A Visual Analytics Approach for Traffic Flow Prediction Ensembles

dc.contributor.authorKong, Kezhien_US
dc.contributor.authorMa, Yuxinen_US
dc.contributor.authorYe, Chentaoen_US
dc.contributor.authorLu, Junhuaen_US
dc.contributor.authorChen, Xiqunen_US
dc.contributor.authorZhang, Weien_US
dc.contributor.authorChen, Weien_US
dc.contributor.editorFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannesen_US
dc.date.accessioned2018-10-07T14:32:23Z
dc.date.available2018-10-07T14:32:23Z
dc.date.issued2018
dc.description.abstractTraffic flow prediction plays a significant role in Intelligent Transportation Systems (ITS). Due to the variety of prediction models, the prediction results form an intricate structure of ensembles and hence leave a challenge of understanding and evaluating the ensembles from different perspectives. In this paper, we propose a novel visual analytics approach for analyzing the predicted ensembles. Our approach models the uncertainty of different traffic flow prediction results. The variations of space, time, and network structures of those results are presented with the visualization designs. The visual interface provides a suite of interactions to enhance exploration of the ensembles. With the system, analysts can discover some intrinsic patterns in the ensemble. We use real-world urban traffic data to demonstrate the effectiveness of our system.en_US
dc.description.sectionheadersVisualization and GPU
dc.description.seriesinformationPacific Graphics Short Papers
dc.identifier.doi10.2312/pg.20181281
dc.identifier.isbn978-3-03868-073-4
dc.identifier.pages61-64
dc.identifier.urihttps://doi.org/10.2312/pg.20181281
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20181281
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytic
dc.titleA Visual Analytics Approach for Traffic Flow Prediction Ensemblesen_US
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