Road traffic prediction and congestion control using Artificial Neural Networks

Abstract

In past decade, the problem of traffic has become severe due to industrialization especially in big cities. Hence, the urban population has to invest much valuable time during traveling. Dynamic traffic flow and static traffic signal is major problem which results in congestion of traffic. Hence for solving this issue, this paper aims at prediction of road traffic using Artificial Neural Networks which will ultimately control congestion and results in the smoothening of overall traffic. Artificial Neural Network possess power to learn from past and predict the future[1]. Neurons - the back-bone of the neural network which are trained with real-time data(time-based data) based on which it predicts future traffic volume. This paper proposes the significance of Jordan sequential network for prediction of future values, depending upon the current value and aggregate past values and also guarantees prediction of traffic flow with accuracy of about 92-98% using Jordan's Sequential Network. Thus, this paper focuses on prediction of traffic flow using Jordan's Neural network with maximum accuracy and analysis on various parameters to obtain the same.