Silva, Carlad'Orey, PedroAguiar, AnaArchambault, Daniel and Nabney, Ian and Peltonen, Jaakko2019-06-022019-06-022019978-3-03868-089-5https://doi.org/10.2312/mlvis.20191159https://diglib.eg.org:443/handle/10.2312/mlvis20191159Traffic 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.Humancentered computingVisual analyticsEmpirical studies in visualizationComputing methodologiesMachine learning approachesModeling and simulationShape modeling"10.2312/mlvis.2019115913-17