Efficient Adaptive Spatial-Temporal Attention Network for Traffic Flow Forecasting

Abstract

Urban traffic flow prediction is a challenging task in the field of intelligent transportation and spatio-temporal data analysis. Accurate prediction of traffic states by leveraging sophisticated spatio-temporal patterns is critical. However, existing methods ignore the local validity of dynamic spatio-temporal auto-correlations, resulting in bottlenecks in the performance and efficiency of the model. In this work, we investigate the effects of dominant as well as invalid spatio-temporal patterns and propose a spatio-temporal forecasting framework. Specifically, we propose a dominant spatial-temporal attention mechanism, which extends the empirical approximation of Kullback-Leibler divergence to the spatial-temporal domain to optimize the computational efficiency of the attention mechanism, and identifies locally valid associations through dominant query generation. Meanwhile, we theoretically demonstrate the validity of the extension. Furthermore, we design an adaptive spatial-temporal fusion embedding scheme to generate heterogeneous and synchronous traffic states without pre-defined graph structures. We further propose an Efficient Adaptive Spatial-Temporal Attention Network (EASTAN) to capture fine-grained spatio-temporal dependencies based on the above modules and perform sequential forecasting. Extensive experiments (Code and appendix available at: https://github.com/ecmlpkdd2023/EASTAN ) on four real-world datasets show that the proposed framework improves the prediction accuracy by 3.31%–48.93%, and significantly reduces the training time as well as model parameters compared to state-of-the-arts.