SavedAdd your journal

Journals / International Journal of Computational and Experimental Science and Engineering / 2026 / Cilt: 5 Sayı: 1

Optimization Of Traffic Signalization For Complex Roundabout By Fuzzy Logic According To Various Parameters

Tolga Palandız(Süleyman Demirel Üniversitesi)0000-0003-0871-6129
Ramazan Şenol(Süleyman Demirel Üniversitesi)0000-0002-7078-3229
Hilmi Cenk Bayrakçı(Süleyman Demirel Üniversitesi)0000-0001-5064-7310
Pages
27–30
Publication Date
2019-03-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Accordingly, neutral fuel consumption with fuzzy logic method is averagely %15.9 less than classical method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, traffic signalization by fuzzy logic according to number of vehicles, vehicle type, fuel and time parameters of 5 leg roundabouts were simulated in computer environment.
📊
Key Findings & Data:For this purpose, city surveillance cameras (mobese) were used for the selected intersection.
💡
Conclusion & Impact:Accordingly, neutral fuel consumption with fuzzy logic method is averagely %15.9 less than classical method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Fuzzy logic · Intersection control · Intersection control

Abstract

In this study, traffic signalization by fuzzy logic according to number of vehicles, vehicle type, fuel and time parameters of 5 leg roundabouts were simulated in computer environment. For this purpose, city surveillance cameras (mobese) were used for the selected intersection. In the simulation, the Mamdani type fuzzy model was used. The results of the application are listed in tables. According to the vehicle density coming near the intersection, the transition times or stopping times applied to the intersection were optimized. Also, types of vehicles coming near the intersection are analyzed and vehicles with high fuel consumption are given priority. Thus, fuel consumption and environmental pollution can be reduced. The methods in this study are compared in terms of fuel saving, environmental effects and faster road flow. Accordingly, neutral fuel consumption with fuzzy logic method is averagely %15.9 less than classical method.

🔖

Cite This Article

Palandız, T., Şenol, R., Bayrakçı, H. C. (2019). Optimization Of Traffic Signalization For Complex Roundabout By Fuzzy Logic According To Various Parameters. International Journal of Computational and Experimental Science and Engineering, Cilt: 5 Sayı: 1, 27–30. https://doi.org/10.22399/ijcesen.446666

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (19)

  1. L.A. Zadeh, Inform. Control 8, 338 (1965).
  2. Pappis, C.P.,Mamdani, E.H., A Fuzzy Logic Controller for a Traffic Junction, IEEE Transactions on systems, Man and Cybernetics, 707-717,(1977).
  3. Tzes A., McShane and Kim, S., Expert Fuzzy Logic Traffic Signal Control for Transportation Networks, Institute of Transportation Engineers 65th Annual Meeting, Denver USA, 154-158, (1995).
  4. Kim, Jongwan, A Fuzzy Logic Control Simulator for Adaptive Traffic Management, Proc IEEE International Conference on Fuzzy Systems, 1519-1524, (1997).
  5. Desai, D., Somani, S., 2014. Instinctive traffic control and vehicle detection techniques. International Journal of Scientific & Engineering Research 5 (1), 2192-2195.
  6. Hegyi, A., Bellemans, T., De-Schutter, B., 2009. Freeway traffic management and control, In: Meyers, R.A. (Ed.), Encyclopaedia of Complexity and Systems Science. Springer, New York, pp. 3943-3964.
  7. Kuhne, R.D., 1991. Freeway control using a dynamic traffic flow model and vehicle reidentification techniques. Transportation Research Record 1320, 251-259.
  8. N.G. Adar∗, A. Egrisogut Tiryaki and R. Kozan,, Acta Phys. Pol. A 128, B-348(2015)
  9. F. Meng, X. Chen, 2015. Correlation Coefficients of Hesitant Fuzzy Sets and Their Application Based on Fuzzy Measures, Cognitive Computation, August 2015, Volume 7, Issue 4, pp 445–463.
  10. F. Chen, L. Wang, B. Jiang, C. Wen, 2015. An Arterial Traffic Signal Control System Based on a Novel Intersections Model and Improved Hill Climbing Algorithm, Cognitive Computation, August 2015, Volume 7, Issue 4, pp 464–476.
  11. M. Czubenko, Z. Kowalczuk, A. Ordys, 2015. Autonomous Driver Based on an Intelligent System of Decision-Making, Cognitive Computation, October 2015, Volume 7, Issue 5, pp 569–581.
  12. Şimşir, F., Özkaynak, E., Ekmekçi D., “Kavşaklarda Trafik Sinyalizasyon Sisteminin Modellemesi ve Benzetimi”, Akademik Bilişim, (2013).
  13. https://www.anl.gov/sites/anl.gov/files/idling_worksheet.pdf(Access Date: 21.08.2017)
  14. www.cleancities.energy.gov (Access Date: 21.08.2017)
  15. https://www.afdc.energy.gov(Access Date: 21.08.2017)
  16. O.M. Pişirir and O. Bingöl, Acta Phys. Pol. A 130, 36 (2016)
  17. B. Kiriş, O. Bingöl, R. Şenol and A. Altintaş, Acta Phys. Pol. A 130, 55 (2016)
  18. N.G. Adar∗, A. Egrisogut Tiryaki and R. Kozan,, Acta Phys. Pol. A 128, B-348(2015)
  19. B. Kiriş, O. Bingöl, R. Şenol and A. Altintaş, Acta Phys. Pol. A 130, 55 (2016)