GPS Trajectory Data Enrichment based on a Latent Statistical Model

Akira Kinoshita, Atsuhiro Takasu, Kenro Aihara, Jun Ishii, Hisashi Kurasawa, Hiroshi Sato, Motonori Nakamura, Jun Adachi

2016

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.

Paper Citation

in Harvard Style

Kinoshita A., Takasu A., Aihara K., Ishii J., Kurasawa H., Sato H., Nakamura M. and Adachi J. (2016). GPS Trajectory Data Enrichment based on a Latent Statistical Model . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 255-262. DOI: 10.5220/0005699902550262

in Bibtex Style

@conference{icpram16,
author={Akira Kinoshita and Atsuhiro Takasu and Kenro Aihara and Jun Ishii and Hisashi Kurasawa and Hiroshi Sato and Motonori Nakamura and Jun Adachi},
title={GPS Trajectory Data Enrichment based on a Latent Statistical Model},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={255-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005699902550262},
isbn={978-989-758-173-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - GPS Trajectory Data Enrichment based on a Latent Statistical Model
SN - 978-989-758-173-1
AU - Kinoshita A.
AU - Takasu A.
AU - Aihara K.
AU - Ishii J.
AU - Kurasawa H.
AU - Sato H.
AU - Nakamura M.
AU - Adachi J.
PY - 2016
SP - 255
EP - 262
DO - 10.5220/0005699902550262