TY - JOUR T1 - Optimization Of Traffic Signalization For Complex Roundabout By Fuzzy Logic According To Various Parameters AU - Palandız, Tolga AU - Şenol, Ramazan AU - Bayrakçı, Hilmi Cenk PY - 2019 DA - March Y2 - 2019 DO - 10.22399/ijcesen.446666 JF - International Journal of Computational and Experimental Science and Engineering JO - IJCESEN PB - İskender AKKURT WT - DergiPark SN - 2149-9144 SP - 27 EP - 30 VL - 5 IS - 1 LA - en AB - In this study, trafficsignalization by fuzzy logic according to number of vehicles, vehicle type,fuel and time parameters of 5 leg roundabouts were simulated in computerenvironment. For this purpose, city surveillance cameras (mobese) were used forthe selected intersection. In the simulation, the Mamdani type fuzzy model wasused. The results of the application are listed in tables. According to thevehicle density coming near the intersection, the transition times or stoppingtimes applied to the intersection were optimized. Also, types of vehiclescoming near the intersection are analyzed and vehicles with high fuelconsumption are given priority. Thus, fuel consumption and environmentalpollution can be reduced. The methods in this study are compared in terms offuel saving, environmental effects and faster road flow. Accordingly, neutralfuel consumption with fuzzy logic method is averagely %15.9 less than classicalmethod. KW - Fuzzy logic KW - Intersection control KW - Intersection control CR - [1] L.A. Zadeh, Inform. Control 8, 338 (1965). CR - [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). CR - [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). CR - [4] Kim, Jongwan, A Fuzzy Logic Control Simulator for Adaptive Traffic Management, Proc IEEE International Conference on Fuzzy Systems, 1519-1524, (1997). CR - [5] Desai, D., Somani, S., 2014. Instinctive traffic control and vehicle detection techniques. International Journal of Scientific & Engineering Research 5 (1), 2192-2195. CR - [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. CR - [7] Kuhne, R.D., 1991. Freeway control using a dynamic traffic flow model and vehicle reidentification techniques. Transportation Research Record 1320, 251-259. CR - [8] N.G. Adar∗ , A. Egrisogut Tiryaki and R. Kozan, , Acta Phys. Pol. A 128, B-348(2015) CR - [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. CR - [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. CR - [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. CR - [12] Şimşir, F., Özkaynak, E., Ekmekçi D., “Kavşaklarda Trafik Sinyalizasyon Sisteminin Modellemesi ve Benzetimi”, Akademik Bilişim, (2013). CR - [13] https://www.anl.gov/sites/anl.gov/files/idling_worksheet.pdf(Access Date: 21.08.2017) CR - [14] www.cleancities.energy.gov (Access Date: 21.08.2017) CR - [15] https://www.afdc.energy.gov(Access Date: 21.08.2017) CR - [16] O.M. Pişirir and O. Bingöl, Acta Phys. Pol. A 130, 36 (2016) CR - [17] B. Kiriş , O. Bingöl , R. Şenol and A. Altintaş, Acta Phys. Pol. A 130, 55 (2016) UR - https://doi.org/10.22399/ijcesen.446666 L1 - https://dergipark.org.tr/tr/download/article-file/640445 ER -