Traffic Congestion Prediction using Real Time Data by using Deep Learning Techniques

Abstract

There is a broad range of the problems attributed to traffic congestion in modern urban transportation systems. This includes large increases in the amount of time to travel, increases in the amount of fuel used, as well as increases in the amount of pollution to the environment. For these reasons, the time and congestion of traffic is the most important thing to manage when it comes to transportation to get the most out of it. This is the primary reason traffic congestion management systems have been built over the years. Because of the explosion of the availability of real time traffic information, most analytically based ways of forecasting traffic congestion have been fundamentally replaced by the most advanced deep learning based forecasting techniques.