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...
In recent years, spatial-temporal graph modeling based on graph convolutional neural networks (GCN) has become an effective method for mining spatial-temporal dependencies in traffic forecasting research. However, existing studies lack the capability of dynamic spatial-temporal modeling of traffic speeds....
BasedSpatial-TemporalGraphConvolutionalNetworksforTrafficFlowForecasting– github链接...之间的空间依赖性是动态的;(2)时间依赖性遵循每日和每周的模式,但它的动态时间周期并不是严格的周期性的。为了解决这两个问题,本文提出了一种新的时空动态网络Spatial-TemporalDynamic ...
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...
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow,...
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。
spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper uses attention mechanisms to unify temporal and spatial continuity and aggregate node neighbor information in multiple directions. Furthermore, a ...
dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) ...
论文《STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning》来自 Arxiv 2024。这篇论文讨论情景多智能体强化学习(Episodic Multi-agent Reinforcement Learning)中的信用分配问题。情景强化学习是指只有当智能体序列终止时才能获得非零奖励,也就是奖励稀疏场景。因此信用分配问题就需要考虑,...
STA:Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification(AAAI2019) 注意力机制对于视频行人重识别的研究越来越得到很多人的关注,同时因为时序特征也是非常重要的一部分,很多方法开始考虑两部分的结合。但是本文采用一个序列中随机选择4张图片就表示利用了时序信息还是有待商榷,感觉更像是基...