Smart Traffic Management using Deep Learning

The recent era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals regardless of the relentless effort devoted to developing & improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. In this paper, we present the architecture of our proposed Smart Traffic Signal controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion. Traffic problem are increasing day by day and it is becoming a serious problem in the recent world [2]. Various traffic monitoring systems have been developed. Traffic is always a complex and challenging problem due to a mixture of different types of vehicles as well as the large number of vehicles on road. To improve the traffic management, it is critical to develop a real time traffic flow estimation system which can detect, classify and count vehicles, detect traffic violation at any given time. In this study, a multi-vehicle detection and tracking approach was proposed to achieve these requirements