Vehicle Speed Detection System Using IoT and Machine Learning
Downloads
Abstract
This paper outlines a detailed strategy for detecting vehicle speed, utilizing Internet of Things (IoTs) and Machine Learning (ML) technologies. Conventional radar-based systems for speed detection face challenges related to scalability, cost, and effectiveness in intricate settings. The suggested system combines IoTs sensors, including Light Detection and Ranging (LIDAR), radar, and high-definition cameras, with ML techniques such as You Only Look Once (YOLO) and regression models to achieve real-time speed detection and anomaly monitoring. The processing of data in real-time through edge and cloud computing allows for swift and effective traffic management solutions. Findings demonstrate enhanced accuracy, scalability, and cost efficiency, carrying significant consequences for the infrastructure of smart cities.
Keywords
Vehicle speed detection Internet of things Machine learning Smart city Traffic managementReferences (10)
- [1] Cate, M., & Olayemi, A. (2025). IoT-based automated speed detection system for urban areas. https://www.researchgate.net/publication/389028146
- [2] William, B., & Olayemi, A. (2025). Integration of IoT with automated speed limit detection systems. https://www.researchgate.net/profile/Bruce-William-2/publication/388762841
- [3] Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., & Saba, T. (2024). Smart cities: the role of internet of things and machine learning in realizing a data-centric smart environment. Complex & intelligent systems, 10(1), 1607–1637. https://doi.org/10.1007/s40747-023-01175-4
- [4] Khan, S. U., Alam, N., Jan, S. U., & Koo, I. S. (2022). IoT-enabled vehicle speed monitoring system. Electronics, 11(4), 614. https://doi.org/10.3390/electronics11040614
- [5] Jamshed, M. A., Ali, K., Abbasi, Q. H., Imran, M. A., & Ur-Rehman, M. (2022). Challenges, applications, and future of wireless sensors in internet of things: A review. IEEE sensors journal, 22(6), 5482–5494. https://doi.org/10.1109/JSEN.2022.3148128
- [6] Grimes, D. M., & Jones, T. O. (1974). Automotive radar: A brief review. https://doi.org/10.1109/PROC.1974.9520
- [7] Panda, A. K., Lenka, A. A., Mohapatra, A., Rath, B. K., Parida, A. A., & Mohapatra, H. (2025). Integrating cloud computing for intelligent transportation solutions in smart cities: A short review. In interdisciplinary approaches to transportation and urban planning (pp. 121-142). IGI Global. https://doi.org/10.4018/979-8-3693-6695-0.ch005
- [8] Alomari, A. H., Khedaywi, T. S., Marian, A. R. O., & Jadah, A. A. (2022). Traffic speed prediction techniques in urban environments. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e11847
- [9] Mohapatra, H., & Rath, A. K. (2021). An IoT based efficient multi-objective real-time smart parking system. International journal of sensor networks, 37(4), 219–232. https://doi.org/10.1504/IJSNET.2021.119483
- [10] Priyadarshi, S., Subudhi, S., Kumar, S., Bhardwaj, D., & Mohapatra, H. (2025). Analysis on enhancing urban mobility with IoT-integrated parking solutions. In interdisciplinary approaches to transportation and urban planning (pp. 143–172). IGI Global. https://doi.org/10.4018/979-8-3693-6695-0.ch006