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Abstract

Short-term traffic flow prediction plays a critical role in alleviating traffic congestion and improving road utilization efficiency. However, due to the intricate spatiotemporal variation patterns in traffic flow data, many existing methods struggle to effectively capture multi-scale temporal features and the influence of spatial locations on flow changes. To tackle this issue, we propose a decomposition and reconstruction network model based on spatiotemporal attention mechanisms, called ST-ADRNet. ST-ADRNet uses trigonometric encoding to generate time position vectors for capturing multi-scale temporal patterns and introduces trend offset vectors to improve stability and long-term prediction accuracy. In the decomposition module, time position and flow feature vectors are processed using a hybrid attention mechanism to combine flow data with time information, while geographical and similarity spatial attention balance the effects of distance and flow similarity. In the reconstruction module, these learned vectors are integrated and processed through temporal and spatial attention modules to further extract multi-scale temporal and spatial features. The experimental results demonstrate that the ST-ADRNet model significantly outperforms the baseline models in predicting traffic flow for the next 60 min. On the PeMSD4 dataset, ST-ADRNet achieves improvements of 16.84, 27.45, and 10.23% in MAE, RMSE, and MAPE, respectively. On the PeMSD8 dataset, ST-ADRNet achieves improvements of 12.08, 20.89, and 8.23% in the same metrics. On the METR-LA dataset, ST-ADRNet achieves improvements of 3.10, 6.78, and 9.02% in the same metrics.

Keywords

traffic flow prediction spatiotemporal attention mechanisms time position vectors trend offset vectors hybrid attention mechanism

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Spatio-Temporal attention-based decomposition and reconstruction network for urban traffic flow prediction

Abstract

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