Traffic modeling and identification using a self-adaptive fuzzy inference network

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...

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Main Authors: Tung, Sau Wai, Quek, Chai, Guan, Cuntai
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/97930
http://hdl.handle.net/10220/12408
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-979302020-05-28T07:41:33Z Traffic modeling and identification using a self-adaptive fuzzy inference network Tung, Sau Wai Quek, Chai Guan, Cuntai School of Computer Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Computer science and engineering 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. 2013-07-26T07:10:04Z 2019-12-06T19:48:27Z 2013-07-26T07:10:04Z 2019-12-06T19:48:27Z 2012 2012 Conference Paper 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). https://hdl.handle.net/10356/97930 http://hdl.handle.net/10220/12408 10.1109/IJCNN.2012.6252621 en © 2012 IEEE.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Tung, Sau Wai
Quek, Chai
Guan, Cuntai
Traffic modeling and identification using a self-adaptive fuzzy inference network
description 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.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Tung, Sau Wai
Quek, Chai
Guan, Cuntai
format Conference or Workshop Item
author Tung, Sau Wai
Quek, Chai
Guan, Cuntai
author_sort Tung, Sau Wai
title Traffic modeling and identification using a self-adaptive fuzzy inference network
title_short Traffic modeling and identification using a self-adaptive fuzzy inference network
title_full Traffic modeling and identification using a self-adaptive fuzzy inference network
title_fullStr Traffic modeling and identification using a self-adaptive fuzzy inference network
title_full_unstemmed Traffic modeling and identification using a self-adaptive fuzzy inference network
title_sort traffic modeling and identification using a self-adaptive fuzzy inference network
publishDate 2013
url https://hdl.handle.net/10356/97930
http://hdl.handle.net/10220/12408
_version_ 1681056659559940096