Study on the Prediction of Urban Traffic Flow Based on ARIMA Model

Abstract

Because the traffic flow has the characteristics of nonlinear and strong interference, good traffic model can accurately describe the flow characteristics, predict the changing trend of the flow and carry out the corresponding control measures to ensure the normal operation of traffic, thus improving the traffic pressure. In this paper, wavelet analysis is applied to analyze the complex network traffic. Time series are decomposed into approximate uncorrelated time series, and the time series are smoothed. The wavelet analysis is carried out by using the mathematical model, independent wavelet model and multi fractal wavelet model. Matlab and SPSS are used to analyze the data of traffic flow. The wavelet analysis and ARIMA model are used to predict the sequence of single branch reconstruction, which reduces the error and improves the feasibility.