Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
IT Convergence and Security 2017

Part of the book series: Lecture Notes in Electrical Engineering ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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. Škrjanc, I., Blažič, S., Matko, D.: Direct fuzzy model-reference adaptive control. Int. J. Intell. Syst. 17(10), 943–963 (2002)

    Article  MATH  Google Scholar 

  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. 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. 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. Pappis, C., Mamdani, E.: A fuzzy logic controller for a traffic junction. IEEE Trans. Syst. Man. Cybern. 7(10), 707–717 (1977)

    Article  MATH  Google Scholar 

  8. Karakuzu, C., Demirci, O.: Fuzzy logic based smart traffic light simulator design and hardware implementation. Appl. Soft Comput. 10, 66–73 (2010)

    Article  Google Scholar 

  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. 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)

    MATH  Google Scholar 

  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 2012

    Google Scholar 

  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 2014

    Google Scholar 

  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. 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. 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. Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier, Japan (1985)

    MATH  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  20. Pelusi, D., Mascella, R., Tallini, L.: Revised gravitational search algorithms based on evolutionary-fuzzy systems. Algorithms 10(2), 44 (2017)

    Article  MathSciNet  Google Scholar 

  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. 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. Vincze, D., Kovács, S.: Performance optimization of the fuzzy rule interpolation method FIVE. J. Adv. Comput. Intell. Intell. Inform. 15(3), 313–320 (2011)

    Article  Google Scholar 

Download references

Acknowledgement

This research is supported by EFOP-3.6.1-16-2016-00006 “The development and enhancement of the research potential at Pallasz Athéné University” project. The Project is supported by the Hungarian Government and co-financed by the European Social Fund. The research was also supported by ShiwaForce Ltd., Andrews IT Engineering Ltd., and the Foundation for the Development of Automation in Machinery Industry.

Author information

Authors and Affiliations

  1. Department of Information Technology, GAMF Faculty of Engineering and Computer Science, Pallasz Athéné University, Izsáki út 10, Kecskemét, 6000, Hungary

    Zsolt Csaba Johanyák & Rafael Pedro Alvarez Gil

Authors
  1. Zsolt Csaba Johanyák
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael Pedro Alvarez Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zsolt Csaba Johanyák .

Editor information

Editors and Affiliations

  1. iCatse, B-3001, Intellige 2, Kyonggi University, Seongnam-si, Kyonggi-do, Korea (Republic of)

    Kuinam J. Kim

  2. Computer Science, Namseoul University Computer Science, Cheonan , Ch´ungch´ong-namdo, Korea (Republic of)

    Hyuncheol Kim

  3. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Nakhoon Baek

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Johanyák, Z.C., Alvarez Gil, R.P. (2018). Fuzzy Model for the Average Delay Time on a Road Ending with a Traffic Light. In: Kim, K., Kim, H., Baek, N. (eds) IT Convergence and Security 2017. Lecture Notes in Electrical Engineering, vol 449. Springer, Singapore. https://doi.org/10.1007/978-981-10-6451-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6451-7_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6450-0

  • Online ISBN: 978-981-10-6451-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation