Pi-Lot the Traffic: An IoT Based Signal Control System
Keywords:
Camera-based detection, Image processing, Real-time traffic control, Raspberry Pi, Vehicle detectionAbstract
This research paper proposes machine learning- based Smart Traffic Management System (STMS) to optimize traffic flow using ultrasonic sensors and a camera for real-time vehicle detection. The system processes sensor and image data using a Raspberry Pi, dynamically adjusting traffic signals based on congestion levels. Image processing techniques improve accuracy through object detection, while real-time monitoring helps in reduce wait times and prevent congestion. Using real-time data, the proposed system (STMS) predicts congestion hotspot optimizes traffic management, creating a safer, cleaner, and more efficient urban transportation system.
References
A. H. Akoum, “Automatic Traffic Using Image Processing,” Journal of Software Engineering and Applications, vol. 10, no. 09, pp. 765–776, 2017, doi: https://doi.org/10.4236/jsea.2017.109042.
M. Humayun, S. Afsar, M. F. Almufareh, N. Z. Jhanjhi, and M. AlSuwailem, “Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies,” Journal of Advanced Transportation, vol. 2022, pp. 1–13, Sep. 2022, doi: https://doi.org/10.1155/2022/4687319.
S. T. Bhuvan, H. R. Manjunath, H. R. Abhiman, R. Kumar, and S. G. Rao, ”Smart traffic management system: A literature review,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 10, no. 2, pp. 1-6, Feb. 2022, Available: https://ijireeice.com/wp-content/uploads/2022/02/IJIREEICE.2022.10201.pdf.
A. Ravi, R. Nandhini, K. Bhuvaneshwari, J. Divya, and K. Janani, ”Traffic management system using machine learning algorithm,” International Journal of Innovative Research in Technology, vol. 7, no. 11, pp. 303–308, Apr. 2021, Available: https://ijirt.org/publishedpaper/ijirt150994_paper.pdf.
A. Yadav, V. More, N. Shinde, M. Nerurkar, and N. Sakhare, “Adaptive Traffic Management System Using IoT and Machine Learning,” International Journal of Scientific Research in Science, Engineering and Technology, pp. 216–229, Jan. 2019, http://dx.doi.org/10.32628/IJSRSET196146
A. Dubey, M. Lakhani, S. Dave, and J. J. Patoliya, "Internet of Things based adaptive traffic management system as a part of Intelligent Transportation System (ITS)," 2017 International Conference on Soft Computing and its Engineering Applications (icsoftcomp), changa, india, 2017, pp. 1-6, doi: https://doi.org/10.1109/icsoftcomp.2017.8280081.
