dc.contributor.authorSng, Darrel Jia Hong
dc.date.accessioned2016-12-14T01:22:55Z
dc.date.available2016-12-14T01:22:55Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10356/69332
dc.description.abstractThis report discusses the expansion process of a simulation model utilizing VISSIM. It also presents the data recorded both of the expanded map and the findings of the data recorded in the real world. PTV VISSIM, which was utilized for this project, is a traffic simulation program capable of integrating multiple variants such as those required of this project; providing the user with critical feedback on traffic flow. Signal controllers are assigned different timing intervals. Initially, all traffic inputs and route settings are dynamically generated using the dynamic assignment module of VISSIM. Values such as vehicle volume and signal controller timings are then continually modified using data collected in the field. The model was expanded into the Jurong West area which presented more dynamic and challenging situations to simulate such as roundabouts, expressways and converging links which will be discussed in a later chapter.en_US
dc.format.extent48 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineeringen_US
dc.titleApplication of extreme learning machine techniques in traffic network parameter estimationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSu Rongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US


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