Pace-Based Clustering of GPS Data for Inferring Visit Locations and Durations on a Trip

Pablo MARTINEZ LERIN; Daisuke YAMAMOTO; Naohisa TAKAHASHI

doi:10.1587/transinf.E97.D.663

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Pace-Based Clustering of GPS Data for Inferring Visit Locations and Durations on a Trip

Pablo MARTINEZ LERIN, Daisuke YAMAMOTO, Naohisa TAKAHASHI

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Summary :

Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.663-672

Publication Date: 2014/04/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.663

Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)

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Authors

Pablo MARTINEZ LERIN
  Nagoya Institute of Technology
Daisuke YAMAMOTO
  Nagoya Institute of Technology,CREST, JST
Naohisa TAKAHASHI
  Nagoya Institute of Technology

Keyword

clustering method, visit inference, place inference, duration inference, GIS

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Pablo MARTINEZ LERIN, Daisuke YAMAMOTO, Naohisa TAKAHASHI, "Pace-Based Clustering of GPS Data for Inferring Visit Locations and Durations on a Trip" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 4, pp. 663-672, April 2014, doi: 10.1587/transinf.E97.D.663.
Abstract: Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.663/_p

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@ARTICLE{e97-d_4_663,
author={Pablo MARTINEZ LERIN, Daisuke YAMAMOTO, Naohisa TAKAHASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Pace-Based Clustering of GPS Data for Inferring Visit Locations and Durations on a Trip},
year={2014},
volume={E97-D},
number={4},
pages={663-672},
abstract={Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.},
keywords={},
doi={10.1587/transinf.E97.D.663},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Pace-Based Clustering of GPS Data for Inferring Visit Locations and Durations on a Trip
T2 - IEICE TRANSACTIONS on Information
SP - 663
EP - 672
AU - Pablo MARTINEZ LERIN
AU - Daisuke YAMAMOTO
AU - Naohisa TAKAHASHI
PY - 2014
DO - 10.1587/transinf.E97.D.663
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2014
AB - Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.
ER -