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...
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。
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...
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。
Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the ...
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...
Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion a... D Xia,B Shen,J Geng,... - 《Neur...
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 Artificial Intelligence (AAAI'19) 2019. Datasets We validate our model on two highway traffic datasets PeMSD4 ...
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 Artificial Intelligence (AAAI'19) 2019. Datasets We validate our model on two highway traffic datasets PeMSD4 ...
首先现有的方法大多针对grid-based和point-based问题,忽略了segment-level的流量预测。其次GCN比较依赖于Laplace矩阵,通常输入图的邻接矩阵是固定的,而实际上道路graph通常具有时变特性,且过去的研究基本都使用地理距离来表达邻接矩阵,实际上地理上的距离并不能很好的体现位置之间的空间相关性。