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Traffic state and evolution are important for better knowledge of urban traffic properties, as well as for the better traffic control and management. Therefore, it has attracted much attention recently. Model-based and data-driven are two kinds of methods in handling with such issues. With the wide deployment of ITS, large volume traffic data are available and data-driven methods such as clustering analysis have found their applications in ITS. According to physical characteristics of urban traffic flow, the paper follows the data-driven analysis and develops a grid-based clustering method for traffic state extraction and state evolution analysis. It also designs a wavelet transformation as a filter to decrease the noise in raw traffic data. Results on de-noised signals show more definite trends for traffic state evolutions.