A Rule based Fuzzy controlled Decision Support System for Intelligent Traffic Control System

Authors

  • Varshney M Research Scholar, Department of Computer Science, Mewar University, Chittorgarh (Raj), India
  • Srivastava AK Department of Computer Science, Mewar University, Chittorgarh (Raj), India
  • Aggarwal A School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India

DOI:

https://doi.org/10.26438/ijcse/v6i11.560564

Keywords:

Fuzzy Logic, Fuzzy Inference Systems (FIS), Decision support system, Traffic control system

Abstract

Congestion of roads particularly at different junction points due to vehicular traffic has become a chronic problem all around. Right now in India, a static timer is used to control the timing of the traffic light which results in a lot of problems. This paper introduces a fuzzy logic (FL) based decision support system (DSS) for intelligent traffic control system. The primary focus of the paper is on the algorithm used to reduce the time spent extra on the traffic light junction so as to save the fuel, time and to reduce the possibility of accidents occurring at the traffic light junction. The proposed system uses three input parameters; namely maximum length of vehicles behind traffic light, left green time, and no. of vehicles reaching the traffic light in a short period of time and one output, extension time which is used to control the congestion at the traffic light junction. Through decision support system, the meaning of transferred data is translated into linguistic variables that can be understood by non-experts. Mamdani inference engine is used to deduce from the input parameters

References

[1] F. V. Webster, “Traffic signal settings,” Road Research Technical. London, U.K., 1958, Paper No. 39, Road Res. Lab.

[2] R. Akcelik,“Time-dependent expressions for delay, stop rate and queue length at traffic signals,” Australia, 1980, Australian Road Res. Board.

[3] R. Akcelik, “Traffic signals: Capacity and timing analysis,” Australia, 1981, Australian Road Res. Board.

[4] D. I. Robertson and R. D. Bretherton, “Optimizing networks of traffic signals in real-time: The SCOOT method,” IEEE Trans. Veh. Technol., vol. 40, no. 1, pp. 11–15, 1991.

[5] P. R. Lowrie, “The Sydney coordinated adaptive traffic system- principles, methodology, algorithms,” in Proc. Int. Conf. Road Traffic Signalling, London, UK, 1982, pp. 67–70, Inst. Elect. Eng..

[6] Prince Singha, Aditya, Kunal Dubey, Jagadeeswararao Palli, “Toolkit for Web Development Based on Web Based Information System,” Isroset-Journal (IJSRCSE), 6, no. 5, pp. 1-5. 2018.

[7] Shubham, Deepak Chahal, Latika Kharb, “Security for Digital Payments: An Update,” Journal (IJSRNSC), 6, no. 5, pp. 51-54. 2018.

[8] C. P. Pappis and E. H. Mamdani, “A fuzzy logic controller for a traffic junction,” IEEE Trans. Syst., Man, Cybern., vol. SMC-7, pp. 707–717, 1977.

[9] J. Horn, N. Nafpliotis, and D. E. Goldberg, “A niched pareto genetic algorithm for multi-objective optimization,” in Proc. First IEEE Conf. Evolutionary Computation, IEEE World Congr. Computational Intelligence, 1994, vol. 1, pp. 82–87.

Downloads

Published

2025-11-18
CITATION
DOI: 10.26438/ijcse/v6i11.560564
Published: 2025-11-18

How to Cite

[1]
M. Varshney, A. K. Srivastava, and A. Aggarwal, “A Rule based Fuzzy controlled Decision Support System for Intelligent Traffic Control System”, Int. J. Comp. Sci. Eng., vol. 6, no. 11, pp. 560–564, Nov. 2025.

Issue

Vol. 6 No. 11 (2018): IJCSE November Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.