An Integrated Model to Control Traffic Lights: Controlling of Traffic Lights in Multiple Intersections using Fuzzy Logic and Genetic Algorithm

Authors

  • Khaled Abdul Rahman Jomaa Faculty of Industrial Management, University Malaysia Pahang, 26300, Pahang
  • Cheng jack Kie

Keywords:

Traffic Light, Fuzzy Logic, Genetic Algorithm, Congestion, Malaysia

Abstract

In this paper we propose an integrated model combines Fuzzy Logic (FL) and Genetic Algorithm (GA), utilizing their applications in order to minimize the traffic congestion and traffic delay, through controlling traffic light system in three proposed traffic intersections. The proposed model in this paper will adjust the timing and phasing of the green traffic lights according to the current situation in the proposed traffic intersections; each intersection is supposed to be controlled by traffic signals that will apply the model. The green light interval time length shall provide at an intersection will be decided by FL. the outputs of FL will be optimized by GA, in order to obtain a higher performance. This performance can be measured considering the reduction in the waiting time and the total amount of vehicles that arrived to the Queue of the three intersections. The proposed model expected to provide a significant improvement to the traffic light system performance which might be very important to be applied in the metropolitan areas in Malaysia.

aper will adjust the timing and phasing of the green traffic lights according to the current situation in the proposed traffic intersections;each intersection is supposed to be controlled by traffic signals that will apply the model. The green light interval time length shall provide at an intersection will be decided by FL. the outputs of FL will be optimized by GA, inorder to obtain a higher performance. This performance can be measured considering the reduction in the waiting time and the total amount of vehicles that arrived to the Queue of the three intersections. The proposed model expected to provide a significant improvement to the traffic light system performance which might be very important to be applied in the metropolitan areas in Malaysia.

References

[ ]-The World Bank, Malaysia Economic Monitor, Transforming Urban Transport, June 2015

[ ]- Marzuki Khalid, at el., Control of a Complex Traffic Junction using Fuzzy Inference, University Technology Malaysia (2002)

[ ] - Jee-Hyong Lee, Hyung Lee-Kwang, “Distributed and Cooperative Fuzzy Controllers for Traffic Intersections Groupâ€, IEEE Transactions on Systems, Man, and Cybernetics – Part C : Applications and Reviews, Vol. 29, No. 2, May, 1999

[ ]-Khaled Abdul Rahman Jomaa, "Google Forms ." Google. 6 17, 2016. https://docs.google.com/forms/d/16VkbGXnU_eAXZ37ZbABYnN37hLs-q5tWR68bf0-Qfbw/viewform?edit_requested=true (accessed 11 15, 2016)

[ ]- Javed Alam, “Design and Analysis of a Two Stage Traffic Light System Using Fuzzy Logicâ€, Journal of Information Technology & Software Engineering, Volume 5, Issue 3, 1000162, 2015.

[ ]- Ahmed A. Ezzat, et al. “Development of a Stochastic Genetic Algorithm for Traffic Signal Timings Optimizationâ€, Industrial and Systems Engineering Research Conference, 2014.

[ ]-Mojtaba Salehi et. al. “TLCSBFL: A Traffic Lights Control System Based on Fuzzy Logicâ€, International Journal of u- and e- Service, Science and Technology Vol.7, No.3, pp.27-34,2014.

[ ]- L. Singh, S. Tripathi, H. Arora, Time Optimization for Traffic Signal Control Using Genetic Algorithm, International Journal of Recent Trends in Engineering, Volume 2,Number 2 ,November 2009.

[ ] A. M.Turky, M. Sh. Ahmad and M. Z. Mohd Yusoff,. "The Use of Genetic Algorithm for Traffic Light and Pedestrian Crossing Control." IJCSNS International Journal of Computer Science and Network 88 Security, VOL.9 No.2, 2009: 88-96.

[ ] Ahmed A. Ezzat, et al. “Development of a Stochastic Genetic Algorithm for Traffic Signal Timings Optimizationâ€, Industrial and Systems Engineering Research Conference, 2014.

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Published

2017-02-15

How to Cite

An Integrated Model to Control Traffic Lights: Controlling of Traffic Lights in Multiple Intersections using Fuzzy Logic and Genetic Algorithm. (2017). Asian Journal of Business and Management, 5(1). https://ajouronline.com/index.php/AJBM/article/view/4120

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