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User Identification across Asynchronous Mobility Trajectories

With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates...

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Detalles Bibliográficos
Autores principales: Qi, Mengjun, Wang, Zhongyuan, He, Zheng, Shao, Zhenfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539004/
https://www.ncbi.nlm.nih.gov/pubmed/31067660
http://dx.doi.org/10.3390/s19092102
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author Qi, Mengjun
Wang, Zhongyuan
He, Zheng
Shao, Zhenfeng
author_facet Qi, Mengjun
Wang, Zhongyuan
He, Zheng
Shao, Zhenfeng
author_sort Qi, Mengjun
collection PubMed
description With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.
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spelling pubmed-65390042019-06-04 User Identification across Asynchronous Mobility Trajectories Qi, Mengjun Wang, Zhongyuan He, Zheng Shao, Zhenfeng Sensors (Basel) Article With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification. MDPI 2019-05-07 /pmc/articles/PMC6539004/ /pubmed/31067660 http://dx.doi.org/10.3390/s19092102 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Qi, Mengjun
Wang, Zhongyuan
He, Zheng
Shao, Zhenfeng
User Identification across Asynchronous Mobility Trajectories
title User Identification across Asynchronous Mobility Trajectories
title_full User Identification across Asynchronous Mobility Trajectories
title_fullStr User Identification across Asynchronous Mobility Trajectories
title_full_unstemmed User Identification across Asynchronous Mobility Trajectories
title_short User Identification across Asynchronous Mobility Trajectories
title_sort user identification across asynchronous mobility trajectories
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539004/
https://www.ncbi.nlm.nih.gov/pubmed/31067660
http://dx.doi.org/10.3390/s19092102
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