Cargando…
A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
Analyzing human mobility with geo-location data collected from smartphones has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns using the GPS data. In particular, we view this problem from a probabilistic perspective. A non-parametric Bayesian mo...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274334/ http://dx.doi.org/10.1007/978-3-030-50146-4_34 |
_version_ | 1783542558899568640 |
---|---|
author | Qian, Weizhu Lauri, Fabrice Gechter, Franck |
author_facet | Qian, Weizhu Lauri, Fabrice Gechter, Franck |
author_sort | Qian, Weizhu |
collection | PubMed |
description | Analyzing human mobility with geo-location data collected from smartphones has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns using the GPS data. In particular, we view this problem from a probabilistic perspective. A non-parametric Bayesian modeling method, the Infinite Gaussian Mixture Model (IGMM) is used to estimate the probability density of the daily mobility. We also utilize the Kullback-Leibler (KL) divergence as the metrics to measure the similarity of different probability distributions. Combining the IGMM and the KL divergence, we propose an automatic clustering algorithm to discover mobility patterns for each individual user. Finally, the effectiveness of our method is validated on the real user data collected from different real users. |
format | Online Article Text |
id | pubmed-7274334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72743342020-06-05 A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data Qian, Weizhu Lauri, Fabrice Gechter, Franck Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Analyzing human mobility with geo-location data collected from smartphones has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns using the GPS data. In particular, we view this problem from a probabilistic perspective. A non-parametric Bayesian modeling method, the Infinite Gaussian Mixture Model (IGMM) is used to estimate the probability density of the daily mobility. We also utilize the Kullback-Leibler (KL) divergence as the metrics to measure the similarity of different probability distributions. Combining the IGMM and the KL divergence, we propose an automatic clustering algorithm to discover mobility patterns for each individual user. Finally, the effectiveness of our method is validated on the real user data collected from different real users. 2020-05-18 /pmc/articles/PMC7274334/ http://dx.doi.org/10.1007/978-3-030-50146-4_34 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Qian, Weizhu Lauri, Fabrice Gechter, Franck A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data |
title | A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data |
title_full | A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data |
title_fullStr | A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data |
title_full_unstemmed | A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data |
title_short | A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data |
title_sort | probabilistic approach for discovering daily human mobility patterns with mobile data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274334/ http://dx.doi.org/10.1007/978-3-030-50146-4_34 |
work_keys_str_mv | AT qianweizhu aprobabilisticapproachfordiscoveringdailyhumanmobilitypatternswithmobiledata AT laurifabrice aprobabilisticapproachfordiscoveringdailyhumanmobilitypatternswithmobiledata AT gechterfranck aprobabilisticapproachfordiscoveringdailyhumanmobilitypatternswithmobiledata AT qianweizhu probabilisticapproachfordiscoveringdailyhumanmobilitypatternswithmobiledata AT laurifabrice probabilisticapproachfordiscoveringdailyhumanmobilitypatternswithmobiledata AT gechterfranck probabilisticapproachfordiscoveringdailyhumanmobilitypatternswithmobiledata |