结合灰聚簇与Fisher变换的城市快速路交通状态判别

为提高少数据下的城市快速路交通流状态类型判别精度,提出一种结合灰聚簇与Fisher变换(GC-Fisher)的组合方法.选择交通量Q、速度v、占有率O作为基础参数,首先经灰聚簇理论将基础参数数据聚簇为4类,其次对分类后的数据构建训练集,训练GC-Fisher模型,获取每一种交通流状态类型的Fisher变换方式及判别函数,最后选择结合K均值与多分类支持向量机(K-SVM)的组合方法进行比较.结果表明:在数据量较少条件下对交通流状态类型进行判别,GC-Fisher模型判别率为92%,优于K-SVM模型的判别率69%,GC-Fisher组合方法在少数据下能够更好地提高交通流状态类型的判别效果.

Traffic state identification for urban expressway: a combination of gray clustering and fisher transform model

To improve the accuracy of urban traffic flow state identification under limited data condition, a model combining grey clustering and fisher transform (GC-fisher model) is proposed. First, the parameters of traffic volume (Q), speed (v), and occupation (O) were divided into four categories according to the grey clustering theory. Then training dataset was established by using the classified data, and the fisher transform and discriminant functions of each traffic flow state were obtained by using the GC-Fisher model. Afterwards, the combination of K-means and multi-class support vector machine (K-SVM) model was selected as the comparison model. Results of model discriminant rate and the case study show that the GC-fisher model outperformed K-SVM model under limited data condition: the discriminant rate was 92% by using GC-fisher, while the rate was 69% by using K-SVM. The GC-Fisher model can improve the discrimination accuracy of traffic flow state under limited data condition.