https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Xie_et_al_2020bXie et al 2020b - Revision history2026-04-25T21:38:06ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Xie_et_al_2020b&diff=211259&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 246594940 to Xie et al 2020b2021-02-12T11:49:35Z<p>Scipediacontent moved page <a href="/public/Draft_Content_246594940" class="mw-redirect" title="Draft Content 246594940">Draft Content 246594940</a> to <a href="/public/Xie_et_al_2020b" title="Xie et al 2020b">Xie et al 2020b</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Xie_et_al_2020b&diff=211258&oldid=prevScipediacontent: Created page with " == Abstract == As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting pr..."2021-02-12T11:49:32Z<p>Created page with " == Abstract == As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting pr..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting provides a reliable traffic dispatch basis for intelligent transport, and most of the existing prediction methods only predict a single saturation or speed and do not use the saturation and speed in a unified way. This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB. First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph convolution neural network (GCN) model is used to obtain the time correlation of the traffic data, and finally, the traffic flow is predicted by the topology graph. The experimental results show that the method has a better performance than ARIMA, LSTM, and GCN.<br />
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Document type: Article<br />
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== Full document ==<br />
<pdf>Media:Draft_Content_246594940-beopen65-1314-document.pdf</pdf><br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
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* [http://dx.doi.org/10.1155/2020/7586154 http://dx.doi.org/10.1155/2020/7586154] under the license https://creativecommons.org/licenses/by/4.0/<br />
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* [http://downloads.hindawi.com/journals/jat/2020/7586154.pdf http://downloads.hindawi.com/journals/jat/2020/7586154.pdf],<br />
: [http://downloads.hindawi.com/journals/jat/2020/7586154.xml http://downloads.hindawi.com/journals/jat/2020/7586154.xml],<br />
: [http://dx.doi.org/10.1155/2020/7586154 http://dx.doi.org/10.1155/2020/7586154]<br />
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* [http://dx.doi.org/10.1155/2020/7586154 http://dx.doi.org/10.1155/2020/7586154],<br />
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],<br />
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license https://creativecommons.org/licenses/by/4.0/<br />
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* [https://www.hindawi.com/journals/jat/2020/7586154 https://www.hindawi.com/journals/jat/2020/7586154],<br />
: [http://downloads.hindawi.com/journals/jat/2020/7586154.pdf http://downloads.hindawi.com/journals/jat/2020/7586154.pdf],<br />
: [https://academic.microsoft.com/#/detail/3092713078 https://academic.microsoft.com/#/detail/3092713078]</div>Scipediacontent