Semantic trajectory segmentation based on change-point detection and ontology

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.

KEYWORDS:

• Trajectory segmentation
• moving pattern
• change-point detection
• ontology

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [61772420]; Natural Sciences and Engineering Research Council of Canada Discovery [RGPIN-2018-03916]; The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [18YJA630025].

Notes on contributors

Yuan Gao

Yuan Gao is an Associate Professor at the Department of Economics and Management, Northwest University. She holds a Ph.D. in Computer Science from Northwest University, China. Her current research interests are spatiotemporal data mining and location recommendation systems.

Longfei Huang

Longfei Huang received the B.Sc. degree in computer science from Northwest University, China. His research interests include spatial data mining and location-based services.

Jun Feng

Jun Feng received the Ph.D. degree in Computer Science from City University of Hong Kong. She is a Full Professor in School of Information Science and Technology. Her current research interests include Brain-Human interaction and pattern recognition.

Xin Wang

Xin Wang received the B.Sc. degree in computer science and the M.Eng. degree in software engineering from Northwest University, China, and the Ph.D. degree in computer science from the University of Regina, Canada. She is a Full Professor with the Department of Geomatics Engineering, University of Calgary. She is also an Adjunct Professor with Northwest University. Her current research interests include spatial data mining, trajectory mining, ontology and knowledge engineering in GIS, web GIS, and location-based social networks.