https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Gao_et_al_2019aGao et al 2019a - Revision history2026-04-21T21:45:17ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Gao_et_al_2019a&diff=201843&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 464915896 to Gao et al 2019a2021-02-02T04:30:47Z<p>Scipediacontent moved page <a href="/public/Draft_Content_464915896" class="mw-redirect" title="Draft Content 464915896">Draft Content 464915896</a> to <a href="/public/Gao_et_al_2019a" title="Gao et al 2019a">Gao et al 2019a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Gao_et_al_2019a&diff=201842&oldid=prevScipediacontent: Created page with " == Abstract == Nowadays traffic congestion has increasingly been a significant problem, which results in longer travel time and aggravates air pollution. Available work show..."2021-02-02T04:30:42Z<p>Created page with " == Abstract == Nowadays traffic congestion has increasingly been a significant problem, which results in longer travel time and aggravates air pollution. Available work show..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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Nowadays traffic congestion has increasingly been a significant problem, which results in longer travel time and aggravates air pollution. Available work showed that backpressure based traffic control algorithms can effectively reduce traffic congestion. However, those work control traffic based on either inaccurate traffic information or local traffic information, which causes inefficient traffic scheduling. In this paper, we propose an adaptive traffic control algorithm based on backpressure and Q-learning, which can efficiently reduce congestion. Our algorithm controls traffic based on accurate real-time traffic information and global traffic information learned by Q-learning. As verified by simulation, our algorithm significantly decreases average vehicle traveling time from 16% to 36% when compared with state-of-the-art algorithm under tested scenarios.<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://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100240835 http://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100240835]<br />
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* [http://xplorestaging.ieee.org/ielx7/8907344/8916833/08917179.pdf?arnumber=8917179 http://xplorestaging.ieee.org/ielx7/8907344/8916833/08917179.pdf?arnumber=8917179],<br />
: [http://dx.doi.org/10.1109/itsc.2019.8917179 http://dx.doi.org/10.1109/itsc.2019.8917179]<br />
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* [https://dblp.uni-trier.de/db/conf/itsc/itsc2019.html#MaipraditGKI19 https://dblp.uni-trier.de/db/conf/itsc/itsc2019.html#MaipraditGKI19],<br />
: [http://library.naist.jp/dspace/handle/10061/13438 http://library.naist.jp/dspace/handle/10061/13438],<br />
: [https://academic.microsoft.com/#/detail/2991602722 https://academic.microsoft.com/#/detail/2991602722]</div>Scipediacontent