Authors:
Akira Kinoshita
1
;
Atsuhiro Takasu
2
;
Kenro Aihara
2
;
Jun Ishii
3
;
Hisashi Kurasawa
3
;
Hiroshi Sato
3
;
Motonori Nakamura
3
and
Jun Adachi
2
Affiliations:
1
The University of Tokyo, Japan
;
2
National Institute of Informatics, Japan
;
3
NTT Network Innovation Laboratories, Japan
Keyword(s):
GPS Trajectory Data, Interpolation, Latent Statistical Model, Moving Mode Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Graphical and Graph-Based Models
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Sparsity
;
Stochastic Methods
;
Theory and Methods
Abstract:
This paper proposes a latent statistical model for analyzing global positioning system (GPS) trajectory data.
Because of the rapid spread of GPS-equipped devices, numerous GPS trajectories have become available,
and they are useful for various location-aware systems. To better utilize GPS data, a number of sensor data
mining techniques have been developed. This paper discusses the application of a latent statistical model
to two closely related problems, namely, moving mode estimation and interpolation of the GPS observation.
The proposed model estimates a latent mode of moving objects and represents moving patterns according to
the mode by exploiting a large GPS trajectory dataset. We evaluate the effectiveness of the model through
experiments using the GeoLife GPS Trajectories dataset and show that more than three-quarters of covered
locations were correctly reproduced by interpolation at a fine granularity.