Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2007.4424683
DC FieldValue
dc.titleMulti-agent system based urban traffic management
dc.contributor.authorBalaji, P.G.
dc.contributor.authorSachdeva, G.
dc.contributor.authorSrinivasan, D.
dc.contributor.authorTham, C.-K.
dc.date.accessioned2014-06-19T03:19:04Z
dc.date.available2014-06-19T03:19:04Z
dc.date.issued2007
dc.identifier.citationBalaji, P.G.,Sachdeva, G.,Srinivasan, D.,Tham, C.-K. (2007). Multi-agent system based urban traffic management. 2007 IEEE Congress on Evolutionary Computation, CEC 2007 : 1740-1747. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CEC.2007.4424683" target="_blank">https://doi.org/10.1109/CEC.2007.4424683</a>
dc.identifier.isbn1424413400
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71031
dc.description.abstractRoad Traffic congestion can occur anywhere from normal city roads, freeways to even highways. Traffic congestion can also be accentuated by incidents like terrorist attacks, accidents and breakdowns. This paper summarizes the use of various evolutionary techniques for traffic management and congestion avoidance in Intelligent Transportation Systems. Evolutionary algorithms with their inherent strength as optimization techniques are good candidates for solutions to road traffic management and congestion avoidance problems. A number of approaches involving the use of Genetic algorithms, Learning Classifier Systems and Genetic programming have been discussed for solutions to different problems In this domain. This paper proposes a multi-agent based real-time centralized evolutionary optimization technique for urban traffic management in the area of traffic signal control. This scheme uses evolutionary strategy for the control of traffic signal. The total vehicle mean delay in a six junction network was reduced by using evolutionary strategy. In order to achieve this the green signal time was optimized in an online manner. Comparison with a fixed time based traffic controller has been made and was found to produce better results. ©2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CEC.2007.4424683
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CEC.2007.4424683
dc.description.sourcetitle2007 IEEE Congress on Evolutionary Computation, CEC 2007
dc.description.page1740-1747
dc.identifier.isiutNOT_IN_WOS
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