Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system

LIU Yu-gang; WANG Zhuo-jun; LIU Yan-fang; YUAN Chuan-jie

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Article Navigation > Journal of Traffic and Transportation Engineering > 2017 > 17(4): 149-158

LIU Yu-gang, WANG Zhuo-jun, LIU Yan-fang, YUAN Chuan-jie. Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system[J]. Journal of Traffic and Transportation Engineering, 2017, 17(4): 149-158.

Citation:

LIU Yu-gang, WANG Zhuo-jun, LIU Yan-fang, YUAN Chuan-jie. Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system[J]. Journal of Traffic and Transportation Engineering, 2017, 17(4): 149-158.

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Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
2.
National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

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Author Bio:
LIU Yu-gang(1978-), male, associate professor, PhD, +86-28-87600165, liuyugang@home.swjtu.edu.cn
Received Date: 2017-03-22
Publish Date: 2017-08-25

Abstract

Abstract

In order to alleviate the traffic congestion caused by the uneven distribution of traffic flow, the variable approach lanes (VAL) of intersection entrance were taken as research object, and an intelligent control system based on adaptive neuro-fuzzy inference system (ANFIS) was established. The intelligent control system consisted of data acquisition subsystem, traffic status prediction subsystem and control subsystem, and the intelligent control of VAL was completed by the three subsystems. When the real-time traffic data detected by the data acquisition subsystem were transfered into the pre-trained traffic status prediction subsystem, the traffic statuses of left-turning and going-straight vehicles were obtained, and the attribute of VAL was determined according to the structured algorithm. Computation result shows that the test error of traffic status prediction subsystem is 0.075 097, which meets the accuracy requirement to predict the traffic status. The intelligent control system of VAL can significantly improve the trafficcongestion at the intersection. While the ratio of left-turning vehicles is 25%, the total delay of key entrance lane reduces by 6.1%, the average stopping number reduces by 9.5%, and the average queue length reduces by 6.1%. When the ratio of left-turning vehicles rises to 30%, the three indicators decrease by 8.1%, 12.4% and 8.0%, respectively. Obviously, the higher the proportion of left-turning vehicles is, the more significant the effect is.
- traffic control,
- intersection,
- variable approach lane,
- traffic status,
- control strategy,
- adaptive neuro-fuzzy inference system

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References(28)

References

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