Early Detection of Road Traffic Injuries with IoT Sensor Networks and K-Nearest Neighbors Algorithm

Injuries sustained in road accidents are a major worldwide public health concern, highlighting the need for effective early detection systems. By combining the K-Nearest Neighbors (KNN) algorithm with Internet of Things (IoT) sensor networks, this research provides a novel approach to the early identification of road traffic injuries. Critical characteristics like vehicle speed, acceleration, and closeness may be continually monitored in real-time by installing IoT sensors in vehicles and road infrastructure. The data is further processed using the KNN algorithm, which may detect trends that might point to the incidence of traffic injuries. This preventative method allows for prompt action, which may lessen the impact of injuries and save lives. One of the many benefits of the proposed system is its quick detection and reaction time, which helps improve road safety and public health. Results from experimental tests show that the suggested method may reliably identify and forecast injuries sustained in traffic accidents. The significance of using IoT technology and machine learning algorithms for preventing and mitigating injuries in transportation systems is highlighted in this study, which shows the evolution of road safety early warning systems.