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

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.

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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