Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/69332
Title: Application of extreme learning machine techniques in traffic network parameter estimation
Authors: Sng, Darrel Jia Hong
Keywords: DRNTU::Engineering
Issue Date: 2016
Abstract: This 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.
URI: http://hdl.handle.net/10356/69332
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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