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dc.contributor.authorTung, Sau Waien
dc.contributor.authorQuek, Chaien
dc.contributor.authorGuan, Cuntaien
dc.identifier.citationTung, S. W., Quek, C., & Guan, C. (2012). Traffic modeling and identification using a self-adaptive fuzzy inference network. The 2012 International Joint Conference on Neural Networks (IJCNN).en
dc.description.abstractTraffic modeling and identification is an important aspect of traffic control today. With an increase in the demands on today's transportation network, an efficient system to model and understand the changes in the network is necessary for policy makers to make timely decisions which affect the overall level of service experienced by commuters. This paper proposes a novel approach to traffic modeling and identification using a Self-adaptive Fuzzy Inference Network (SaFIN). The study is performed on a set of real world traffic data collected along the Pan Island Expressway (PIE) in Singapore. By applying a hybrid fuzzy neural network in the traffic modeling task, SaFIN is able to capitalize on the functionalities of both the fuzzy system and the neural network to (1) provide meaningful and intuitive insights to the traffic data, and (2) demonstrate excellent modeling and identification capabilities for highly nonlinear traffic flow conditions.en
dc.rights© 2012 IEEE.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleTraffic modeling and identification using a self-adaptive fuzzy inference networken
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceInternational Joint Conference on Neural Networks (2012 : Brisbane, Australia)en
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