The Role of Convolutional Neural Network in Vehicle Detection on Spatial - Temporal Road Traffic Data

Due to urbanization, people who reside in cities utilize different types of transportation based on their requirement. On regular working days, on-time travelling from one place to another is a difficult task. This results in on-road traffic. Traffic congestion is still a challenging task to be solved by using different methodologies. This research study discusses about different ways to measure traffic congestion in the Intelligent Transportation System (ITS) in Chennai. Since the proposed research study is focused on road traffic congestion, a convolutional neural network is used to detect the vehicles in road traffic by means of a trained neural network to recognize vehicles such as cars, two-wheelers, buses, trucks, vans, auto, etc. To display the road traffic images in different forms, computer vision technology is used. Additionally, the Chennai road traffic dataset is applied in a grouped bar graph to display the number of persons killed and injured due to traffic congestion from 2014 to 2021.