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Smart city article details

Title	User Community Identification Through Fine-Grained Mobility Records For Smart City Applications
ID_Doc	60393
Authors	Ferreira D.L.; Nunes B.A.A.; Campos C.A.V.; Obraczka K.
Year	2022
Published	IEEE Transactions on Intelligent Transportation Systems, 23, 5
DOI	http://dx.doi.org/10.1109/TITS.2020.3044328
Abstract	Motivated by Smart City applications and services, this article presents a novel approach to identifying user communities in communication networks. We define user communities as groups of users that share common mobility features over spatio-temporal scales of arbitrary length, such as time spent in certain locales, mobility speed, and time between consecutive movements. We describe our user community identification framework in detail including how mobility features can be extracted from real mobility traces (as examples of unlabelled data) and synthetic mobility records (as examples of labeled data). We present results obtained when using our approach in four distinct mobility scenarios represented by both unlabeled and labeled datasets. We also introduce a new validation methodology that uses image-based similarity metrics in order to assess the quality of identified communities. Our results show that the proposed approach significantly increases similarity between users within the same community as well as dissimilarity between users in different communities. We also demonstrate that the proposed user community identification approach yields significant increase in contact time amongst users belonging to the same community when compared to the average contact time when not considering community structures. © 2000-2011 IEEE.
Author Keywords	Clustering methods; geospatial analysis; mobile applications; pattern analysis; principal component analysis; unsupervised learning

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