基于交叉验证支持向量机算法的交通状态判别研究

Identification of Traffic State Based on Cross-Validation SVM

传统交通状态判别算法存在准确率低和速度较慢的缺点。将支持向量机(SVM)算法应用于交通状态监测中,并且以径向基函数(RBF)作为核函数,在SVM训练过程中采取交叉验证的方法,即将训练样本分成多个子集,再通过每次保留其中一个子集作为检测样本进行反复训练,获得RBF所需的σ值和惩罚系数C。通过对某主干路上某时段的数据验证和检测表明,基于交叉验证SVM算法具有较高的准确率和速度,可以有效地将其运用到实际中。

Because traditional traffic identification algorithm has the shortcomings of low accuracy and slow speed,we applied SVM（support vector machine）which its learning procedure is based on the method of cross-validation into detecting traffic state with the RBF（radial basis function）core.The cross-validation SVM separates the training samples into several subsets,and each subset was tested by the classifier created by training other subsets.Then the best parameters of SVM and RBF,σand Cwere gained.In the end,we used the data from a period of a main road as training samples to test SVM based on cross-validation,and the experimental result shows that,with this method,we can detect traffic state fast and precisely.In addition,this method can be applied into practice effectively.