https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Dahal_2007aDahal 2007a - Revision history2026-04-14T18:19:36ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Dahal_2007a&diff=174576&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 738219974 to Dahal 2007a2020-10-14T12:44:36Z<p>Scipediacontent moved page <a href="/public/Draft_Content_738219974" class="mw-redirect" title="Draft Content 738219974">Draft Content 738219974</a> to <a href="/public/Dahal_2007a" title="Dahal 2007a">Dahal 2007a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Dahal_2007a&diff=174575&oldid=prevScipediacontent: Created page with " == Abstract == When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or c..."2020-10-14T12:44:34Z<p>Created page with " == Abstract == When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or c..."</p>
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== Abstract ==<br />
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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.<br />
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Document type: Part of book or chapter of book<br />
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== Full document ==<br />
<pdf>Media:Draft_Content_738219974-beopen617-2914-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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* [https://bradscholars.brad.ac.uk/bitstream/10454/2554/1/dahal7-2.pdf https://bradscholars.brad.ac.uk/bitstream/10454/2554/1/dahal7-2.pdf]</div>Scipediacontent