为了提高城市快速路交通状态判别的准确性，构建了一种基于谱聚类与随机子 空间集成 K 最近邻(RS-KNN)的交通状态判别模型． 以地点交通参数为基础，根据交通流 运行特性并结合中国道路服务水平的 4 个等级，采用谱聚类算法将交通状态划分为 4 类; 然后使用已分类的交通流数据训练 RS-KNN 模型． 通过上海快速路的实测数据完成模型 的实验验证和对比分析． 实验结果表明，所提出的模型不仅能够提高交通状态判别的精 度，而且具有良好的鲁棒性，其判别率比标准 KNN 模型、BP 神经网络模型和 SVM 模型分 别提高 7． 3%、4． 9%和 4． 5%．

In order to improve the accuracy of traffic state identification for urban expressway,a traffic state identi- fication model based on spectral clustering and RS-KNN (Random Subspace Ensemble K-Nearest Neighbors) is de- veloped.In the investigation,first,on the basis of spot traffic parameters data and according to the operation cha- racteristics of traffic flow,the traffic state is divided into four categories with the consideration of the four levels of service for Chinese roads.Then,the classified traffic flow data are used to train the RS-KNN model.Finally,by using the real data of an expressway in Shanghai,China,an experimental verification and a comparative analysis for the proposed model are carried out.Experimental results demonstrate that the proposed model not only improves the accuracy of traffic state identification but also possesses good robustness; and that the identification rate of the proposed model is 7. 3%,4. 9% and 4. 5% higher than that of the standard KNN model,the BP neural network and the SVM model,respectively.