Short-term traffic flow forecasting model based on wavelet neural network

Short-term traffic flow forecasting plays an important role in the urban traffic control and guidance system. In the paper, the advantages of wavelet transform and artificial neural network are introduced. Focusing on the characteristics of time-variation and uncertainty of urban traffic flow, the paper adopts the combination of wavelet analysis and artificial neural network, establishes the short-term traffic flow forecasting model of wavelet neural network (WNN) and carries out independent test by rolling forecasting based on the measured data from traffic library. Simulation results indicate that, compared with the forecasting model of BP neural network, the WNN model has better forecasting precision and faster convergence speed, and wavelet neural network could be better applied in the short-term forecasting of traffic flow.