Predicting and Mitigating Road Hazards with IoT & Machine learning

Abstract

This paper presents an extensive method for the real-time prediction and mitigation of road risks using the integration of machine learning and Internet of Things (IoT) techniques, and machine learning approaches. For both drivers and pedestrians, road hazards can cause crashes, injuries, and even fatalities. Here we offer a novel approach that uses the integration of machine learning algorithms with Internet of Things (IoT) sensors to detect and mitigate road hazards in real-time. Firstly, deployed Internet of Things (IoT) sensors along roads to collect various data points such as weather, traffic density, road surface conditions, and vehicle speed. The data from these sensors is continuously received by a centralized system for analysis. Next, applied machine learning algorithms, like support vector machines, decision trees, and neural networks, to build prediction models that anticipate potential threats based on historical data trends and real-time inputs from the Internet of Things (IoT) network.