warp公式如下(p代表一个2维位置(x,y)): aggregation后得到的结果: 上式中的w是一个自适应权值,由文中提出的spatial-temporal attention机制决定。 spatial attention使用余弦相似度来计算: temporal attention和大多数论文中的都差不多: 【AAAI-2019】论文速读——交通领域 Based Spatial-Temporal Graph Convolutiona...
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。
原文:(PDF) Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 现有的交通流预测方法大多缺乏对交通数据的动态时空相关性进行建模的能力,因此无法得到令人满意的预测结果。因此这篇文章提出了一种新的基于注意力的时空图卷积网络(Attention Based Spatial-Temporal Graph Convolutiona...
Zhao, ChongHefei University of TechnologySpringer, ChamGuo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), ...
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) AAAI 2019 - wanhuaiyu/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 Artifici...
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...
Name Last commit message Last commit date Latest commit Cannot retrieve latest commit at this time. History 6 Commits .idea configurations fig lib model paper README.md prepareData.py train_ASTGCN_r.py train_MSTGCN_r.py ASTGCN Attention Based Spatial-Temporal Graph Convolutional Networks for Traf...
of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on ...
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。 Preliminaries: 1、作者定...