Short-Term Traffic Flow Prediction With Wavelet and Multi-Dimensional Taylor Network Model

Accurate prediction of the traffic state has received sustained attention for its ability to provide the anticipatory traffic condition required for people's travel and traffic management. In this paper, we propose a novel short-term traffic flow prediction method based on wavelet transform (WT) and multi-dimensional Taylor network (MTN), which is named as W-MTN. Influenced by the short-term noise disturbance in traffic flow information, the WT is employed to improve prediction accuracy by decomposing the time series of traffic flow. The MTN model, which exploits polynomials to approximate the unknown nonlinear function, makes full use of periodicity and temporal feature without transcendental knowledge and mechanism of the system to be predicted. Our proposed W-MTN model is evaluated on the traffic flow information in a certain area of Shenzhen, China. The experimental results indicate that the proposed W-MTN model offers better prediction performance and temporal correlation, as compared with the corresponding models in the known literature. In addition, the proposed model shows good robustness and generalization ability, when considering data from the different days and locations.