According to traffic flow characteristics under different traffic conditions on expressways, a kind of expressway traffic condition pattern that is recognition based on eigenvector construction by Factor Analysis is put forward, which synthetically applies Factor Analysis and SVM technology. Above all, a common factor was extracted from pre-selected traffic parameters using Factor Analysis, and then makes it with interpretation by circumvolve transformation, to realize dimension reduction, and to construct traffic parameter eigenvectors. Then the validity of the eigenvectors and a new algorithm proposed in this paper have been tested using various SVM models, and it also contrasts with the results of algorithms only based on SVM. The instance results demonstrate that the new algorithm can detect traffic state more effectively and more expeditiously with preferable detection performance.
机译：针对高速公路不同交通状况下的交通流特征，提出了一种基于因子分析的特征向量构造识别的高速公路交通状况模式，将因子分析与支持向量机技术相结合。首先，通过因子分析从预先选择的交通参数中提取出一个公共因子，然后通过绕行变换对其进行解释，以实现降维，并构造交通参数特征向量。然后使用各种支持向量机模型测试了特征向量和本文提出的新算法的有效性，并与仅基于支持向量机的算法结果进行了对比。实例结果表明，新算法可以更有效，更快捷地检测交通状态，并且具有更好的检测性能。

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