Construction and Analysis of Urban Traffic Noise Prediction Model Based on PSO Algorithm and BRF Network Structure

Abstract

Due to the current nonlinear traffic noise and changing natural environment, accurate prediction of traffic noise has become a major challenge. In order to improve the accuracy of traffic noise prediction, this paper constructs a short-term traffic noise prediction model based on PSO algorithm and BRF network structure. The model can predict the noise state of future traffic according to historical traffic information. Particle swarm optimization (PSO) network is used for short-term traffic prediction to deal with the problems of low accuracy and long-time consumption. A BRF (one-dimensional convolution)-PSO short-term traffic noise prediction model is proposed. BRF method is used to extract the spatial features of traffic noise and compared with PCA (principal component analysis)-PSO model and PSO model. The results show that the prediction accuracy of BRF-PSO model is 2% higher than that of PCA-PSO model and 6% higher than that of PSO model, which proves the effectiveness of BRF-PSO model. This study provides a technical reference for the short-term traffic noise prediction of intelligent city and the effective improvement of urban traffic environment.