Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting 技术标签: 【交通预测论文阅读】 交通预测AAAI2021的一篇文章,文章标题为:Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting。 问题: 交通速度
Graph Convolutional Neural NetworkSimilarity measureTraffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GA...
目前的方法主要利用给定的空间邻接图以及复杂的极值来对时空信息相关性进行建模,但空间邻接图的结构不完整等因素会限制这些方法的性能,所以当前的方法无法胜任复杂的时空数据 作者提出的时空融合图神经网络可以胜任任何交通流预测 首先作者使用一个数据驱动的算法生成时空图,用于不全几个存在的但空间图可能无法反映出的相关...
1. 本文亮点 使用DTW算法计算时间序列相似度,生产不同的子图,然后将多个子图和预先给定的空间领接矩阵集成成为一个时空融合图,得到隐藏的时空依赖关系。 引入门控膨胀卷积算法,并提出一种新的空间图和时间图的融合方法。 2. 现有方法的局限性 大多数现有模型仅利用给定的空间邻接矩阵进行图形建模,在对邻接矩阵建模时...
Concerning feature fusion, this approach integrates features from multiple Richly connected graph construction As previously mentioned, most fault diagnosis methods based on graph neural networks begin by constructing graph structures through establishing connections and weights between nodes based on certain ...
2) The susceptibility of the existing graph neural networks (GNNs) to oversmoothing reflects an inherent limitation of these networks. As the network layers deepen, all node representations tend to converge to a uniform value, which greatly affects the ability of the employed model to capture long...
3 Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation 链接:https://arxiv.org/abs/2403.11960 作者:Baoyu Jing,Dawei Zhou,Kan Ren,Carl Yang 关键词:插补,时空图神经网络,因果注意力 CASPER 4 ByGCN: Spatial Temporal Byroad-Aware Graph Convolution Network for Tra...
Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, Online, pp 4189–4196 Jin G, Li F, Zhang J et al (2023) Automated dilated spatio-temporal synchronous graph modeling for traffic ...
Adaptively Spatial Feature Fusion (ASFF) 张佳程发表于MCPRL 【文献阅读】Spatio-temporal fusion for remote sensing data: an overview and new benchmark 遥感时空融合:综述和新基准 鸡汤谁都会说 文献阅读:ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection 时间: 2024...
To overcome those limitations, our paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-...