Prediction of Traffic Congestion through Convolutional Neural Network

In today's world, the usage of vehicles has increased that leads to vehicle traffic congestion. In this scenario, it is difficult to detect and control the traffic flow using man power and it is a time consuming process. In our proposed system we apply convolutional neural network to identify the traffic based on 3 categories namely low, medium and heavy regions. In order to identify the congestion state, we use two models of convolutional neural network namely VGG16 and ResNet50. In both of the models we use optimiser algorithm as ‘Adam optimiser’. By analysing the number of vehicles moving in a road, we predict the traffic congestion. If there is heavy traffic congestion, then it will send an alert message to respective authorities. Hence the traffic will be cleared. Henceforth, Deep learning plays an important role in traffic management system.