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Title	Mobvis: A Framework For Analysis And Visualization Of Mobility Traces
ID_Doc	37389
Authors	Silva L.N.; Rettore P.H.L.; Mota V.F.S.; Santos B.P.
Year	2022
Published	Proceedings - IEEE Symposium on Computers and Communications, 2022-June
DOI	http://dx.doi.org/10.1109/ISCC55528.2022.9912988
Abstract	Due to the increasing location-aware devices, mobility traces datasets have become an essential source for smart cities planning. Given this scenario, we propose MobVis, a framework to characterize mobility traces through different metrics, allowing comparisons between different mobility traces in a simplified way. Furthermore, MobVis can extract and visualize spatial, temporal, and social aspects of mobility data through a Web interface. MobVis architecture has five main components: input data; data preparation; data processing and analysis to extract mobility metrics; visualization; and a web interface. To demonstrate the framework's process, we created a use case analyzing the characteristics of two distinct traces (Taxi and IoT-Objects). Then, through different metrics, we evaluated the data in two aspects: i) descriptive, through a set of graphics and quantitative data that enables characterizing each trace; and ii) comparative, presenting the main differences between the traces. © 2022 IEEE.
Author Keywords	IoT; ITS; Mobility; Smart Cities

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