GPS Trajectory Data Enrichment based on a Latent Statistical Model

• DOI: 10.5220/0005699902550262
• Publication Date: 2016-04-28T09:19:58Z
• Authors: Akira Kinoshita; Atsuhiro Takasu; Kenro Aihara; Jun Ishii; Hisashi Kurasawa; Hiroshi Sato; Motonori Nakamura; Jun Adachi
• Keywords: Interpolation; Granularity; Mode (computer interface)

This paper proposes a latent statistical model for analyzing global positioning system (GPS) trajectory data.

Because of the rapid spread GPS-equipped devices, numerous GPS trajectories have become available,

and they are useful various location-aware systems. To better utilize data, number sensor data

mining techniques been developed. discusses application model

to two closely related problems, namely, moving mode estimation and interpolation observation.

The proposed estimates objects represents patterns according to

the by exploiting large dataset. We evaluate effectiveness through

experiments using GeoLife Trajectories dataset show that more than three-quarters covered

locations were correctly reproduced at fine granularity.