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Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Araghi, Sahar, Khosravi, Abbas and Creighton, Douglas 2015, Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network, Expert systems with applications, vol. 42, no. 9, pp. 4422-4431.

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Title Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network
Author(s) Araghi, Sahar
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Journal name Expert systems with applications
Volume number 42
Issue number 9
Start page 4422
End page 4431
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-06-01
ISSN 0957-4174
Keyword(s) ANFIS
Cuckoo search
Fuzzy logic systems
Machine learning
Neural Network
Q-learning
Traffic signal timing
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Operations Research & Management Science
Computer Science
Engineering
SYSTEM
APPROXIMATION
ARCHITECTURE
PREDICTION
CONGESTION
Summary Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q-learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q-learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075099

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Fri, 21 Aug 2015, 09:43:32 EST

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