Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97930
Title: Traffic modeling and identification using a self-adaptive fuzzy inference network
Authors: Tung, Sau Wai
Quek, Chai
Guan, Cuntai
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Tung, 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).
Abstract: Traffic 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.
URI: https://hdl.handle.net/10356/97930
http://hdl.handle.net/10220/12408
DOI: 10.1109/IJCNN.2012.6252621
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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