A Visual Analytics Approach for Traffic Flow Prediction Ensembles

Kezhi Kong, Yuxin Ma, Chentao Ye, Junhua Lu, Xiqun Chen, Wei Zhang, Wei Chen
<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> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2312/pg.20181281">doi:10.2312/pg.20181281</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/pg/KongMYLCZC18.html">dblp:conf/pg/KongMYLCZC18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yhyjbafxzbhd3gyyuekr6d2vey">fatcat:yhyjbafxzbhd3gyyuekr6d2vey</a> </span>
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