Every second, the traffic monitoring system receives vast quantities of data on the movement of vehicles. There is a significant time and effort commitment involved in monitoring these KPIs. Managing and controlling traffic might be made easier with the use of a deep learning technique called a Convolutional Neural Network. Data from traffic monitors that have already been analyzed is used to construct the training dataset. Building the Traffic net requires transferring the network to the new domain and retraining it using data from apps relevant to traffic. Regional detection is one of the large-scale applications for this Traffic net. Even more crucially, it may be used in a variety of contexts. Impressive evidence of efficiency may be shown in the case study's faster discovery and improved accuracy. Its incorporation into a traffic monitoring system and, in the long run, an enhanced intelligent transportation system may be the result of the preliminary examination.