SMART TRAFFIC LIGHT SIMULATION
EOI: 10.11242/viva-tech.01.09.15
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Citation
Akshata Chaudhari, Aarya Kini, Priyanka Kini, Prof. Poonam Jadhav, " SMART TRAFFIC LIGHT SIMULATION ", VIVA-IJRI Volume 1, Issue 9, Article 15, pp. 1-7, 2026. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.
Abstract
With the rapid growth of urbanization and increasing vehicle density, efficient traffic management has become a major challenge. Conventional traffic signal systems operate on fixed time intervals, leading to traffic congestion, longer waiting times, and increased pollution. To overcome these limitations, this project presents a Smart Traffic Light Simulation System that dynamically controls traffic signals based on real-time traffic conditions. The proposed system uses a hybrid approach for traffic detection. An ultrasonic sensor is employed to detect vehicle density in the North South direction, while a camera-based vision system is used to analyze traffic flow in the East–West direction. Vehicle detection and counting are performed using Python with OpenCV and YOLO-based object detection techniques. An Arduino Uno microcontroller controls the traffic signal LEDs and ensures safe signal transitions through serial communication. Additionally, environmental sensors such as the DHT11 temperature and humidity sensor and the MQ-135 air quality sensor are integrated to monitor surrounding conditions at the intersection. By combining sensor-based detection, computer vision, and intelligent decision-making, the system effectively reduces traffic congestion, minimizes waiting time, and improves overall traffic efficiency. The proposed model supports the development of smart and sustainable traffic management systems for future smart cities
Keywords
- Smart Traffic Light System, Traffic Density Detection, Computer Vision, YOLO, Arduino Uno, Ultrasonic Sensor, IoT, Air Quality Monitoring, Real-Time Traffic Control.
References
- Arduino Documentation, “Arduino Uno Microcontroller and Embedded System Programming”.
- OpenCV Documentation, “Open Source Computer Vision Library for Image and Video Processing”.
- J. Redmon and A. Farhadi, “YOLO: You Only Look Once – Unified, Real-Time Object Detection”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
- A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection”, arXiv preprint arXiv:2004.10934, 2020.
- Adafruit Learning System, “Ultrasonic Distance Sensor – HC-SR04”.
- DHT11 Sensor Datasheet, “Digital Temperature and Humidity Sensor”.
- MQ-135 Gas Sensor Datasheet, “Air Quality Monitoring Sensor”.
- Python Software Foundation, “Python Programming Language Documentation”.
- A. K. Mittal and D. Bhandari, “Smart Traffic Light Control System Using Image Processing”, International Journal of Engineering Research and Technology (IJERT), 2018.
- S. Sharma and R. Kaur, “IoT-Based Intelligent Traffic Signal System for Smart Cities”, International Journal of Advanced Research in Computer Science, 2019.
- Ministry of Road Transport and Highways (MoRTH), Government of India, “Road Traffic Statistics and Smart Traffic Management Initiatives”.
- World Health Organization, “Air Pollution and Health – Urban Traffic Emissions”.