Development of Intelligent Traffic Control System by Implementing Fuzzy-logic Controller in Labview and Measuring Vehicle Density by Image Processing Tool in Labview

Authors

  • Viral K Patel Control and Instrumentation Department,Gujarat State Fertilizers and Chemicals Ltd, Vadodara, India
  • Maitri N Patel Student (M.Tech.-Control & automation) Nirma University, Ahmedabad, India

Keywords:

Fuzzy Logic, Vision Sensor, Rationalized Delay, Adjacent, Rule.

Abstract

In this paper, we have described a whole new approach of rationalization of existing traffic control systems by means of Fuzzy logic based control system and Vision sensors based vehicle counting method. As Conventional traffic control systems are inefficient because they provide fixed time-delay though no vehicle is present in that lane. So it results in congestion of vehicles on the adjacent side. And it generates the Noise and Air pollution, which is undesirable and not favorable. In an Intelligent traffic control system (ITCS), vision sensors will measure the number of vehicles on arrival as well as on queue side, and fuzzy logic rule based system will provide the rational time-delay, which is dependent on vehicle density on both arrival and queue side. So it will give zero delays if no vehicle is present in that lane. So ITCS works with intelligence given by the Fuzzy logic system.

References

[1]. A. Hegyi, B. De Schutter, S. Hoogendoorn, R. Babu?ska, and H. van Zuylen, “Fuzzy decision support system for traffic control centers,” Proceedings of the European Symposium on Intelligent Techniques (ESIT 2000), Aachen, Germany, pp. 389–395, Sept. 2000. PaperBC-01-2.
[2]. Rongtao Hou1, Qin Wang1, Jin Wang1, Jinjia Wang2, Yu Lu1 and Jeong-Uk Kim3, “A Fuzzy Control Method of Traffic Light with Countdown Ability”, International Journal of Control and Automation Vol. 5, No. 4, December 2012.
[3]. Z. Yang, H. Liu and C. Du, “Study of fuzzy control for intersections with traffic intensity being priority”, Computer Engineering and Applications, vol. 45, no. 36, (2009).
[4]. José E. Naranjo, Carlos González, Ricardo García, and Teresa de Pedro, “Using Fuzzy Logic in Automated Vehicle Control”, Published by the IEEE Computer Society.
[5]. E. Dickmanns, “The Development of Machine Vision for Road Vehicles in the Last decade”, Proc. IEEE Intelligent Vehicle Symp, vol. 1, IEEE Press, 2002, pp. 268–281.
[6]. Stephen Chiu and Sujeet Chand, “Self-Organizing Traffic Control via Fuzzy Logic”, Proc. 32nd IEEE Conf. on Decision & Control San Antonio, Texas - December 1993, pp. 1897-1902.
[7]. Zadeh, L., Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst., Man, Cybern. Vol. SMC-3, No. 1, pp. 28-44, 1973.
[8]. Favilla, J., Machin, A., and Gomide, F., Fuzzy traffic control: adaptive strategies, Proc. 2nd IEEE Int. Conf. on Fuzzy Systems, pp. 506-511, San Francisco, CA, March 1993.
[9]. http://sine.ni.com/nips/cds/view/p/lang/en/nid/209054
[10]. http://www.ni.com/vision/

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Published

2016-12-27

How to Cite

K Patel, V., & N Patel, M. (2016). Development of Intelligent Traffic Control System by Implementing Fuzzy-logic Controller in Labview and Measuring Vehicle Density by Image Processing Tool in Labview. American Scientific Research Journal for Engineering, Technology, and Sciences, 26(4), 406–417. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/2480

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Articles