An intelligent IoT Enabled Traffic queue handling System Using Machine Learning Algorithm

Overcrowding, pollutants, and logistical delays have been exacerbated by the fast expansion in the number of automobiles. The Internet of Things (IoT) is a new breakthrough that is bringing the world closer to fully automated processes and systems with sophisticated control. The growth of a nation on the other side of the world leads to an increase in personal automobiles. As a result, metropolitan cities have seen an increase in traffic. A improved traffic management system is needed, therefore. Good and dependable traffic control and bottleneck management may save a lot of money and time. IoT-based ITM sensors are installed in automated cars and smart gadgets to detect, acquire, and send data.. The use of machine learning (ML) may also help to enhance the transportation system. Because of the many issues with current transportation management systems, there is a high prevalence of traffic congestion, delays, and fatalities. Adaptive traffic control based on machine learning and the internet of things is the subject of this study. For the purposes of this proposal, vehicles, infrastructure, and events all play a key role in its design. The design uses a variety of situations to address all of the transportation system's challenges. A machine-learning-based DBSCAN clustering algorithm is included into the system's design to catch any unintentional anomalies. The suggested algorithm continually adjusts traffic light timings based on traffic flow and predicted movements from neighboring intersections. In addition to reducing travel time, it also reduces traffic congestion by creating a smoother transition. For smart-city-based transportation systems, the suggested traffic-management technique greatly outperforms the traditional approach, according to the results of an experiment. Decreases traffic jams, decrease traffic fatalities and enhances the overall route experience via the use of a suggested solution.