消除传播延迟：基于注意力的时空融合图卷积网络用于交通流预测
Eliminating Propagation Delay: Attention-Based Spatial-Temporal Fusion Graph Convolution Network for Traffic Flow Prediction
- School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（北京邮电大学计算机学院（国家示范性软件学院））
- Department of Automation, School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院自动化系）
- School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
- Department of Automation, Tsinghua University（清华大学自动化系）
- College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对交通流预测，提出基于注意力的时空融合图卷积网络（A-STFGCN），设计时空融合块，通过掩码矩阵多头自注意力机制提取特征相关性，去除传播延迟误差，实验表明该方法相比基线方法性能最佳，效率良好。
AI中文摘要：
预测交通流对于优化交通系统和改善城市机动性至关重要。许多基于图卷积的模型已被提出用于提取时空特征和预测交通流。然而，大多数模型关注拓扑关系中的时空和语义相关性。存在两个主要问题需要解决。首先，模型中的卷积结构侧重于利用拓扑结构中的静态空间依赖性和时空关系，而忽略了卷积中相邻节点之间不同的信息传播延迟。其次，这些方法通常堆叠大量复杂结构，导致模型训练阶段的计算时间大幅增加，从而忽视了模型对及时性的要求。在本文中，我们提出了一种名为基于注意力的时空融合图卷积网络（A-STFGCN）的新型网络。我们设计了一个时空融合块，以去除传播延迟误差来提取时空特征相关性，并在基于掩码矩阵的多头自注意力机制内捕获数据的长期和短期时间特征。在五个真实世界数据集上的大量实验表明，与八种基线方法相比，我们的方法在具有良好的计算和数据利用效率的同时，实现了最佳的整体性能。
英文摘要：
Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility. Many graph convolution-based models have been proposed to extract spatial-temporal features and predict traffic flow. However, most focus on spatial-temporal and semantic correlation in topological relationships. There are two primary problems to address. Firstly, the convolutional structure in the model focuses on utilizing static spatial dependencies and spatial-temporal relationships in topological structures, while neglecting the different information propagation delays between adjacent nodes in the convolution. Secondly, these methods often stack a large number of complex structures, resulting in a substantial increase in computational time during the model training phase, thereby disregarding the model's requirements for timeliness. In this paper, we propose a novel network called the Attention-Based Spatial-Temporal Fusion Graph Convolution Network (A-STFGCN). We design a spatial-temporal fusion block to extract the spatial-temporal feature correlations with propagation delay errors removed and to capture both long-term and short-term temporal characteristics of the data within a multi-head self-attention mechanism based on a mask matrix. Extensive experiments on five real-world datasets demonstrate that our method achieves the best overall performance while having good computation and data utilization efficiency compared with the eight baseline methods.
