While node attributes, which record valuable information of traffic conditions, have not been fully exploited to guide the learning of better graph structure. In this paper, we propose an Adaptive Spatio-Tempora
However, a single attention mechanism cannot simultaneously capture the spatiotemporal attributes of traffic data, nor does it account for the influence of the urban traffic network’s spatial topology on the spatiotemporal correlations in traffic data. Graph convolutional neural network Traditional CNN ...
A recent, state-of-the-art approach is based on spatio-temporal transformer network, which has extended the lead time for ENSO predition to 18 months. Even with the recent advances in applications with deep learning, the predictability at long lead times of up to 19 months and beyond is ...
In this paper, the accuracy of the model’s recognition is improved by combining two approaches, namely adaptive graphs and Transformers, to focus more on the spatiotemporal information of the skeletal structure. Firstly, an adaptive graph method is employed to capture the connectivity relationships ...
et al. PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 1–9 (2019). Article Google Scholar Sunkin, S. M. et al. Allen Brain Atlas: An integrated spatio-temporal portal for exploring the central ...
tool wear prediction; spatial–temporal graph neural network; attention mechanism; multi-sensor fusion1. Introduction The cutting tools are the key factors for achieving efficient and high-quality processing, as the condition of the tool directly affects the cutting performance, processing accuracy, and...
2.3. Spatio-Temporal Graph Neural Networks In recent years, Graph Neural Networks (GNNs) have been widely used in traffic prediction, utilizing graph theory to filter signals on local subgraphs. The graph convolution in GNN models combines the central node representation with neighboring node represent...
reporting out of Jinan, People's Republic ofChina, by NewsRx editors, research stated, "This study addresses the complex challenges associated withroad traffic flow prediction and congestion management through the enhancement of the attention-basedspatiotemporal graph convolutional network (ASTGCN) ...
标题论文标题:Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting 摘要 城市交通流预测是智能交通系统中的一个关键问题。由于动态城市交通条件所带来的复杂的时空依赖性和本质的不确定性,这是一个相当具有挑战性的问题。在现有的大多数方法中,基于局部空间邻近性,利用图神经网络(...
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition 时空初始图卷积网络用于基于骨骼的动作识别 CVPR2020 STIGCN 邻接矩阵的拓扑是建模输入骨骼相关性的关键因素。先前方法主要集中于图拓扑的设计/学习。但是一旦了解了拓扑,网络的每一层中将仅存在一个单比例特征...【...