Attention Based Spatial-Temporal Dynamic Interact Network for Traffic Flow Forecasting

Abstract

The prediction of spatio-temporal traffic flow data is challenging due to the complex dynamics among different roads. Existing approaches often focused on capturing traffic patterns at a single temporal granularity, disregarding spatio-temporal interactions and relying heavily on prior knowledge. However, this limits the generality of the models and their ability to adapt to dynamic changes in traffic patterns. We argue that traffic flow changes co-occur in the road network’s temporal and spatial dimensions, which leads to commonalities and regularities in the data across these dimensions, with their dynamic changes depending on the temporal granularity. In this research, we propose an attention based spatio-temporal dynamic interaction network consisting of a spatio-temporal interaction filtering module and a spatio-temporal dynamic perception module. The interaction filtering module captures commonalities and regularities from a global perspective, ensuring adherence to the temporal and spatial dimensions of the road network structure. The dynamic perception module incorporates a sliding window attention mechanism to capture local dynamic correlations between the temporal and spatial dimensions at different time granularities. To address the issue of time series span, we design a more adaptive time-aware attention mechanism that effectively captures the impact of time intervals. Extensive experiments on four real-world datasets demonstrate that our approach achieves state-of-the-art performance and consistently outperforms other baseline methods. The source code is available at https://github.com/JunweiXie/ASTDIN.