Research on short-term traffic flow prediction based on wavelet de-noising preprocessing

Abstract

A Single traffic flow prediction method has weak applicability for short-time traffic flow prediction. In order to adapt to the needs for traffic guidance and signal control, a short-time traffic flow RBF neural network model combination prediction method based on wavelet de-nosing processing is put forward. First, the traffic flow data are decomposed and reconstructed by using the wavelet transform technique. Then Under the intensive analysis the characteristics of short-time traffic flow, the low frequency outline signal and high frequency detail signal are fitted respectively by using two different RBF neural network models, and particle swarm optimization (PSO) algorithm is proposed to train RBF neural network. The confirmation analysis is carried on with traffic flow data from typical roads in some city urban districts. The results show that the precision of combination prediction method is significantly improved.