A Trajectory Clustering Method Based on Moving Index Analysis and Modeling

Abstract

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, the PD (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 called PMS(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 of PD. (2) Calculating the MPD of all the points to approximate the trajectory points. (3) Establishing a MIGM (Moving Index Gaussian model) model based on the MPD representation. (4) Fitting all the trajectories and extracting the points that are not fitted by MIGM. (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. INDEX TERMS Trajectory clustering, moving index, stop points extraction, moving index Gaussian model.

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints

“…The method enabled the realistic extraction of various complex trajectories with high effectiveness. Furthermore, to evaluate the movement performance of different types of points, Yang et al [ 8 ] developed a new moving index that combines point density and movement characteristics. A moving index Gaussian model was constructed based on this index to extract stopping points, which overcomes limitations of clustering based on stopping point features and reduces the pseudo-merging of stopping point clusters.…”