Ada-STNet: A Dynamic AdaBoost Spatio-Temporal Network for Traffic Flow Prediction

Jiawei Sun, Jie Li, Chentao Wu, Zili Tang, Celimuge Wu

Abstract: Traffic flow prediction is of particular interest since its massive applications in intelligent transportation systems (ITS). The problem is challenging due to the complex spatio-temporal correlations and nonlinearities of traffic flows. However, existing methods based on the graph neural networks cannot efficiently extract the dynamic and long-range spatial correlations, thus producing unsatisfactory prediction results. In this paper, we propose an AdaBoost Spatio-temporal Network (Ada-STNet). Similar to AdaBoost, Ada-STNet stacks several base neural networks as "layers" which capture spatial and temporal correlations simultaneously. Each layer learns an adaptive adjacency matrix from weights and embedding of nodes. The adjacency matrix is layer-wise adjusted to extract information from distant neighbors and adapt to dynamic correlations. Experiments are conducted on three real-world benchmark datasets, demonstrating that the Ada-STNet outperforms the state-of-the-art methods.