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

Abstract

The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have proven ineffective in accurately identifying non-stopping points caused by trailing or round trips. To address this issue, this paper proposes a novel density peak clustering algorithm based on coherence distance, incorporating temporal and entropy constraints, referred to as the two-step DPCC-TE. The proposed algorithm introduces a coherence index to integrate spatial and temporal features, and imposes temporal and entropy constraints on the clusters to mitigate local density increase caused by slow-moving points and back-and-forth movements. Moreover, to address the issue of interactions between subclusters after one-step clustering, a two-step clustering algorithm is proposed based on the DPCC-TE algorithm. Experimental results demonstrate that the proposed two-step clustering algorithm outperforms the DBSCAN-TE and one-step DPCC-TE methods, and achieves an accuracy of 95.49% in identifying stopping points.

An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study

“…With the development of science and technology, the application of navigation and positioning technology is being increasingly researched [1][2][3], and the methods of navigation and positioning are being gradually diversified. At present, common positioning technologies include global positioning system (GPS) [4][5][6][7], BeiDou navigation satellite system (BNSS) [8,9], inertial navigation system (INS) [10][11][12], UWB (ultra-wide band, UWB) [13][14][15][16][17], machine vision navigation systems [18][19][20][21][22] and LIDAR [23][24][25][26]. Among them, ultra-wide band technology stands out among many positioning technologies due to its low power consumption, strong anti-interference, high penetration power and high positioning accuracy, and its application is becoming more and more widespread.…”