Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves

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dc.contributor.authorSilva, Carlaen_US
dc.contributor.authord'Orey, Pedroen_US
dc.contributor.authorAguiar, Anaen_US
dc.contributor.editorArchambault, Daniel and Nabney, Ian and Peltonen, Jaakkoen_US
dc.date.accessioned2019-06-02T18:23:43Z
dc.date.available2019-06-02T18:23:43Z
dc.date.issued2019
dc.description.abstractTraffic congestion causes major economic, environmental and social problems in modern cities. We present an interactive visualization tool to assist domain experts on the identification and analysis of traffic patterns at a city scale making use of multivariate empirical urban data and fundamental diagrams. The proposed method combines visualization techniques with an improved local principle curves method to model traffic dynamics and facilitate comparison of traffic patterns - resorting to the fitted curve with a confidence interval - between different road segments and for different external conditions. We demonstrate the proposed technique in an illustrative real-world case study in the city of Porto, Portugal.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20191159
dc.identifier.isbn978-3-03868-089-5
dc.identifier.pages13-17
dc.identifier.urihttps://doi.org/10.2312/mlvis.20191159
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mlvis20191159
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectEmpirical studies in visualization
dc.subjectComputing methodologies
dc.subjectMachine learning approaches
dc.subjectModeling and simulation
dc.subjectShape modeling"
dc.titleVisual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curvesen_US
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Machine Learning Methods in Visualisation for Big Data 2019