[1]Dong Hailong,Tang Minan,Cheng Haipeng.Research on Intelligent Control of Traffic Signal for Five-road Intersection of Unbalanced Traffic Flow[J].Journal of Zhengzhou University (Engineering Science),2017,38(01):68-.[doi:10.13705/j.issn.1671-6833.2017.01.005]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
38
Number of periods:
2017 01
Page number:
68-
Column:
Public date:
2017-02-24
- Title:
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Research on Intelligent Control of Traffic Signal for Five-road Intersection of Unbalanced Traffic Flow
- Author(s):
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Dong Hailong; Tang Minan; Cheng Haipeng
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School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070
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- Keywords:
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five-road intersection; traffic signalfuzzy neural network; artificial fish swarm algorithm; average delay of vehicles
- CLC:
-
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- DOI:
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10.13705/j.issn.1671-6833.2017.01.005
- Abstract:
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In order to alleviate traffic congestion and reduce the time of vehicles waiting,of unbalaned intersection,this paper adopted a traffic signal control method based on artificial fish swarm algorithm,which could achieve multi-phase and variable phase sequence intelligent control at five-road intersection.Firstly,the passable urgency of a red light phase was decided according to its vehicle queuing length and the red light duration;and the highest passable urgency phase should be the next green light phase.Secondly,the current vehicle queuing length of green light phase and the difference between vehicle queuing length of the next and current green light phase were taken as the inputs.The fuzzy neural network controller was used to control delays of the green light.In order to avoid fuzzy neural network falling into local minimum,artificial fish swarm algorithm had optimized the parameters of it.After simulation studies in the case of different rates of vehicle arrival,the results showed that this method was better than the traditional control in automatically adjusting the signal cycle,which reduced the average delay of vehicles for about 7.2%.