Semantic trajectory segmentation based on change-point detection and ontology. Issue 12 (1st December 2020)

• Record Type: Journal Article
• Authors: Gao, Yuan; Huang, Longfei; Feng, Jun; Wang, Xin
• Is Part Of: International journal of geographical information science. Volume 34:Issue 12(2020)
• Page Start: 2361
• Page End: 2394
• Publication Date: 2020-12-01
• Subjects: Trajectory segmentation -- moving pattern -- change-point detection -- ontology; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1798966
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Trajectory segmentation is a fundamental issue in GPS trajectory analytics. The task of dividing a raw trajectory into reasonable sub-trajectories and annotating them based on moving subject's intentions and application domains remains a challenge. This is due to the highly dynamic nature of individuals' patterns of movement and the complex relationships between such patterns and surrounding points of interest. In this paper, we present a framework called SEMANTIC-SEG for automatic semantic segmentation of trajectories from GPS readings. For the decomposition component of SEMANTIC-SEG, a moving pattern change detection (MPCD) algorithm is proposed to divide the raw trajectory into segments that are homogeneous in their movement conditions. A generic ontology and a spatiotemporal probability model for segmentation are then introduced to implement a bottom-up ontology-based reasoning for semantic enrichment. The experimental results on three real-world datasets show that MPCD can more effectively identify the semantically significant change-points in a pattern of movement than four existing baseline methods. Moreover, experiments are conducted to demonstrate how the proposed SEMANTIC-SEG framework can be applied.