Adaptive Fuzzy Logic Traffic Signal Control Based on Cuckoo Search Algorithm

Traffic congestion becomes a big problem to perplex the current society. Effective traffic signal control can alleviate traffic congestion, especially for real-time traffic signal control. To improve the control efficiency, fuzzy logic control based on cuckoo search algorithm is applied to solve the problem of real-time traffic signal control. Research object is multi-lane four-phase single intersection which is also the commonly intersection in reality. Vehicular evaluation index model is established firstly. Then, the appropriate green time is given by the cuckoo search algorithm and fuzzy logic control according to the number of real-time road vehicles. Through simulation experiments, the proposed method based on the fuzzy logic control optimized by cuckoo search algorithm can be verified to obtain a good effect. This method also suits for other complex nonlinear systems.

[1]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[2]  Fei-Yue Wang,et al.  A Novel Approach for Traffic Signal Control: A Recommendation Perspective , 2017, IEEE Intelligent Transportation Systems Magazine.

[3]  C. P. Pappis,et al.  A Fuzzy Logic Controller for a Trafc Junction , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Karima Benhamza,et al.  Adaptive traffic signal control in multiple intersections network , 2015, J. Intell. Fuzzy Syst..

[5]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[6]  Xueying Liu,et al.  Cuckoo search algorithm based on frog leaping local search and chaos theory , 2015, Appl. Math. Comput..

[7]  Hamid Saadat Talab,et al.  Design optimization traffic light timing using the fuzzy logic at a Diphasic's Isolated intersection , 2014, J. Intell. Fuzzy Syst..

[8]  Dipti Srinivasan,et al.  Distributed Geometric Fuzzy Multiagent Urban Traffic Signal Control , 2010, IEEE Transactions on Intelligent Transportation Systems.

[9]  Dipti Srinivasan,et al.  Neural Networks for Real-Time Traffic Signal Control , 2006, IEEE Transactions on Intelligent Transportation Systems.

[10]  C Pappis,et al.  A FUZZY CONTROLLER FOR A TRAFFIC JUNCTION. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS (IEEE) , 1977 .

[11]  Yetis Sazi Murat,et al.  A FUZZY LOGIC MULTI-PHASED SIGNAL CONTROL MODEL FOR ISOLATED JUNCTIONS , 2005 .

[12]  Ning Wang,et al.  Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks , 2017, Appl. Soft Comput..

[13]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[14]  Dongbin Zhao,et al.  Computational Intelligence in Urban Traffic Signal Control: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Changxu Wu,et al.  Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences , 2015, IEEE Transactions on Intelligent Transportation Systems.

[16]  Fenghua Zhu,et al.  Parallel Transportation Management and Control System and Its Applications in Building Smart Cities , 2016, IEEE Transactions on Intelligent Transportation Systems.