DEVELOPING TRAFFIC CONGESTION DETECTION MODEL USING DEEP LEARNING APPROACH

Abstract

Traffic congestion is one of the most irritating problem that society face in their everyday activity. In Ethiopia, the number of vehicles and pedestrians is increasing at a high rate from time to time. Excessive numbers of traffic on roads and improper control of traffic create traffic congestion. Uncontrolled traffic congestion hinders the transportation of goods and commuters from place to place and increases the volume of carbon emitted into the air. It can also either hampers or stagnates schedule, business, and commerce. Many images and video processing approaches have been researched across the world to detect traffic congestion. One such approach is that of using background and foreground subtraction, average frame differencing, and convolutional neural network to detect traffic congestion from video