To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.Design/methodology/approachThis paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks...
Motivation: 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: ...
我们认为TGConv具有通用性,可以应用于其他任务,并将其留待进一步研究。 3.5 Spatio-Temporal Graph Transformer 时间transformer可以单独模拟每个行人的运动动力学,但不能考虑空间交互作用;spatial Transformer利用TGConv处理人群交互,但很难推广到时间序列。行人预测的一个主要挑战是建模耦合时空交互作用。行人的空间和时间...
Each space–time block is composed of two graph attention networks and a gated recurrent unit, which are used to extract the spatial and temporal characteristics of road traffic flow respectively, while adding residual connections to prevent the gradient from disappearing. Then, with the traffic ...
Each space–time block is composed of two graph attention networks and a gated recurrent unit, which are used to extract the spatial and temporal characteristics of road traffic flow respectively, while adding residual connections to prevent the gradient from disappearing. Then, with the traffic ...
《Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting》,程序员大本营,技术文章内容聚合第一站。
In light of the positive results presented in this work, as well as its present limitations, we can foresee several research avenues to improve spatio-temporal models further. First, using time-varying, as opposed to static networks, either through auto-regressive or recurrent architectures, would...
To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multi-head ...
在时间维度上,对每个行人单独考虑,应用temporal Transformer抽取时许相关性; 即使是时许上的Transformer,也提供了比RNN更好的表现; 在空间维度上,引入TGConv--Transformer-based message passing graph convolution mechanism。相较于传统的图卷积抽取行人之间的交互关系,采用TGConv在高人群密度、复杂交互关系的情形下能...
ST-Meta Graph Reconstruction进一步设计用于通过重建不同城市的结构关系来进行结构感知元训练。 ST-GFSL 的端到端学习过程遵循基于MAML的episode learning。通过模拟目标城市的小样本场景,对批量的小样本训练任务进行采样,得到适应性强的基础模型。 Spatio-Temporal Neural Network ...