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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63038
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Mathematics
Krishna's Vikash Institute of Technology, Raipur,CG
Computer Science and Engineering
Bhilai Institute of Technology, Durg,CG
Intelligent Traffic Control Systems are becoming essential in modern smart cities due to increasing traffic congestion and road accidents. Traditional traffic signal systems use fixed timing methods that are inefficient under varying traffic conditions. This research paper presents a Traffic Control System using Grey Fuzzy Logic and Artificial Intelligence techniques for adaptive traffic signal management. Grey System Theory is used for traffic prediction under uncertain and incomplete data conditions, while Fuzzy Logic is applied for intelligent decision-making. Artificial Intelligence techniques improve system adaptability and optimization. The proposed system reduces traffic congestion, waiting time, fuel consumption, and improves overall traffic efficiency.
KEYWORDS
This research paper presents an intelligent Traffic Control System using Grey System Theory, Fuzzy Logic, and Artificial Intelligence. The proposed system predicts traffic conditions and dynamically adjusts signal timings according to real-time traffic density. The integration of Grey Theory and Fuzzy Logic provides accurate prediction and intelligent decision-making under uncertain conditions. The system improves traffic management efficiency and supports smart city development. Future work may include integration with IoT devices and deep learning algorithms for more accurate traffic prediction.
[1] M. Varshney, A. K. Srivastava, and A. Aggarwal, “A Rule based Fuzzy controlled Decision Support System for Intelligent Traffic Control System,” International Journal of Computer Sciences and Engineering, vol. 6, no. 5, pp. 100–106, 2018 [2] T. D. Chala and L. T. Kóczy, “Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction,” Symmetry, vol. 16, no. 9, pp. 1–20, 2024. [3] G. Comert, N. Begashaw, and N. Huynh, “Improved Grey System Models for Predicting Traffic Parameters,” arXiv preprint arXiv:2011.09469, 2020. [4] A. C. Pinto and G. V. Mattioli, “Intelligent Traffic Lights Control System using Fuzzy Logic,” SAE Technical Paper 2014-36-0359, 2014. [5] T. Sayers, M. G. H. Bell, T. Mieden, and F. Busch, “Traffic Responsive Signal Control Using Fuzzy Logic: A Practical Modular Approach,” Transportation Research Board, 1996. [6] S. K. Dhillon and I. Singh, “Intelligent Traffic Control using Fuzzy Logic and Reinforcement Learning,” Journal of Fuzzy Sets and Fuzzy Logic Design, 2025. [7] “Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control,” Expert Systems with Applications, vol. 185, 2021. [8] R. R. Saxena, “Artificial Intelligence in Traffic Systems,” a rXiv preprint arXiv:2412.12046, 2024.
Publication Date
5/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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