1)Spatial attention 2)Temporal attention 在时间维度上,不同时间片上的交通状况之间存在相关性,且在不同情况下其相关性也不同。 Spatial-Temporal Convolution 时空关注模块让网络自动对有价值的信息给予相对更多的关注。本文提出的时空卷积模块包括空间维度上的图卷积,从邻近时间捕获空间相关性,以及沿时间维度上的卷积...
GRU: Gated Recurrent Unit network STGCN: spatial-temporal graph convolution model based on the spatial method GLU-STGCN: A graph convolution network with a gating mechanism GeoMAN: A multi-level attention-based recurrent neural network model 实验结果 从表1可以看出,就所有评估指标而言,ASTGCN在两个数...
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting,程序员大本营,技术文章内容聚合第一站。
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》,程序员大本营,技术文章内容聚合第一站。
ASTGCN Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) References Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan(*). Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. The 33rd AAAI Conference on...
该文章提出了attention based spatial-temporal graph convolutional network (ASTGCN) model来进行交通流的预测 交通数据网络: 为什么说交通数据网络是空间时序问题?: 图(a) 两点间的连线代表其互相影响强度(mutual influence strength),颜色越深代表影响越大*交通流的预测除了时序和空间关系之外,还与路网结构、车速等相...
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) This is a Pytorch implementation of ASTGCN and MSTCGN. The pytorch version of ASTGCN released here only consists of the recent component, since the other two components have the same network architecture...
In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic...
Therefore, we propose the Spatio-Temporal Dependence Learning Network (STDL-Net), which is an attention-based network for MTS with explicit spatial dependencies. The STDL-Net first simultaneously extracts spatial dependencies and models short-term temporal dependencies via a sparse convolutional neural ...
Therefore, we propose the Spatio-Temporal Dependence Learning Network (STDL-Net), which is an attention-based network for MTS with explicit spatial dependencies. The STDL-Net first simultaneously extracts spatial dependencies and models short-term temporal dependencies via a sparse convolutional neural ...