Abstract: In the evolving landscape of smart cities, efficient traffic management is paramount to reducing congestion, minimizing accidents, and improving urban mobility. This paper presents a comprehensive approach to road vehicle detection utilizing the Internet of Things (IoT). The proposed system integrates various IoT devices, including sensors, cameras, and communication modules, to create a robust network capable of real-time vehicle detection and traffic monitoring. Our methodology employs a combination of sensor networks and edge computing to collect and process data locally, thereby reducing latency and enhancing responsiveness. The system architecture is designed to be scalable and adaptable, accommodating various urban environments and traffic conditions. By leveraging advanced data analytics and machine learning algorithms, the system can accurately identify vehicle types, count traffic flow, and detect anomalies such as accidents or traffic violations. To validate the effectiveness of the proposed system, extensive simulations and field tests were conducted in diverse traffic scenarios. The results demonstrate significant improvements in detection accuracy, real-time data processing, and overall system reliability. Moreover, the IoT-based vehicle detection system offers a cost-effective solution with potential applications in intelligent transportation systems, automated toll collection, and enhanced road safety measures. The integration of IoT in road vehicle detection presents a transformative approach to traffic management, promising smarter and more efficient urban transportation infrastructures. Future research directions include the enhancement of sensor fusion techniques, integration with autonomous vehicle systems, and the exploration of 5G networks to further augment system capabilities.

Keywords: Node MCU, Ultra-Sonic sensor, Power supply, IoT.

| DOI: 10.17148/IARJSET.2024.115108