Spatio-Temporal Traffic Prediction Under Multi-Scale Dynamic Convolution

Traffic flow prediction remains a challenging task due to the complexity of spatio-temporal dependencies and the dynamic nature of traffic patterns. Traditional methods often struggle to efficiently model long-term dependencies and adapt to dynamic changes in traffic networks, leading to suboptimal prediction performance. To address these challenges, this paper proposes a novel traffic flow prediction model that integrates multi-scale convolutional networks and dynamic graph convolutional networks. By replacing the traditional Transformer architecture with Linformer, the model significantly reduces computational complexity while efficiently processing long sequential data. Additionally, the introduction of multi-scale convolutional networks allows for effective extraction of flow features at short, medium, and long time scales, enhancing prediction accuracy across different horizons. The incorporation of Dynamic Graph Convolutional Networks (Dynamic GCN) enables dynamic adjustment of the adjacency matrix, allowing the model to adaptively capture changes in spatio-temporal relationships within the traffic network. Experimental results show that the proposed model outperforms existing approaches in short-, medium-, and long-term traffic flow prediction tasks across multiple datasets, demonstrating its effectiveness in addressing the complexities of spatio-temporal dependencies and dynamic changes.