Real-time Road Surface Assessment through IoT Data Analysis and Random Forest

Using the Internet of Things (IoT) and the Random Forest algorithm, this research proposes a way to evaluate the condition of road surfaces in real-time. For transportation networks to be safer and more efficient, it is necessary to monitor and evaluate the state of the roads continuously. The IoT collects data from various sources, including temperature, humidity, vibration, and traffic flow, and processes it using the Random Forest algorithm. The data obtained is processed using Random Forest, a popular and effective algorithm for classification jobs. Technology can predict and identify problems like potholes, cracks, or surface degradation by training the model with previous data and enhancing it with real-time input. This foresight reduces the likelihood of accidents and keeps traffic flow uninterrupted, allowing for prompt repair actions. An efficient and scalable way to manage road infrastructure is to combine IoT with machine learning. This will improve road safety and performance in the long run.