At present, trajectory data, series of coordinate data traversed by moving objects, can be readily obtained due to the advent of positioning technologies. Clustering of trajectories and giving meanings to the resulting clusters is an active research area. Recently, we proposed an analysis approach that clusters trajectories in two steps: the first step based on data distribution and the second step based on state transition. In this approach, for coping with the distinguished characteristic of each trajectory, a map of interest is dynamically divided into multiple states, according to the trajectory distribution, and a quadtree is generated for each trajectory. The first-step clustering is then performed based on the differences between the quadtrees. For all trajectories in a resulting cluster of interest, the second-step clustering is further performed based on the differences in their state-to-state transition probabilities using a proposed method for comparing a pair of trajectori...