Abstract：In order to construct the typical driving cycle of the passenger cars to meet the traffic characteristics of Zhengzhou city, 60 passenger cars were selected to collect the data, then the effective driving data were divided into 120 744 kinematics fragments, and principal component analysis method was used to reduce the dimensions and to execute the nonlinear processing of the constructed characteristic parameter matrix. DaviesBouldin index ( DB) was chosen to determine the optimal cluster number, and the four principal components obtained by the principal component analysis method were clustered into three categories by the global Kmeans clustering algorithm, then the representative kinematics fragments were selected from fragment libraries by means of the correlation coefficient method, thus the typical driving cycle of Zhengzhou passenger cars ( ZZ＿DC) was constructed. Compared with the driving cycle constructed by the traditional K-means clustering algorithm, the driving cycle constructed by the global K-means clustering could more accurately reflect the driving status of the passenger cars on the actual roads in Zhengzhou. The ZZ＿DC was compared with the test driving cycle of the domestic passenger cars ( NEDC) , and the results showed that the differences between the ZZ＿DC and the NEDC were significant. Therefore, it was necessary to develop the driving cycle suitable for the traffic characteristics of Zhengzhou.

Keyword：passenger car;driving cycle;principal component analysis method;global K-means clustering;