Traffic Forecasting Based on Integration of Adaptive Subgraph Reformulation and Spatio-Temporal Deep Learning Model

Abstract

Traffic forecasting provides the foundational guidance for many typical applications in the smart city management, such as urban traffic control, congestion avoidance, and navigation guidance. Many researchers have focused on the spatio-temporal correlations under fixed topology structure in traffic network to improve the traffic forecasting accuracy. Despite their advantages, the existing approaches are not completely discussed that the association relationship among traffic network nodes are not invariable under different traffic conditions. In this paper, a novel traffic forecasting framework is proposed by integrating the dynamic association of traffic nodes with the spatio-temporal deep learning model. To be specific, an adaptive subgraph reformulation algorithm is designed first based on the specific forecasting interval to reduce the interference of irrelevant spatio-temporal information. After that, by enhancing the attention mechanism with the generative decoder, a spatio-temporal deep learning model with only one forward operation is proposed to avoid the degradation of accuracy in the long-term prediction, in which the spatio-temporal information and the external factors (such as weather and holiday) are fused together to be as an input vector. Based on the reformulated subgraph constructed of traffic nodes with closer spatio-temporal correlation, experiments show that the proposed framework consistently outperforms other GNN (Graph Neural Network)-based state-of-the-art baselines for various forecasting intervals on a real-world dataset.