Abstract:

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.

Key words: traffic engineering, traffic state identification, spectral clustering, random subspace, K-nearest neighbor