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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

ISBN: 978-989-758-173-1

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.

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Paper citation in several formats:
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

@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},
}

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

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