Abstract: A driver behavior identification method in comprehensive consideration of vehicle handling stability and intelligent transportation system is proposed in this paper. Firstly, the local road network simulation under different driving conditions is achieved by Microscopic traffic simulation software, and massive basic simulation data is obtained. The transformation from basic driving parameters to running status parameters is realized based on the theory of vehicle dynamics. Secondly, neighborhood rough set is applied for feature reduction. Sample data mining is realized by combined use of ensemble empirical mode decomposition (EEMD), correlation coefficient and sample entropy. And the obtained sample entropy value is used as the eigenvectors. Finally, the eigenvectors are put into GG fuzzy clustering for clustering. Then on the basis of the samples, which are obtained by Microscopic traffic software and UC-Win/Road driving simulator, different driver behavior identification is achieved by minimum average closeness degree. Based on the maximum closeness degree and the secondary maximum closeness degree, the driving behavior membership degree of the test sample is calculated. The experiment demonstrates that the method has achieved good effect in driver behavior identification

Key words: driver behavior, attribute reduction, data mining, fuzzy clustering