Türkay DERELİ, Cihan ÇETİNKAYA, Nazmiye ÇELİK

DESIGNING A FUZZY LOGIC CONTROLLER FOR A SINGLE INTERSECTION: A CASE STUDY IN GAZIANTEP

The traffic problem is a multidimensional problem and traffic lights are the key points to solving this problem. Thus, optimum green light duration helps reduce the traffic congestion. “Queue length during red light” and “remaining vehicles in line after green light” are important parameters for the determination of the green light duration. In this paper, a single intersection is taken into account with mentioned two inputs of fuzzy logic controller considering each intersection approach one by one. The output variables are chosen as “phase selection” and “extend of green light duration”. Unlike many other studies, these output variables are combined into one system based on modal distinction. Each approach is normalized according to the number of lanes to calculate a membership function value: because of different capacities of intersection approaches. The model is solved using MATLAB fuzzy inference system. The delay parameter is considered as performance measurement. Accordingly, proposed model is compared with various traditional models in literature. Finally, the model is evaluated using ANN (artificial neural network modeling) and ANFIS (adaptive neuro-fuzzy inference system) and then the consistency of the results is checked. Results show that the fuzzy controller can effectively minimize total delay and it is superior to compared methods.

Keywords:

Adaptive neuro-fuzzy, artificial neural network, fuzzy logic controller Gaziantep, single intersection, traffic light,

PDF

___

[1] Chiu S., Chand S., (1993a) Adaptive Traffic Signal Control Using Fuzzy Logic. Fuzzy Systems, Second IEEE International Conference, 1371-1376, San Francisco, CA.
[2] Chiu S., Chand S.,(1993b) Self-Organizing Traffic Control via Fuzzy Logic. 32nd IEEE Conference on Decision and Control, 1371–1376, San Antonio, TX.
[3] Niittymaki J., Kosonen T.; Nevala R., (2001) Fuzzy Traffic Signal Control in Major Arterials, IEEE Intelligent Transportation Systems Conference, 346–351, Oakland (CA), USA.
[4] Chou CH., Teng JC., (2002) Fuzzy Logic Controller for Traffic Junction Signals. Information Sciences. 143,73–97.
[5] Li Y., Fan X.,(2003) Design of Signal Controllers for Urban Intersections Based on Fuzzy Logic and Weightings, IEEE Intelligent Transportation Systems. 1, 867–871.
[6] Pappis, CP., Mamdani EH., (1977) A Fuzzy Logic Controller for a Traffic Junction. IEEE Transactions on Systems, Man, and Cybernetics. 7, 707–717.
[7] Baez GB., Morales ER., Marrero EL.,(1997) A Fuzzy Logic Based Phase Controller for Traffic Control, Sixth IEEE International Conference on Fuzzy Systems, 1533–1539, Barcelona,
[8] Trabia MB., Kaseko MS., Ande M., (1999) A Two-Stage Fuzzy Logic Controller for Traffic Signals, Transportation Research Part C, 7, 353–367.
[9] Niittymaki J., Pursula M., (2000) Signal Control Using Fuzzy Logic, Fuzzy Sets and Systems, 116, 11–22.
[10] Niittymaki J.,(2001) General Fuzzy Rule Base for Isolated Traffic Signal Control-Rule Formulation, Transportation Planning and Technol, 24,227–247.
[11] Murat YS., Gedizlioglu E., (2005) A Fuzzy Logic Multi-Phased Signal Control Model for Isolated Junctions, Transportation Research Part C, 13,19–36.
[12] Yang Z., Xiyue H., Du C., Tang M., Yang F., (2008) Hierarchical Fuzzy Logic Traffic Control for Urban Signalized Intersections, 7th World Congress on Intelligent Control and Automation, 5203–5207, Chongqing, China.
[13] Junqiang L., Yuqin F.,(2009) Research on the Fuzzy Control and Simulation for Intersection Based on the Phase Sequence Optimization, International Conference on Measuring Technology and Mechatronics Automation, 578–581.
[14] Gong YJ., Zhang J., Liu O., Liu Y., (2011) A Novel Fuzzy Model for the Traffic Signal Control of Modern Roundabouts, Systems, Man and Cybernetics (SMC), IEEE International Conference on Anchorage, 1777–1782, AK.
[15] Zhu M., (2012) Fuzzy Logic Signal Controller Based on Competition for Isolated Intersection, 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, 116–118, Nanchang, Jiangxi.
[16] Zaied ANH., Othman WA., (2011) Development of a Fuzzy Logic Traffic System for Isolated Signalized Intersections in the State of Kuwait, Expert Systems with Applications. 38,9434–9441.
[17] Teo KTK., Yeo KB., Tan SE., Siew ZW., Lim KG., (2013) Design and Development of Portable Fuzzy Logic Based Traffic Optimizer, IEEE ICCE-China Workshop, ,7–12, Shenzhen, China.
[18] Alam J., Pandey MK., (2014) Development of Intelligent Traffic Light System Based on Congestion Estimation Using Fuzzy Logic, IOSR Journal of Computer Engineering (IOSR-JCE), 16, 36–44.
[19] Homaei H., Hejazi SR., Dehghan AAM., (2015) A New Traffic Light Controller Using Fuzzy Logic for a Full Single Junction Involving Emergency Vehicle Preemption, Journal of Uncertain Systems, 9, 49–61.
[20] Nadia Postorino M., Versaci M., (2015) Upgrading Urban Traffic Flow by a Demand- Responsive Fuzzy-Based Traffic Lights Model, International Journal of Modelling and Simulation, 2, 102-109.
[21] Rao DRJB., Zakaria A., (2016) Improved Traffic Light Controller Based on Fuzzy Inference System, Proceedings of International Conference on Advanced Processes and Systems in Manufacturing, 55-56, Putrajaya, Malaysia.
[22] Shirvani MJ., Maleki, HR., (2017) Maximum Green Times Settings for Traffic Actuated Signal Control at Isolated Intersections Using Fuzzy Logic, Int.J. Fuzzy Syst., 19, 247-256.
[23] Nakatsuji T., Kaku T., (2016) Development of a Self-Organizing Traffic Control System Using Neural Network Models, Transportation Research Record, 1324, 137-145.
[24] Araghi S., Khosravi A., Johnstone M., Creighton D., (2013) Intelligent Traffic Light Control of Isolated Intersections Using Machine Learning Methods, IEEE International Conference on Systems, Man, and Cybernetics, 3621-3626.
[25] Araghi S., Khosravi A., Creighton D., (2014) ANFIS Traffic Signal Controller for an Isolated Intersection, Proceedings of the International Conference on Fuzzy Computation Theory and Applications, 175-180, Portugal.
[26] Lai GR., Che Soh A, Sarkan HMD., Abdul Rahman RZ., Hassan MK., (2015) Controlling Traffic Flow in Multilane-Isolated Intersection Using ANFIS Approach Techniques, Journal of Engineering Science and Technology, 10, 1009-1034.
[27] Udofia KM., Emagbetere JO., Edekoi FO.,(2014) Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS, Automation, Control and Intelligent Systems, 2, 21-26.
[28] Seesara S., Gadit J., (2015) Smart Traffic Control Using Adaptive Neuro-Fuzzy Inference System(ANFIS), International Journal of Advance Engineering and Research Development, 295-302.
[29] Zadeh LA., (1965) Fuzzy sets. Information and Control, 8, 338–353
[30] Turkish Standards Institute., (1989) Automobile Equivalence in Urban Areas (Turkish Standards TS6407), Ankara (in Turkish).
[31] Mamdani, E., H., (1974) Application of fuzzy algorithms for control of simple dynamic plant, Proceedings of the Institution of Electrical Engineers Control & Science, 121, 1585–1588.
[32] Arıkan Öztürk E., (2004) Modeling of cycle time for signalized intersections in urban areas: Ankara case study. Ph.D Thesis. Gazi University, Institute of Science and Technology.
[33] Webster FV.,(1958) Traffic Signal Settings. Road Research Technical Paper, No 39, London, UK: Road Research Laboratory, Her Majesty Stationary Office.
[34] Mulbay Z., Cay E., Binici M., Macit O., Doruk H., (2001) Trafik Sinyalizasyon Sürelerinin Değiştirilmesi ile Trafik Akışının Iyileştirilmesi: Isparta örneği. 18. Endüstri Mühendisleri Öğrenci Sempozyumu.
[35] Rafiq MY., Bugmann G., Easterbrook DJ., (2001) Neural Network Design for Engineering Applications, Computers and Structures, 1541-1552.
[36] Araghi S.,Khosravi A., Creighton D., (2015) Design of an Optimal ANFIS Traffic Signal Controller by using Cuckoo Search for an Isolated Intersection, IEEE International Conference on Systems, Man, and Cybernetics.
[37] Mashrei MA., (2012) Neural Network and Adaptive Neuro-Fuzzy Inference System Applied to Civil Engineering Problems, Fuzzy Inference System - Theory and Applications.
[38] Jang S., (1993) Adaptive Network-Based Fuzzy Inference System. IEEE Journal, 23, 665-685.

Sigma Mühendislik ve Fen Bilimleri Dergisi-Cover

ISSN: 1304-7191
Yayın Aralığı: Yılda 4 Sayı
Yayıncı: Yıldız Teknik Üniversitesi

Arşiv