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

  • Cheng-You Cui
  • Hee-Hyol Lee
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)


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.


traffic jam bayesian network cellular automaton particle swarm optimization probabilistic distribution 


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  1. 1.
    Peck, C., Gorton, P.T.W., Liren, D.: The Application of SCOOT in developing countries. In: Third Int. Conf. on Road Traffic Control (1990)Google Scholar
  2. 2.
    Sims, A.G., Dobinson, K.W.: The Sydney Coordinated Adaptive Traffic (SCAT) system philosophy and benefits. IEEE Trans. Veh. Technol. 29, 130–137 (1980)CrossRefGoogle Scholar
  3. 3.
    Kouvelas, A., Aboudolas, K., Papageorgiou, M., Kosmatopoulos, E.B.: A Hybrid Strategy for Real-Time Traffic Signal Control of Urban Road Networks. IEEE Trans. Intelligent Transportation System 12(3), 884–894 (2011)CrossRefGoogle Scholar
  4. 4.
    Lee, J., Abdulhai, B., Shalaby, A., Chung, E.-H.: RealTime Optimization for Adaptive Traffic Signal Control Using Genetic Algorithms. Journal of Intelligent Transportation System 9(3), 111–122 (2005)MATHGoogle Scholar
  5. 5.
    Dong, C., Huang, S., Liu, X.: Urban Werea Traffic Signal Timing Optimization based on Sa-PSO. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence (2010)Google Scholar
  6. 6.
    Murat, Y.S., Ergun, G.: A fuzzy logic multi-phased signal control model for isolated junctions. Transportation Research Part C 13(1), 19–36 (2005)CrossRefGoogle Scholar
  7. 7.
    Gokulan, B.P., Srinivasan, D.: A fuzzy logic multi-phased signal control model for isolated junctions. IEEE Trans. intelligent Transportation System 11(3), 714–727 (2010)CrossRefGoogle Scholar
  8. 8.
    Chao, K.-H., Lee, R.-H., Wang, M.-H.: An intelligent traffic light control based on extension neural network. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part I. LNCS (LNAI), vol. 5177, pp. 17–24. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Dipti, S., Choy, M.C., Cheu, R.L.: Neural Network for Real-Time Traffic Signal Control. IEEE Trans. Intelligent Transportation System 7(3), 261–272 (2006)CrossRefGoogle Scholar
  10. 10.
    Dai, Y., Hu, J., Zhao, D., Zhu, F.: Neural Network Based Online Traffic Signal Controller Design with Reinforcement Training. In: 2011 14th IEEE Confer. on Intelligent Transportation System (2011)Google Scholar
  11. 11.
    Balaji, P.G., German, X., Srinivasan, D.: Urban traffic signal control using reinforcement learning agents. Intelligent Transport Systems, IET 4(3) (2010)Google Scholar
  12. 12.
    Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I France 2(12), 2221–2229 (1992)CrossRefGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushuJapan

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