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Optimal design of traffic signal controller using neural networks and fuzzy logic systems
conference contribution
posted on 2014-01-01, 00:00 authored by Sahar Araghi, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas CreightonThis 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.
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Event
Neural Networks. Joint Conference (2014 : Beijing, China)Series
International Joint Conference on Neural NetworksPagination
42 - 47Publisher
Institute of Electrical and Electronics EngineersLocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISBN-13
9781479914845Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, Institute of Electrical and Electronics EngineersEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2014 : Proceedings of the International Joint Conference on Neural NetworksUsage metrics
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