MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES

By the dramatic growth of the population in cities requires the traffic systems to be designed efficiently and sustainably by taking full advantage of modern-day technology. Dynamic traffic flow is a significant issue which brings about a block of traffic movement. Thus, for tackling this issue, this paper aims to provide a mechanism to predict the traffic congestion with the help of Artificial Neural Networks (ANN) which shall control or minimize the blockage and result in the smoothening of road traffic. Proposed Modeling Smart Road Traffic Congestion Control using Artificial Back Propagation Neural Networks (MSR2C-ABPNN) for road traffic increase transparency, availability and efficiency in services offered to the citizens. In this paper, the prediction of congestion is operationalized by using the algorithm of backpropagation to train the neural network. The proposed system aims to provide a solution that will increase the comfort level of travellers to make intelligent and better transportation decision, and the neural network is a plausible approach to find traffic situations. Proposed MSR2C-ABPNN with Time series gives attractive results concerning MSE as compared to the fitting approach.