https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Gao_et_al_2018bGao et al 2018b - Revision history2026-04-25T09:00:45ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Gao_et_al_2018b&diff=204777&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 193473662 to Gao et al 2018b2021-02-03T15:34:08Z<p>Scipediacontent moved page <a href="/public/Draft_Content_193473662" class="mw-redirect" title="Draft Content 193473662">Draft Content 193473662</a> to <a href="/public/Gao_et_al_2018b" title="Gao et al 2018b">Gao et al 2018b</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Gao_et_al_2018b&diff=204776&oldid=prevScipediacontent: Created page with " == Abstract == Back-pressure algorithm has been shown to be effective in reducing traffic congestion. However, available works on back-pressure based traffic control usually..."2021-02-03T15:34:05Z<p>Created page with " == Abstract == Back-pressure algorithm has been shown to be effective in reducing traffic congestion. However, available works on back-pressure based traffic control usually..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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Back-pressure algorithm has been shown to be effective in reducing traffic congestion. However, available works on back-pressure based traffic control usually ignore the fact that vehicles need time to travel across roads, resulting in inconsistency between controllers' viewpoint of traffic congestion situation and real traffic situation and thus misleading controllers. In this paper, we propose back-pressure based adaptive traffic signal control and vehicle routing with real-time control information update such that controllers always have consistent viewpoint of traffic congestion with real traffic situation and make wise signal control and vehicle routing decisions. As verified by simulations, our algorithm significantly reduces traffic congestion. For example, it reduces average vehicle travelling time by percentage ranging from 71% to 87% under high vehicle arrival rates when compared to other three algorithms.<br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
<br />
* [http://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100236545 http://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100236545]<br />
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* [http://xplorestaging.ieee.org/ielx7/8506478/8519482/08519601.pdf?arnumber=8519601 http://xplorestaging.ieee.org/ielx7/8506478/8519482/08519601.pdf?arnumber=8519601],<br />
: [http://dx.doi.org/10.1109/icves.2018.8519601 http://dx.doi.org/10.1109/icves.2018.8519601]<br />
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* [https://library.naist.jp/dspace/handle/10061/13255?show=full https://library.naist.jp/dspace/handle/10061/13255?show=full],<br />
: [http://library.naist.jp/dspace/handle/10061/13255?show=full http://library.naist.jp/dspace/handle/10061/13255?show=full],<br />
: [https://dblp.uni-trier.de/db/conf/icves/icves2018.html#LiuGI18 https://dblp.uni-trier.de/db/conf/icves/icves2018.html#LiuGI18],<br />
: [https://academic.microsoft.com/#/detail/2899555245 https://academic.microsoft.com/#/detail/2899555245]</div>Scipediacontent