Abstract: Trajectory similarity estimation is the key to discover vehicle motion characteristics and trajectory classification, but the calculation of trajectory similarity is slow, and improving the speed of trajectory matching can help to carry out trajectory characteristics mining fast. Therefore, an efficient trajectory matching algorithm based on the rotation of spatial coordinate is proposed. Firstly, the trajectory curve is converted into the mean and variance curve which the number of points is equal to the number of rotations by multiple rotation of the spatial coordinate system. Then, Pearson correlation coefficient and Fréchet distance are used to measure the correlation of the mean and variance curves. Finally, the similarity between the original trajectories is determined indirectly according to the values of four parameters: Fréchet mean, Fréchet var, Pearson mean and Pearson var. Based on the taxi trajectory data of Hangzhou, under different trajectory sampling points and spatial coordinate rotation times, compared trajectory matching accuracy and speed with the traditional Hausdorff trajectory matching algorithm. The several experiments show that the algorithm can improve the trajectory matching speed by 85% on average while ensuring the accuracy of trajectory matching. By constructing a visual analysis system that includes three major modules: a map overview showing the results of trajectory matching, several visual in-teractive components that explore the differences in trajectory matching results, and a module for selecting tra-jectory matching parameters, explore the differences in the results of the four trajectory matching methods to help road network researchers to match real driving trajectories and find similar trajectory groups faster.