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: ...
A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.FindingsThe proposed model...
ST-GRAT是动态解码式的迭代预测,而GMAN是生成式预测不存在累积误差,所以长期结果GMAN优一点也很正常。 此外此文还将一天分为多个时段对每个时段分别进行预测,可以看到ST-GRAT在每个时段都取得了sota的效果,而Graph WaveNet明显在高峰时刻的效果更好。此外此文选择了速度变化较快的区间称为Impeded Interval进行预测,ST-...
SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate 34 -- 30:23 App PreStnet 77 -- 4:44 App Rethinking Attention Mechanism for Spatio-Temporal Modeling:A Decoupling Perspec 129 -- 21:47 App BasisFormer Attention-based Time Series Forecasting 145 -- 7:13 App Di...
ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations Urban data Ⅰ 1. Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization ...
STGATP: A Spatio-Temporal Graph Attention Network for Long-Term Traffic Prediction Traffic prediction is essential to public transportation management in cities. However, long-term traffic prediction involves complex spatio-temporal corre... M Zhu,X Zhu,C Zhu - International Conference on Artificial ...
STAM: A Spatiotemporal Aggregation Method for Graph NeuralNetwork-based Recommendation STAM:一种基于图神经网络的推荐的时空聚合方法 来源:WWW 2022 摘要:现有的基于图神经网络的推荐方法通常关注于如何从空间结构信息的角度来聚合信息,但关于邻居的时间信息却没有得到充分的探索。在这项工作中,作者提出了一种时空聚...
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 ...
accommodate such network data is through graph neural networks (GNNs). These techniques build on the intuitive idea of message passing between nodes20and have recently attracted a lot of attention in the deep learning community21. Among the wide range of use cases of GNNs are node classification...
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 ...