A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision



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Submitted Aug 4, 2021
Published Aug 31, 2021
10.24018/ejece.2021.5.4.353

Abstract viewed = 512 times

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In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Keywords: Computer Vision, Convolutional Neural Networks (CNNs), Traffic Density Estimation

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