Abstract:

. However, efficiently and comprehensively capturing the complex spatiotemporal correlations within urban traffic flow presents a key challenge. Existing research methods struggle to fully capture these spatiotemporal dependencies. To address these issues, we propose a novel end-to-end deep learning framework called the spatiotemporal multi-view attention residual network (ST-MVAR) for predicting traffic flow in urban areas. we integrate the proximity, periodicity, trend, and external factors of traffic flow as inputs to the network. This network employs skip connections to form a multi-layer nested residual network structure. Additionally, we design a Multi-View Extension module to capture spatial dependencies of traffic flow at various distances and introduce a coordinate attention network to effectively establish the spatiotemporal correlations within traffic flow. Furthermore, we use the k-means clustering method to obtain patterns for each cross-sectional time traffic flow and incorporate them as additional features to further enhance the model's predictive accuracy. Experimental results demonstrate that ST-MVAR achieves higher performance with fewer parameters, 14.2% lower RMSE compared to the best previous methods.

Key words: traffic flow prediction, residual network, view extension, coordinate attention, K-Means clustering