Prediction of network traffic of smart cities based on DE-BP neural network

Abstract

Smart cities make full use of information technology so as to make intelligence responses to all requirements, including network and city services. This paper proposes a differential evolution back propagation (DE-BP) neural network traffic prediction model applicable for a smart cities network to predict the network traffic. The proposed approach takes the impact factor of network traffic as the input layer and the network traffic as the output layer and trains the DE-BP network with the past traffic data so as to obtain the mapping relationship between the impact factor and the network traffic and get the predicted value of the network traffic. The experimental results show that the proposed approach can accurately predict the trend of network traffic. Within the allowable error range, the predicted traffic volume is consistent with the actual traffic volume trend, and the predicted error is small.