Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory

Based on the previous literature review, this paper builds a short-term traffic speed forecasting model using Support Vector Machine (SVM) regression theory (referred as SVM model in this paper). Besides the advantages of the SVM model, it also has some limitations. Perhaps the biggest one lies in choice of the appropriate kernel function for the practical problem; how to optimize the parameters efficiently and effectively presents another one. Unfortunately, these limitations are still research topics in current literature. This paper puts an effort to investigate these limitations. In order to find the effective way to choose the appropriate and suitable kernel function, this paper constructs a new kernel function using a wavelet function to capture the non-stationary characteristics of the short-term traffic speed data. In order to find the efficient way to identify the model structure parameters, this paper uses the Phase Space Reconstruction theory to identify the input space dimension. To take the advantage of these components, the paper proposes a short-term traffic speed forecasting hybrid model (Chaos–Wavelet Analysis-Support Vector Machine model, referred to as C-WSVM model in this paper). The real traffic speed data is applied to evaluate the performance and practicality of the model and the results are encouraging. The theoretical advantage and better performance from the study indicate that the C-WSVM model has good potential to be developed and is feasible for short-term traffic speed forecasting study.