Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network

  • Mohammed Abdulmaged Faraj University of Human Development, College of Science and Technology, Department of Computer Science, Sulaymaniyah, Iraq, College of Science, Department of computer, University of Sulaimani, Sulaymaniyah, Iraq
  • Najmadin Wahid Boskany College of Science, Department of computer, University of Sulaimani, Sulaymaniyah, Iraq


Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.


[1] N. Diaz, J. Guerra, and J. Nicola. “Smart Traffic Light Control System”. In: 2018 IEEE 3rd Ecuador Tech. Chapters Meet, ETCM 2018, 2018.
[2] I. Santos-González, P. Caballero-Gil, A. Rivero-García, and C. Caballero-Gil. “Priority and collision avoidance system for traffic lights”. Ad Hoc Networks, vol. 94, pp. 101931, 2019.
[3] A. H. Akoum. “Automatic traffic using image processing.” Journal of Software Engineering and Applications, vol. 10, no. 9, pp. 765-776, 2017.
[4] H. Joo, S. H. Ahmed and Y. Lim. “Traffic signal control for smart cities using reinforcement learning”. Computer Communications, vol. 154, pp. 324-330, 2020.
[5] A. Dumka and A. Sah. Smart Ambulance Traffic Management System (SATMS)-a Support for Wearable and Implantable Medical Devices. Elsevier Inc., Amsterdam, 2020.
[6] M. M. Elkhatib and A. S. Alsamna. “Smart Traffic Lights using Image Processing Algorithms”. In: 2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering, pp. 1-6, 2019.
[7] E. L. H. Imad. “Proposed Solutions for Smart Traffic Lights using Machine Learninig and Internet of Thing”. In: 2019 International Conference on Wireless Networks and Mobile Communications, pp. 1-6, 2019.
[8] L. F. P. Oliveira, L. T. Manera and P. D. G. Luz. “Smart Traffic Light Controller System”. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security, pp. 155-160, 2019.
[9] T. A. Kareem and M. K. Jabbar. Design and Implementation Smart Traffic Light Using GSM and IR. pp. 1-5, 2018.
[10] M. B. Natafgi, M. Osman, A. S. Haidar and L. Hamandi. Smart Traffic Light System Using Machine Learning”. In: 2018 IEEE International Multidisciplinary Conference on Engineering Technology, pp. 1-6, 2019.
[11] M. Z. Ismail, A. Z. A. Lutfi and M. A. M. Roslan. “Smart traffic light for emergency vehicle by using arduiono”. AIP Conference Proceedings, vol. 2129, pp. 1-6, 2019.
[12] Andreas, C. R. Aldawira, H. W. Putra, N. Hanafiah, S. Surjarwo and A. Wibisurya. “Door security system for home monitoring based on ESp32”. Procedia Computer Science, vol. 157, pp. 673-682, 2019.
[13] J. Redmon and A. Farhadi. YOLOv3: An Incremental Improvement, 2018.
[14] Y. Jamtsho, P. Riyamongkol and R. Waranusast. “Real-time Bhutanese license plate localization using YOLO”. ICT Express, vol. 6, no. 2, pp. 121-124, 2019.
[15] Y. Jamtsho, P. Riyamongkol and R. Waranusast. Real-time License Plate Detection for Non-helmeted Motorcyclist using YOLO. ICT Express, Amsterdam, 2020.
[16] F. Koyanagi. ESP32 With ESP-Now Protocol, 2019.
How to Cite
FARAJ, Mohammed Abdulmaged; BOSKANY, Najmadin Wahid. Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network. UHD Journal of Science and Technology, [S.l.], v. 4, n. 2, p. 123-131, dec. 2020. ISSN 2521-4217. Available at: <>. Date accessed: 16 june 2021. doi: