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Distributed Traffic Signal Control Using PSO Based on Probability Model for Traffic Jam

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Intelligent Autonomous Systems 12

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 193))

Abstract

In this article, a new traffic signal control method is proposed. The Bayesian Network (BN) model and the Cellular Automaton (CA) model are used to build up a probability model for traffic jam. And then using the Particle Swarm Optimization (PSO) based on the probability model, the optimal traffic signal can be obtained. Finally, the effectiveness of the proposed method is shown with a micro-traffic simulator.

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Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan

    Cheng-You Cui & Hee-Hyol Lee

Authors
  1. Cheng-You Cui
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  2. Hee-Hyol Lee
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Corresponding author

Correspondence to Cheng-You Cui .

Editor information

Editors and Affiliations

  1. , School of Information and Communication, Sungkyunkwan University, 300 Chunchun-Dong Jangan-Ku, Kyunggi-Do, 440-746, Korea, Republic of (South Korea)

    Sukhan Lee

  2. Science and Technology, Daegu Gyeongbuk Institute of, 50-1, Sang-Ri, Hyeonpung-Myeon, Dalseong-Gun, Daegu, 711-873, Korea, Republic of (South Korea)

    Hyungsuck Cho

  3. Konkuk University, Neungdong-ro, Gwangjin-gu 120, Seoul, 143-701, Korea, Republic of (South Korea)

    Kwang-Joon Yoon

  4. , Department of Electronics Engineering, Pusan National University, Geum Jeong-ku, Pusan, 609-735, Korea, Republic of (South Korea)

    Jangmyung Lee

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Cui, CY., Lee, HH. (2013). Distributed Traffic Signal Control Using PSO Based on Probability Model for Traffic Jam. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_60

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  • DOI: https://doi.org/10.1007/978-3-642-33926-4_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33925-7

  • Online ISBN: 978-3-642-33926-4

  • eBook Packages: EngineeringEngineering (R0)

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