Short-Term Traffic Flow Forecasting Based on Wavelet Network Model Combined with PSO

The real time adaptive control of urban traffic, as a complex large system, usually needs to know the traffic of every intersection in advance. So traffic flow forecasting is a key problem in the real time adaptive control of urban traffic. This paper proposed an improved wavelet network model (WNM) which combined with particle swarm optimization (PSO) to forecast urban short-term traffic flow, PSO algorithm is used to determine the weights and parameters of WNM, which can avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. The simulation results show that the average time cost of the proposed method in the flow forecasting process is reduced by 8s, and the precision of the proposed method is increased by 4.23% compared to the standard WNM model.