关键词: 交通流预测, 智能交通, 时空预测, 扩散机制, 自注意力机制

Abstract:

To address the limitations of the existing traffic flow prediction methods in capturing dynamic patterns of road network topology and local fine-grained features， a traffic flow prediction model with Spatial-Temporal Awareness and self-supervised Transformer （STA-Autoformer） was proposed. In the model， by integrating self-attention mechanisms with self-supervised learning strategies， the accuracy of spatial-temporal traffic flow prediction was improved. First， spatial and temporal self-attention mechanisms were integrated to overcome the limitation of the existing methods in capturing long-range spatial-temporal dependencies. Second， a dynamic topology awareness module was designed to address the local impact of emergencies. Finally， a multi-scale temporal awareness module was constructed， in which temporal enhancement and diffusion mechanisms were used to extract temporal features from periodic and local features， respectively. Experimental results demonstrate that compared to the best-performing baseline model ST-SSL （Spatio-Temporal Self-Supervised Learning）， STA-Autoformer reduces the Mean Absolute Error （MAE） in inflow prediction by 3.0% and 1.8%， respectively， while lowering the Mean Absolute Percentage Error （MAPE） by 5.8% and 1.4%， respectively， on the NYCBike1 and NYCBike2 datasets； on the NYCTaxi dataset， STA-Autoformer reduces the MAE in inflow and outflow predictions by 1.4% and 1.3%， respectively. It can be seen that STA-Autoformer achieves significant performance improvements across multiple real-world traffic datasets.

Key words: traffic flow prediction, intelligent transportation, spatial-temporal prediction, diffusion mechanism, self-attention mechanism