Fuzzy Model for the Average Delay Time on a Road Ending with a Traffic Light

  • Zsolt Csaba Johanyák
  • Rafael Pedro Alvarez Gil
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 449)

Abstract

Urban traffic is continuously increasing and therefore especially in peak-hours an optimized traffic light system can provide significant advantages. As a step towards developing such a system this paper presents a fuzzy model that estimates the average delay times on a road that ends at an intersection with traffic lights. The model was created based on data obtained using a validated microscopic traffic simulator that is based on the Intelligent Driver Model. Simulations were carried out for different traffic flow, traffic signal cycles, and green period values. The newly developed fuzzy model can be used as a module in a traffic light optimization system.

Keywords

Fuzzy model Average delay time Traffic light Microscopic traffic simulator 

References

  1. 1.
    Devasenapati, S.B., Ramachandran, K.I.: Hybrid fuzzy model based expert sytem for misfire detection in automobile engines. Int. J. Artif. Intell. 7(A11), 47–62 (2011)Google Scholar
  2. 2.
    Portik, T., Pokorádi, L.: Fuzzy rule based risk assessment with summarized defuzzyfication. In: Proceedings of the XIIIth Conference on Mathematics and its Applications, Timisoara, pp. 277–282 (2013)Google Scholar
  3. 3.
    Škrjanc, I., Blažič, S., Matko, D.: Direct fuzzy model-reference adaptive control. Int. J. Intell. Syst. 17(10), 943–963 (2002)CrossRefMATHGoogle Scholar
  4. 4.
    Vaščák, J.: Approaches in adaptation of fuzzy cognitive maps for navigation purposes. In: Proceedings of the 8th International Symposium on Applied Machine Intelligence and Informatics—SAMI, Herľany, pp. 31–36 (2010)Google Scholar
  5. 5.
    Yorita, A., Botzheim, J., Kubota, N.: Self-efficacy using fuzzy control for long-term communication in robot-assisted language learning. Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Tokyo, pp. 5708–5715 (2013)Google Scholar
  6. 6.
    Pelusi, D., Mascella, R., Tallini, L., Vazquez, L.: Control of Drum Boiler dynamics via an optimized fuzzy controller. Int. J. Simul. Syst. Sci. Technol. 17(33), 1–7 (2016)Google Scholar
  7. 7.
    Pappis, C., Mamdani, E.: A fuzzy logic controller for a traffic junction. IEEE Trans. Syst. Man. Cybern. 7(10), 707–717 (1977)CrossRefMATHGoogle Scholar
  8. 8.
    Karakuzu, C., Demirci, O.: Fuzzy logic based smart traffic light simulator design and hardware implementation. Appl. Soft Comput. 10, 66–73 (2010)CrossRefGoogle Scholar
  9. 9.
    Postorinoa, M.N., Versacia, M.: Upgrading urban traffic flow by a demand-responsive fuzzy-based traffic lights model. Int. J. Model. Simul. 34(2), 102–109 (2014)Google Scholar
  10. 10.
    Baydokht, R.N., Noori, S., Azhangzad, A.: Presenting a fuzzy model to control and schedule traffic lights. J. Intell. Fuzzy Syst. 26(2), 1007–1016 (2014)MATHGoogle Scholar
  11. 11.
    Murat, Y.Z., Cakici, Z., Yaslan, G.: Use of fuzzy logic traffic signal control approach as dual lane ramp metering model for freeways. In: Online Conference on Soft Computing in Industrial Applications Anywhere on Earth, pp. 10–21, December 2012Google Scholar
  12. 12.
    Castán-Rocha, J.A., Ibarra-Martínez, S., Laria-Menchaca, J., Castan, E.R.: An implementation of case-based reasoning to control traffic light signals. In: Proceedings of the World Congress on Engineering, vol. 1. WCE, London, UK, 2–4 July 2014Google Scholar
  13. 13.
    Teo, K.T.K., Kow, W.Y., Chin, Y.K.: Optimization of traffic flow within an urban traffic light intersection with genetic algorithm. In: Proceedings of 2010 Second International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM 2010), pp. 172–177 (2010). doi:10.1109/CIMSiM.2010.95
  14. 14.
    Khanjary, M., Navidi, H.: Optimizing traffic light of an intersection by using game theory. In: Proceedings of 3rd World Conference on Information Technology (WCIT-2012), AWERProcedia Information Technology & Computer Science, vol. 3, pp. 1163–1168 (2012)Google Scholar
  15. 15.
    Kovács, T., AvarezGil, R.P., Bolla, K., Csizmás, E., Fábián, C., Kovács, L., Medgyes, K., Osztényi, J., Végh, A.: Parameters of the intelligent driver model in signalized intersections. Tech. Gaz. 23(5), 1469–1474 (2016)Google Scholar
  16. 16.
    Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier, Japan (1985)MATHGoogle Scholar
  17. 17.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks IV. Perth, pp. 1942–1948 (1995)Google Scholar
  18. 18.
    David, R.-C., Precup, R.-E., Petriu, E.M., Rădac, M.-B., Preitl, S.: Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivit. Inf. Sci. 247, 154–173 (2013)CrossRefMATHGoogle Scholar
  19. 19.
    Precup, R.-E., David, R.-C., Petriu, E.M., Preitl, S., Rădac, M.-B.: Novel adaptive gravitational search algorithm for fuzzy controlled servo systems. IEEE Trans. Ind. Inform. 8(4), 791–800 (2012)CrossRefGoogle Scholar
  20. 20.
    Pelusi, D., Mascella, R., Tallini, L.: Revised gravitational search algorithms based on evolutionary-fuzzy systems. Algorithms 10(2), 44 (2017)CrossRefGoogle Scholar
  21. 21.
    Johanyák, Z.C.: Performance improvement of the fuzzy rule interpolation method LESFRI. In: Proceedings of the 12th IEEE International Symposium on Computational Intelligence and Informatics, Budapest, pp. 271–276 (2011)Google Scholar
  22. 22.
    Kovács, L., Ratsaby, J.: Analysis of linear interpolation of fuzzy sets with entropy-based distances. Acta. Polytech. Hung. 10(3), 51–64 (2013)Google Scholar
  23. 23.
    Vincze, D., Kovács, S.: Performance optimization of the fuzzy rule interpolation method FIVE. J. Adv. Comput. Intell. Intell. Inform. 15(3), 313–320 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information Technology, GAMF Faculty of Engineering and Computer SciencePallasz Athéné UniversityKecskemétHungary

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