Extracting Visited Points of Interest from Vehicle Trajectories

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2 Citationer (Scopus)

Resumé

Identifying visited points of interest (PoIs) from vehicle trajectories remains an open problem that is difficult due to vehicles parking often at some distance from the visited PoI and due to some regions having a high PoI density. We propose a visited PoI extraction (VPE) method that identifies visited PoIs using a Bayesian network. The method considers stay duration, weekday, arrival time, and PoI category to compute the probability that a PoI is visited. We also provide a method to generate labeled data from unlabeled GPS trajectories. An experimental evaluation shows that VPE achieves a precision@3 value of 0.8, indicating that VPE is able to model the relationship between the temporal features of a stop and the category of the visited PoI.
OriginalsprogEngelsk
TitelProceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data
Antal sider6
ForlagAssociation for Computing Machinery
Publikationsdato2017
Artikelnummer2
ISBN (Elektronisk)978-1-4503-5047-1
DOI
StatusUdgivet - 2017
BegivenhedFourth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data - Chicago, USA
Varighed: 14 maj 201719 maj 2017

Workshop

WorkshopFourth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data
LandUSA
ByChicago
Periode14/05/201719/05/2017

Fingerprint

Trajectories
Parking
Bayesian networks
Global positioning system

Citer dette

Keles, I., Schubert, M., Kröger, P., Saltenis, S., & Jensen, C. S. (2017). Extracting Visited Points of Interest from Vehicle Trajectories. I Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data [2] Association for Computing Machinery. https://doi.org/10.1145/3080546.3080552
Keles, Ilkcan ; Schubert, Matthias ; Kröger, Peer ; Saltenis, Simonas ; Jensen, Christian Søndergaard. / Extracting Visited Points of Interest from Vehicle Trajectories. Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data. Association for Computing Machinery, 2017.
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title = "Extracting Visited Points of Interest from Vehicle Trajectories",
abstract = "Identifying visited points of interest (PoIs) from vehicle trajectories remains an open problem that is difficult due to vehicles parking often at some distance from the visited PoI and due to some regions having a high PoI density. We propose a visited PoI extraction (VPE) method that identifies visited PoIs using a Bayesian network. The method considers stay duration, weekday, arrival time, and PoI category to compute the probability that a PoI is visited. We also provide a method to generate labeled data from unlabeled GPS trajectories. An experimental evaluation shows that VPE achieves a precision@3 value of 0.8, indicating that VPE is able to model the relationship between the temporal features of a stop and the category of the visited PoI.",
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Keles, I, Schubert, M, Kröger, P, Saltenis, S & Jensen, CS 2017, Extracting Visited Points of Interest from Vehicle Trajectories. i Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data., 2, Association for Computing Machinery, Chicago, USA, 14/05/2017. https://doi.org/10.1145/3080546.3080552

Extracting Visited Points of Interest from Vehicle Trajectories. / Keles, Ilkcan; Schubert, Matthias; Kröger, Peer; Saltenis, Simonas; Jensen, Christian Søndergaard.

Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data. Association for Computing Machinery, 2017. 2.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AU - Schubert, Matthias

AU - Kröger, Peer

AU - Saltenis, Simonas

AU - Jensen, Christian Søndergaard

PY - 2017

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AB - Identifying visited points of interest (PoIs) from vehicle trajectories remains an open problem that is difficult due to vehicles parking often at some distance from the visited PoI and due to some regions having a high PoI density. We propose a visited PoI extraction (VPE) method that identifies visited PoIs using a Bayesian network. The method considers stay duration, weekday, arrival time, and PoI category to compute the probability that a PoI is visited. We also provide a method to generate labeled data from unlabeled GPS trajectories. An experimental evaluation shows that VPE achieves a precision@3 value of 0.8, indicating that VPE is able to model the relationship between the temporal features of a stop and the category of the visited PoI.

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Keles I, Schubert M, Kröger P, Saltenis S, Jensen CS. Extracting Visited Points of Interest from Vehicle Trajectories. I Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data. Association for Computing Machinery. 2017. 2 https://doi.org/10.1145/3080546.3080552