Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS
Automation, Control and Intelligent Systems
Volume 2, Issue 2, April 2014, Pages: 21-26
Received: Mar. 20, 2014;
Accepted: Apr. 10, 2014;
Published: May 20, 2014
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Kingsley Monday Udofia, Department of Elect/Elect/Computer Engineering, University of Uyo, Uyo, Nigeria
Joy Omoavowere Emagbetere, Department of Electrical/Electronic Engineering, University of Benin, Benin City, Nigeria
Frederick Obataimen Edeko, Department of Electrical/Electronic Engineering, University of Benin, Benin City, Nigeria
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This paper presents a traffic signal phase sequencing using adaptive neuro-fuzzy inference system (ANFIS) technique. The system is designed to emulate traffic expert on the selection of the appropriate phase to be given right-of-way at an isolated intersection based on the prevailing traffic situation. Inputs (queuelength and waiting time of vehicles) from traffic detectors are used to determine the selection of the next green phase. We evaluated the developed model for five different common traffic scenarios using MATLAB. The results obtained indicates that the developed model adaptively and effectively selects a phase to be given next green signal after considering the traffic situation and the nature of the intersection in question.
Adaptive, Neuro-Fuzzy Inference System, Phase Sequencing, Vehicle Traffic Control, Isolated Intersection
To cite this article
Kingsley Monday Udofia,
Joy Omoavowere Emagbetere,
Frederick Obataimen Edeko,
Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS, Automation, Control and Intelligent Systems.
Vol. 2, No. 2,
2014, pp. 21-26.
E.A. Mueller. Aspects of the history of traffic signals. IEEE Transactions on Vehicular Technology, 19(1):6 –17, February 1970.
G.E.M.D.C. Bandara et at.: Application of fuzzy logic in intel-ligent traffic control systems, National University of Singapore, CIRAS, 2003
M. R. A. Pur-nomo et al.: Development of a low cost smart traffic controller system, European Journal of Scientific Research, 2009, 32(4): 490 – 499
Niittymaki J. et al.: Fuzzy Traffic Signal Control and a New Interface Method - Maximal Fuzzy Similarity. In: Proc., The 13th Mini-EURO Conf. (Handling Uncertainty in the Analysis of Traffic and Transportation Systems) and the 9th Mtg. EURO Working Group on Transportation Intermodality, Sustainability and Intelligent Transporta-tion Systems, Bari, Italy, 2002, 716–728.
Zhang L, Li H, Prevedouros P D. Signal control for oversaturated intersections using fuzzy logic. In: Proc. of 84th Transp. Res. Bd. Ann. Mtg., Wash-ington, D.C., 2005
Nakatsuyama M, Nagahashi H, Nishizuka N. Fuzzy logic phase control-ler for traffic junctions in the one-way arterial road. In: Proc., IFAC 9th Triennial World Cong., Bu-dapest, Hungary, 1984, 2865–2870
Tan, K.K., Khalid, M., Yusof, R.: Intelligent Traffic Lights Control by Fuzzy Logic. Malaysian Journal of Computer Science 9(2), 29–35 (1996)
Pappis C. and Mamdani E.: A fuzzy logic controller for a traffic junction, IEEE Trans. Systems, Man, and Cybernetics SMC-7, 1977, 7(10): 707–717.
J.S.R. Jang: ANFIS: Adaptive-network-based Fuzzy Inference Systems. IEEE Trans, Syst, Man Cybern., 23(3), pp. 665–685, 1993.
Wenteng M.: A Real-time Performance Measurement System for Arterial Traffic Signals. A Ph.D thesis, Graduate School, University of Minnesota, 2008.