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

Yıl 2019, , 27 - 30, 31.03.2019

Tolga Palandız , Ramazan Şenol , Hilmi Cenk Bayrakçı

https://doi.org/10.22399/ijcesen.446666

Cited By: 2

Öz

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.

Anahtar Kelimeler

Fuzzy logic, Intersection control, Intersection control

Kaynakça

• [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)