Research of Traffic Signal Control Strategy Based on the Fuzzy Control

Xue Bo Yan

doi:10.4028/www.scientific.net/AMM.651-653.486

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Research of Traffic Signal Control Strategy Based...

Research of Traffic Signal Control Strategy Based on the Fuzzy Control

Abstract:

To ease the traffic pressure on urban traffic signal control strategy research started. Dynamic change prediction analysis of traffic flow through the flow of information as a basis for fuzzy reasoning, automatically adjust the signal cycle, green ratio and phase control parameters, real-time signal timing to generate optimal solutions for optimal control effect. The results show that this method can effectively alleviate traffic congestion, meet the design expectations.

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Periodical:

Applied Mechanics and Materials (Volumes 651-653)

Pages:

486-490

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.651-653.486

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Online since:

September 2014

Authors:

Xue Bo Yan*

Keywords:

Intelligent Transportation System (ITS), Split Green, Traffic Light

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* - Corresponding Author

References

[1] De Oliveira L. B, Camponogara E. Multi-agent model predictive control of signaling split in urban traffic networks[J]. Transportation Research Part C: Emerging Technologies, 2010, 18(1): 120-139.

DOI: 10.1016/j.trc.2009.04.022

Google Scholar

[2] Lee J, Park B. Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm under the Connected Vehicles Environment [J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(1): 81-90.

DOI: 10.1109/tits.2011.2178836

Google Scholar

[3] Arel 1, Liu C, Urbanik T. Reinforcement learning-based multi-agent system for network traffic signal control [J], IEEE Transactions on Intelligent Transport Systems, 2010, 4(2): 128-135.

DOI: 10.1049/iet-its.2009.0070

Google Scholar

[4] Menouar H, Lenardi M, Filali F. An Intelligent Movement-based Routing for VANETs[C]. ITS World Congress 2006, London, United Kingdom, 2006: 1-8.

Google Scholar

[5] Zhou Binbin, Chen Luyi. Research on Traffic Signals Real-time Scheduling [J]. Journal of Zhenjiang Shoren University, 2014, 14(3): 1-4.

Google Scholar

[6] Lee P S, Lee C S, Lee J H. Development of FPGA-based digital signal processing system for radiation spectroscopy[J]. Radiation Measuements, 2013, 48: 12-17.

DOI: 10.1016/j.radmeas.2012.11.018

Google Scholar