AI-driven traffic signal system with YOLO for dynamic congestion control
Ravi Saharanravisaharan@curaj.ac.inDepartment of Computer Science & EngineeringCentral University of RajasthanAjmer, Rajasthan, 305817, IndiaView full profile → , Omkar Ji Sainiomkarjisaini2004@gmail.comDepartment of Computer ScienceCentral University of RajasthanAjmer, Rajasthan, 305817, IndiaView full profile → , *Ravi Raj ChoudharyCorresponding authorraviraj@curaj.ac.inDepartment of Computer ScienceCentral University of RajasthanAjmer, Rajasthan, 305817, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 May 2025
- Published Online:
- 27 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1557
- Pages:
- 517–527
Abstract
Keywords
Subject Classifications
References
[1] M. B. Natafgi, M. Osman, A. S. Haidar, and L. Hamandi, “Smart traffic light system using machine learning,” in IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 1–6 (2018).
[2] F. Sultana, R. Bhardwaj, R. N. Kumar, R. Gaur, S. Shankar, and A. Bharadwaj, “Smart traffic management system for efficient mobility and emergency response,” in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), vol. 1, pp. 1–5 (2024).
[3] C. Ashokkumar, D. A. Kumari, S. Gopikumar, N. Anuradha, R. S. Krishnan, and R. Santhana Krishnan, “Urban traffic management for reduced emissions: Ai-based adaptive traffic signal control,” in 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), pp. 1609–1615 (2024).
[4] J. Dinesh C, S. Shrinidhi, S. Amaran, K. S. Kumar, and U. Karthikeyan, “YOLO-based traffic signal optimization for intelligent traffic flow management,” in Proc. 2024 8th Int. Conf. I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Kirtipur, Nepal, pp. 828–831 (2024).
[5] V. Dankan Gowda, K. S. Yogi, I. V. Srinivas, B. K. Kumar, D. Srinivas, and N. S. Reddy, “Ai and machine learning for intelligent traffic management in iot-connected cities,” in 2024 Asian Conference on Intelligent Technologies (ACOIT), pp. 1–8 (2024).
[6] S. Usmonov, A. Pradeep, Z. Fakhriddinov, T. Sanjar, A. Abdurakhim, and M. Khusniddinova, “Intelligent traffic management system: Ai-enabled iot traffic lights to mitigate accidents and minimize environmental pollution,” in 2023 3rd International Conference on Intelligent Technologies (CONIT), (2023).
[7] K. P. Muriuki, J. O. Okello, and J. Chepkoech, “Advanced intelligent traffic management system(aitms): A generative ai-enhanced model,” in IEEE PES/IAS PowerAfrica, pp. 1–3, (2024).
[8] C. Monica, B. Jyothi, A. Ramagiri, S. Gottipati, V. Jahnavi, S. A. Akther, and R. Chinnaiyan, “Intelligent traffic monitoring, prioritizing and controlling model based on GPS,” in Proc. 2023 Int. Conf. on Intelligent Data Communication Technologies and Internet of Things (ICIDCA), pp. 297–299 (2023).
[9] K. Dhatchayani, R. Shubavathy, G. Reshma, A. A. Eunice, D. Sundar, and D. Vezhaventhan, “Optimizing smart city infrastructure using 5g edge ai with adaptive multi-agent reinforcement learning,” in International Conference on Visual Analytics and Data Visualization (ICVADV), pp. 1286–1292 (2025).
[10] X. Mu, “Public Security Road Traffic Management Strategy based on Big Data and Intelligent Dispatching System,” 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, pp. 589-592 (2022), doi: 10.1109/ICSCDS53736.2022.9760758.
[11] J. N. P. Markavathi, N. Saravanaselva, M. R. Viswam, M. Veeramani, and M. Pandikannan, “Real-time traffic density monitoring and adaptive signal control using YOLOv8 and Arduino-based LED system,” in Proc. 2024 9th Int. Conf. on Communication and Electronics Systems (ICCES), pp. 227–232 (2024).
[12] A. Kumar, N. Gupta, R. Misra, S. Sharma, D. Chaudhary, and G. Sharma. “Deep learning based highway vehicles detection and counting system using computer vision,” Journal of Information & Optimization Sciences, vol. 44, no. 5, pp. 997-1008 (2023).
[13] H. Sharma, “Commutative encryption-based video encoding technique with high-efficiency and video adaptation capabilities,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 24, no. 8, pp. 2207–2219 (2021).
[14] V. Pasupathy and R. Khilar, “Advancements in deep structured learning based medical image interpretation,” Journal of Information and Optimization Sciences, vol. 43, no. 5, pp. 1131–1138 (2022).




