A Novel Framework for Traffic Management using Machine Learning
Traffic Congestion is becoming a worldwide problem, it is necessary to find out the solution. It is necessary to find Traffic Congestion factors for reducing the level of the congestion. Addressing Traffic Congestion is a challenging and time-demanding task that requires a large research study to ensure successful observing situations. Our systematic approach uses modeling on travel situations for the purpose of analyzing the state of traffic and offers an immediate solution to travelers and traffic administrators. This is possible by deploying to measure the level of congestion at a particular time and its magnitude. By having prior knowledge of traffic congestion, the traveler can take a decision. The significance of modeling helps in saving cost, time and money. Therefore, it is necessary to develop foolproof solutions by creating new modeling ideas or combining existing modeling techniques to address the issues of road safety support, human life safety support, and reducing traffic congestion. This paper discusses about modeling the level of traffic congestion and gives the best solution.