Optimal design of traffic signal controller using neural networks and fuzzy logic systems

Araghi,S, Khosravi,A and Creighton,D 2014, Optimal design of traffic signal controller using neural networks and fuzzy logic systems, in IJCNN 2014 : Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers, Piscataway, N.J., pp. 42-47, doi: 10.1109/IJCNN.2014.6889477.

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Title Optimal design of traffic signal controller using neural networks and fuzzy logic systems
Author(s) Araghi,S
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Creighton,DORCID iD for Creighton,D orcid.org/0000-0002-9217-1231
Conference name Neural Networks. Joint Conference (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings IJCNN 2014 : Proceedings of the International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2014
Conference series International Joint Conference on Neural Networks
Start page 42
End page 47
Total pages 6
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Summary This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Neural network (NN) and fuzzy logic system (FLS) are two methods applied to develop intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The training approach and data for both these learning methods are similar. Both methods use genetic algorithm to tune their parameters during learning. Finally, The performance of the two intelligent learning methods is compared with the performance of simple fixed-time method. Simulation results indicate that both intelligent methods significantly reduce the total delay in the network compared to the fixed-time method.
ISBN 9781479914845
Language eng
DOI 10.1109/IJCNN.2014.6889477
Field of Research 080101 Adaptive Agents and Intelligent Robotics
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071607

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
2018 ERA Submission
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