A Trajectory Clustering Method Based on Moving Index Analysis and Modeling
Yuqing Yang, Jianghui Cai, Haifeng Yang, Xujun Zhao, Jing Liu · IEEE Access · 2022
Aiming at the problem of low trajectory clustering accuracy caused by only focusing on the characteristics of Stop Points, this paper analyses the features of both the Stop and the Move Points and proposes a trajectory clustering method based on the moving index analysis and modelling. Firstly, the different characteristics of the trajectory points are explored, and each feature is analysed and evaluated by experiments. On this basis, thePD(Point Density) and MC (Movement characteristic) are selected to define a new moving index (MPD) to evaluate the movement performance of different types of points. Secondly, a trajectory clustering algorithm calledPMS(Points Moving Index Analysis and Modelling) is proposed. This algorithm finds the Stop Points by the following steps. (1) Obtaining the candidate move points with the help ofPD. (2) Calculating theMPDof all the points to approximate the trajectory points. (3) Establishing aMIGM(Moving Index Gaussian model) model based on theMPDrepresentation. (4) Fitting all the trajectories and extracting the points that are not fitted byMIGM. (5) Judging whether the extracted points satisfy the convergence condition. If the convergence condition is satisfied, the extracted points are Stop Points. Otherwise, adjust the radius${R}$and repeat the above four steps. Experimental results show that this method can reduce error merging of adjacent clusters and find the trajectory clusters of Stop Points with different shapes.