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dc.contributor.authorAlmejalli, Khaled A.*
dc.contributor.authorDahal, Keshav P.*
dc.contributor.authorHossain, M. Alamgir*
dc.date.accessioned2009-04-09T08:56:41Z
dc.date.available2009-04-09T08:56:41Z
dc.date.issued2007
dc.identifier.citationAlmejalli, K. A., Dahal, K. P. and Hossain, M. A. (2007) Intelligent traffic control decision support system. In: Applications of evolutionary computing. Proceedings of the EvoWorkshops 2007 (EvoTransLog Workshop), Valencia, Spain, April 11-13, 2007. Heidelberg: Springer. pp. 688-701.en
dc.identifier.urihttp://hdl.handle.net/10454/2554
dc.description.abstractWhen 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.en
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.relation.isreferencedbyhttp://www.springerlink.com/en
dc.rights© 2007 Springer-Verlag. Reproduced in accordance with the publisher's self-archiving policy.en
dc.subjectTraffic managementen
dc.subjectDecision support systemsen
dc.subjectFuzzy logicen
dc.titleIntelligent traffic control decision support systemen
dc.status.refereedYesen
dc.typeConference paperen
dc.type.versionAccepted Manuscripten
refterms.dateFOA2018-07-18T13:35:45Z


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