Spatio-Temporal Meta-Graph Learning for Traffc Forecasting, 视频播放量 186、弹幕量 0、点赞数 2、投硬币枚数 2、收藏人数 3、转发人数 2, 视频作者 机智小赛尔, 作者简介 ,相关视频:SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivaria
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 巴拉巴拉 积极向上的懒人1.创新点 第一篇将图卷积用于提取空间和时间信息的文章,没有使用正则卷积和递归单元,使用了完整的卷积结构,在更少的参数下,可以得到更快的训练速度 2.问题描述 通过前[t-m+1,t] 的交通流量...
相反,CNN是fast training的。 Temporal Gated-Conv是一个1D-Conv+GLU(gated linear units): \Gamma \in \mathbb{R}^{K_{t} \times C_{i} \times 2 C_{o}}将inputY\in \mathbb{R}^{M \times C_{i}}映射到[PQ]\in \mathbb{R}^{(M-K_t+1) \times 2C_{0}},那么最终的temporal gated-...
问题背景:交通流量预测忽略时空依赖性。 提出模型:Spatio-Temporal Graph Convolutional Networks (STGCN)。instead of 常规卷积和递归单元,本文在图上公式化问题,并使用完整的卷积结构构建模型,使得以更少的参数实现更快的训练速度。 流量预测分为:短期(5-30min),中长期(>30min)。 RNN迭代训练会累积误差,并且难训...
To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic...
加权求和，指导结构感知学习。ST-Meta Graph Reconstruction预测边可能性，指导元知识重构，引入图重建损失优化。参数生成方法，节点元知识指导模型参数生成，减少参数量，适应不同场景。ST-GFSL学习过程遵循MAML的episode学习，从源数据集中采样训练任务，优化模型适应能力，实现小样本场景的高效学习。
Spatio-TemporalGraphConvolutionalNetwork: ImprovingTrafficPrediction with Navigation Data 【混合时空图卷积网络:交通流量预测】 【要点】:将交通路网视为一个以路段为节点的图。 【补充知识】 图卷积:https://zhuanlan.zhihu.com/p/89503068 Spatio-Temporal Attention Based LSTM Networks for 3D Action Recognition ...
摘要: Contents·Prediction Problems in Traffic·Data-driven methods/Deep learning·Graph Convolution Operation·Spatio-Temporal Graph Convolutional Networks·Experiments and Results·Summary & Beyond Prediction Problems in Traffic 会议名称: 第五届交通科学与计算专题研讨会 会议地点: 贵阳 收藏...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting Introduction We propose a novel deep learning framework,STGCN, to tackle time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem...
摘要原文 Spatio-temporal modeling as a canonical task of multivariate time seriesforecasting has been a significant research topic in AI community. To addressthe underlying heterogeneity and non-stationarity implied in the graph streams,in this study, we propose Spatio-Temporal Meta-Graph Learning as ...