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A Visual Analytics Approach for Traffic Flow Prediction Ensembles
<span title="">2018</span>
<i title="The Eurographics Association">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b7jv7gwrbfg6ja4cdhjozcc6sa" style="color: black;">Pacific Conference on Computer Graphics and Applications</a>
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Traffic 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
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... 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.
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