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Adaptive Fuzzy Logic Traffic Signal Control Based on Cuckoo Search Algorithm

  • Suhua Wu
  • Yunrui BiEmail author
  • Gang Wang
  • Yan Ma
  • Mengdan Lu
  • Kui Xu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 127)

Abstract

Traffic congestion becomes a big problem to perplex the current society. Effective traffic signal control can alleviate traffic congestion, especially for real-time traffic signal control. To improve the control efficiency, fuzzy logic control based on cuckoo search algorithm is applied to solve the problem of real-time traffic signal control. Research object is multi-lane four-phase single intersection which is also the commonly intersection in reality. Vehicular evaluation index model is established firstly. Then, the appropriate green time is given by the cuckoo search algorithm and fuzzy logic control according to the number of real-time road vehicles. Through simulation experiments, the proposed method based on the fuzzy logic control optimized by cuckoo search algorithm can be verified to obtain a good effect. This method also suits for other complex nonlinear systems.

Keywords

Fuzzy logic control Cuckoo algorithm Real-time traffic signal control 

Notes

Acknowledgments

The project is supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 18KJB510016), School-level Project of Nanjing Institute of Technology (No. YKJ201718, No. JCYJ201819), Practical Innovation Training Program of Jiangsu Province for College Students (No. 201811276051X) and a Project Funded by the Priority Academic Development of Jiangsu Higher Education Institutions.

References

  1. 1.
    Srinivasan, D., Choy, M.C., Cheu, R.L.: Neural networks for real-time traffic signal control. IEEE Trans. Intell. Transp. Syst. 7(3), 261–272 (2006)CrossRefGoogle Scholar
  2. 2.
    Wang, F.Y.: Parallel control and management for intelligent transportation systems: concepts, architectures and applications. IEEE Trans. Intell. Transp. Syst. 11(3), 630–638 (2010)CrossRefGoogle Scholar
  3. 3.
    Balaji, P.G., Srinivasan, D.: Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Trans. Intell. Transp. Syst. 11(3), 714–727 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhu, F., Li, Z., Chen, S., Xiong, G.: Parallel transportation management and control system and its applications in building smart cities. IEEE Trans. Intell. Transp. Syst. 17(6), 1576–1585 (2016)CrossRefGoogle Scholar
  5. 5.
    Talab, H.S., Mohammadkhani, H.: Design optimization traffic light timing using the fuzzy logic at a Diphasic’s Isolated intersection. J. Intell. Fuzzy Syst. 27(4), 1609–1620 (2014)Google Scholar
  6. 6.
    Ding, N., He, Q., Wu, C., Fetzer, J.: Modeling traffic control agency decision behavior for multimodal manual signal control under event occurrences. IEEE Trans. Intell. Transp. Syst. 16(5), 2467–2478 (2015)CrossRefGoogle Scholar
  7. 7.
    Benhamza, K., Seridi, H.: Adaptive traffic signal control in multiple intersections network. J. Intell. Fuzzy Syst. 28(6), 2557–2567 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhao, D., Dai, Y., Zhang, Z.: Computational intelligence in urban traffic signal control: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 485–494 (2012)CrossRefGoogle Scholar
  9. 9.
    Zhao, Y., Gao, H., Wang, S., Wang, F.: A novel approach for traffic signal control: a recommendation perspective. IEEE Intell. Transp. Syst. Mag. 9(3), 127–135 (2017)CrossRefGoogle Scholar
  10. 10.
    Pappis, C.P., Mamdani, E.H.: A fuzzy logic controller for a traffic junction. IEEE Trans. Syst. Man Cybern. 7(10), 707–717 (1977)CrossRefGoogle Scholar
  11. 11.
    Murat, Y.S., Gedizlioglu, E.: A fuzzy logic multi-phased signal control model for isolated junctions. Transp. Res. Part C Emerg. Technol. 13(1), 19–36 (2005)CrossRefGoogle Scholar
  12. 12.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing Coimbatore, pp. 210–214. IEEE (2009)Google Scholar
  13. 13.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. 11(8), 5508–5518 (2011)CrossRefGoogle Scholar
  14. 14.
    Liu, X.Y., Fu, M.L.: Cuckoo search algorithm based on frog leaping local search and chaos theory. Appl. Math. Comput. 266, 1083–1092 (2015)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Zhu, X., Wang, N.: Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks. Appl. Soft Comput. 56, 458–471 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Suhua Wu
    • 1
  • Yunrui Bi
    • 1
    • 2
    Email author
  • Gang Wang
    • 3
  • Yan Ma
    • 1
  • Mengdan Lu
    • 1
  • Kui Xu
    • 1
  1. 1.School of AutomationNanjing Institute of TechnologyNanjingChina
  2. 2.Key Laboratory of Measurement and Control of CSE, Ministry of EducationSoutheast UniversityNanjingChina
  3. 3.School of BusinessQingdao UniversityQingdaoChina

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