International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Smart Traffic Light Control Intersection System |
|---|---|
| ABSTRACT | The Smart Traffic Light Control System is an intelligent traffic management solution designed to improve vehicle flow at busy urban intersections. Traditional traffic signals operate on fixed timers, which often lead to unnecessary waiting and traffic congestion because they do not consider real-time traffic conditions. This project introduces a dynamic traffic control system that adjusts signal timings based on vehicle density detected in each lane. The system uses YOLOv8 for real-time object detection to identify vehicles such as cars, buses, trucks, and motorcycles from live camera feeds. Video frames are processed using OpenCV, which enables accurate vehicle detection and counting. Based on the number of vehicles detected, the system dynamically assigns longer green signals to lanes with higher traffic density to reduce congestion and waiting time. A web-based monitoring interface built with Flask allows real-time visualization of traffic conditions and signal status. An important feature of the system is emergency vehicle prioritization, where the system detects vehicles such as ambulances or fire trucks and immediately switches the signal to green for that lane to ensure quick passage. After the emergency vehicle passes, the system returns to normal density-based operation. This project demonstrates how computer vision and intelligent algorithms can be used to create an adaptive and efficient traffic management system suitable for future smart city applications. |
| AUTHOR | A.HEMA LATHA, CH.MANASA, V.NAVEEN KUMAR , B.JOGESWARA RAO, K.YASWANTH, B.SIDDARTH Assistant Professor, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India Student of Department of CSE (Data Science), NSRIT, Vishakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403053 |
| pdf/53_Smart Traffic Light Control Intersection System.pdf | |
| KEYWORDS | |
| References | 1. Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767. 2. Jocher, G., et al. (2023). YOLOv8: Ultralytics YOLO Object Detection. Ultralytics. https://github.com/ultralytics/ultralytics 3. Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools. 4. Flask Documentation. (2026). Flask Web Development. https://flask.palletsprojects.com 13. Li, L., & Wang, X. (2020). Real-Time Traffic Flow Monitoring Using Computer Vision. IEEE Access, 8, 123456–123468. 5. Ahmed, M. S., & Cook, A. R. (1979). Analysis of Traffic Flow Data for Signal Control. Transportation Research Record, 722, 1–10. 6. Mir, F., & Kumar, R. (2021). Smart Traffic Light Control Systems Using AI and IoT. International Journal of Intelligent Transportation Systems Research, 19(4), 345–356. 7. Chen, Y., et al. (2019). Emergency Vehicle Priority in Smart Traffic Control Using AI. Journal of Transportation Technologies, 9(2), 150–163. 8. Parsa, S., & Azari, M. (2022). Adaptive Traffic Signal Control Based on Vehicle Density Using Machine Learning. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5678– 5689. |
