The prediction of spatiotemporal correlation of short-term traffic flow based on multi-level data decomposition and adaptive aggregation algorithm

Abstract

Traffic flow prediction is of great significance to the development of intelligent transportation. Since most of the current research is to improve the prediction structure, ignoring the impact of the nonlinear and unstable characteristics of the data itself on the prediction, this paper proposes an algorithm that combines data decomposition and spatiotemporal correlation prediction. In the decomposition part, VMD (variational mode decomposition) and CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) are introduced to decompose the original data, and the subsequences are adaptively aggregated based on the permutation entropy theory. The spatiotemporal correlation prediction part uses DTW (dynamic time warping) to select the site most relevant to the measured site, and then weighs the predicted values of multiple sites according to the rank index method to obtain the final predicted value. Finally, the experimental results show that the method in this paper can extract traffic flow trend information well, and achieve better accuracy in the short-term forecasting.