Fuzzy multiobjective traffic light signal optimization. (English) Zbl 1266.90063

Summary: Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the control of fluctuating traffic volumes such as oversaturated or unusual load conditions. The problem is solved by genetic algorithm, and a new defuzzification method is introduced. The performance of the new defuzzification method (NDM) is compared with the centroid point defuzzification method (CPDM) by using ANOVA. Finally, an illustrative example is presented to show the competency of proposed algorithm.


90B20 Traffic problems in operations research
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
93C42 Fuzzy control/observation systems
Full Text: DOI


[1] J. M. Bayley, “Pedestrian and traffic control,” Traffic Engineering and Control, vol. 8, pp. 311-312, 1966.
[2] R. Noland, “Pedestrian travel times and motor vehicle traffic signals,” in Proceedings of the Annual Meeting of the Transportation Research Board, Washington, DC, USA, 1996, paper no. 1553.
[3] R. Noland, “Optimal travel time trade-offs at signalized pedestrian crossings,” Unpublished Work, 2003.
[4] E. Brockfeld, R. Barlovic, A. Schadschneider, and M. Schreckenberg, “Optimizing traffic lights in a cellular automaton model for city traffic,” Physical Review E, vol. 64, Article ID 056132, 2001. · Zbl 0978.90017
[5] V. Gradinescu, C. Gorgorin, R. Diaconescu, and V. Cristea, Adaptive traffic light using car-to-car communication [Dissertation], Computer Science Department, University of Bucharest, 2007.
[6] K. T. K. Teo, W. Y. Kow, and Y. K. Chin, Optimization of Traffic Flow within an Urban Traffic Light Intersection with Genetic Algorithm, School of Engineering and Information Technology, University Malaysia Sabah, Kota Kinabalu, Malaysia, 2010.
[7] F. V. Webster, Traffic Signal Settings, Road Research Technical Paper no. 39, Department of Scientific and Industrial Research, 1958.
[8] Z. Yang, Thesis signal timing optimization based on minimizing vehicle and pedestrian delay by genetic algorithm [Dissertation], Graduate College of the University of Illinois at Urbana-Champaign, 2010.
[9] C. Dong, S. Huang, and X. Liu, “Urban area traffic signal timing optimization based on Sa-PSO,” in Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, vol. 3, pp. 80-84, 2010.
[10] K. K. Tan, M. Khalid, and R. Yusof, “Intelligent traffic lights control by fuzzy logic,” Malaysian Jurnal of Computer Science, vol. 3, pp. 45-58, 1996.
[11] J. Favilla, A. Machion, and F. Gomide, “Fuzzy traffic control: adaptive strategies,” in Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, pp. 506-511, San Francisco, Calif, USA, March 1993.
[12] M. R. Virkler, Pedestrian Compliance Effects on Signal Delay, Research Record no. 1636, TRB, National Research Council, Washington, DC, USA, 1998.
[13] V. Chilukuri and M. R. Virkler, “Validation of HCM pedestrian delay model for interrupted facilities,” Journal of Transportation Engineering, vol. 131, no. 12, pp. 939-945, 2005.
[14] P. Bhattacharya and M. R. Virkler, Optimization for Pedestrian and Vehicular Delay in a Signal Network, Transportation Research Record no. 1939, TRB, National Research Council, Washington, DC, USA, 2005.
[15] D. P. Filev and R. R. Yager, “A generalized defuzzification method via bad distribution,” International Journal of Intelligent Systems, vol. 6, pp. 687-697, 1991. · Zbl 0752.93040
[16] R. R. Yager and D. P. Filev, “SLIDE: a simple adaptive defuzzification method,” IEEE Transactions on Fuzzy Systems, vol. 1, no. 1, pp. 69-78, 1993.
[17] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338-353, 1965. · Zbl 0139.24606
[18] H. Hellendoorn and C. Thomas, “Defuzzification if fuzzy controllers,” Journal of Intelligent Fuzzy Systems, vol. 1, pp. 109-123, 1993.
[19] N. Shahsavari-Pour, M. Modarres, R. Tavakoli-Moghadam, and E. Najafi, “Optimizing a multi-objectives time-cost-quality trade-off problem by a new hybrid genetic algorithm,” World Applied Science Journal, vol. 10, no. 3, pp. 355-363, 2010.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.