Abstract
Intelligent Transportation Systems (ITSs) have become pivotal in urban traffic management by utilizing traffic flow prediction, which aids in alleviating congestion and facilitating route planning. This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. LASTGCN incorporates a Multifactor Fusion Unit (MFF-unit) to dynamically integrate meteorological factors, an advanced multi-graph convolutional network for spatial correlations, and the Receptance Weighted Key Value (RWKV) block, which employs a linear attention mechanism for efficient processing of historical traffic data.The model achieves computational efficiency by using RWKV, which offers advantages over Transformer-based models in handling large-scale data while capturing complex dependencies. The model is designed to achieve computational efficiency, making it suitable for mid-term traffic management scenarios and potentially adaptable to real-time applications with further optimization. Experimental results using real-world highway traffic datasets indicate that LASTGCN outperforms several state-of-the-art methods in terms of accuracy and robustness, especially in long-term predictions. Additionally, integrating external factors such as weather conditions was found to significantly enhance the model's predictive accuracy.