Zhang Yanping, Xu Wenjin, Ma Benjiang, Zhang Dan, Zeng Fanli, Yao Jiayu, Yang Hongning, Du Zhenzhen
School of Computer and Information Engineering, Qilu Institute of Technology, Jinan, 250299, China.
Qingdao University of Science and Technology, Qingdao, 266061, China.
Sci Rep. 2025 Mar 10;15(1):8249. doi: 10.1038/s41598-025-93179-y.
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.
智能交通系统（ITSs）通过利用交通流预测在城市交通管理中发挥着关键作用，这有助于缓解拥堵并促进路线规划。本研究介绍了基于线性注意力的时空多图卷积神经网络（LASTGCN），这是一种专为交通流预测量身定制的新型深度学习模型。LASTGCN包含一个多因素融合单元（MFF单元），用于动态整合气象因素；一个用于空间相关性的先进多图卷积网络；以及接受加权键值（RWKV）块，该块采用线性注意力机制来高效处理历史交通数据。该模型通过使用RWKV实现了计算效率，RWKV在处理大规模数据并捕捉复杂依赖关系方面比基于Transformer的模型具有优势。该模型旨在实现计算效率，使其适用于中期交通管理场景，并通过进一步优化可能适用于实时应用。使用真实世界高速公路交通数据集的实验结果表明，LASTGCN在准确性和鲁棒性方面优于几种先进方法，尤其是在长期预测中。此外，发现整合天气条件等外部因素可显著提高模型的预测准确性。
