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

Year 2019, , 27 - 30, 31.03.2019
https://doi.org/10.22399/ijcesen.446666

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.

References

  • [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.
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  • [12] Şimşir, F., Özkaynak, E., Ekmekçi D., “Kavşaklarda Trafik Sinyalizasyon Sisteminin Modellemesi ve Benzetimi”, Akademik Bilişim, (2013).
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  • [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)
Year 2019, , 27 - 30, 31.03.2019
https://doi.org/10.22399/ijcesen.446666

Abstract

References

  • [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)
There are 17 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Tolga Palandız 0000-0003-0871-6129

Ramazan Şenol 0000-0002-7078-3229

Hilmi Cenk Bayrakçı 0000-0001-5064-7310

Publication Date March 31, 2019
Submission Date July 22, 2018
Acceptance Date January 5, 2019
Published in Issue Year 2019

Cite

APA 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, 5(1), 27-30. https://doi.org/10.22399/ijcesen.446666