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Traffic flow prediction is an important part of intelligent transportation system, which can provide important support for traffic management optimization, resource scheduling and urban planning. At its core, it uses historical and real-time data to predict future traffic conditions. The main challenge of traffic flow forecasting is how to effectively model complex spatio-temporal dependencies in traffic data. However, with the continuous development of traffic forecasting technology, traffic forecasting models still have obvious shortcomings in dynamic spatial dependence and periodicity modeling, which limits their forecasting effect. Therefore, this paper proposes an improved traffic flow prediction model DASTFormer (Dynamic Adaptive Spatio-Temporal Transformer). Firstly, an adaptive adjacency matrix is added to the embedding layer to enable the model to dynamically adjust the relationship weights between nodes, thereby enhancing the adaptability of the model to the spatial dependence of real-time changes. Secondly, an Adaptive Weighted Adapter is designed and combined with semantic spatial attention to further optimize the model’s semantic dependency modeling in complex traffic scenarios. Finally, adding the Auto-Correlation and then combining the self-attention mechanism into the time prediction module can effectively capture the periodic features in the data, and also improve the modeling ability of the model for complex temporal relationships, thus further enhancing the accuracy and stability of the prediction. Experimental results show that the proposed method achieves better prediction accuracy and robustness on multiple traffic datasets.
Wu et al. (Sun,) studied this question.