Real-Time Traffic Flow Prediction using IoT-Driven Machine Learning

Abstract

Road accidents result in deaths, infections, and many injuries. The main causes of these accidents are traffic congestion, road blockages, and traffic anomalies. Several factors, such as busy routes, damaged roads, or incidents, can trigger traffic. Traffic and roadblocks primarily cause time wastage, energy consumption, delays in reaching destinations, and accidents. Tracking traffic patterns may be a solution to this problem. However, IoT has been successful in a wide range of applications, such as healthcare and inventory management; the downside of tracking traffic is that it is difficult to manage. Therefore, to address this issue, we will design and develop a traffic flow forecasting system using an IoT framework and machine learning. To train and test this system, we will use the ANFIS model, which will then integrated into an Android application. This framework will identify the traffic patterns with the help of IoT sensors in real time, taking into account specific origins, times, peak hours, and speeds. It will then display these patterns in a graphical user interface, enabling users to understand traffic flow pattern and select the most efficient route to reach their destination on time.