Congestion and Accident Alerts Using Cloud Load Balancing & Random Forest in VANET

The traffic forecast system is critical in intelligent transportation system. Vehicular Ad-hoc Networks will play a significant role in future intelligent transportation system (VANET). On the highways, they give perfect service to the drivers. For traffic control systems, information systems for drivers and passengers, such as speed, journey time, congestion, emergency services, weather alerts, and many more services, pattern clarity and traffic forecast are critical. Problematic nonlinear pattern complicates these systems. This study offers a Cloud Based Random Forest Algorithm to prevent such issues (CRFA). For road facilities, the Random Forest approach is used to expect the next condition of traffic based on traffic changing in brief intervals of time. The suggested approach employs Random Forest on VANET to evaluate route optimization to get a congestion free road, accident detection, and prevention, as well as providing various services to drivers and passengers while maintaining QOS. Because it is based on the cloud and the Global Positioning System, even in foggy or misty conditions, each vehicle will know exactly where it is going, and if a target approaches another vehicle, a flash of warning will appear on the screen with a loud beep sound, resulting in the automatic reduction of speed to maintain the speed and distance between the vehicles.