Spatiotemporal dataTraffic prediction? 2024Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge o
引入特征融合模块,特征融合模块形成连接层,后面1×1卷积、BN、relu。 temporal inception:两个分支,一个分支直接将连续帧中相同关节特征作为位置特征处理的输入,另一个分支将输入馈入运动采样模块进行运动特征处理,这是关节运动特征首次用于基于骨骼的动作识别中。 运动采样:设计运动采样模块建模二阶空间信息 。运动的矢量...
Subsequently, it integrates graph convolutional neural networks (GCNs) with long short-term memory (LSTM) networks to capture the spatiotemporal characteristics of traffic data. Additionally, a GCN is designed to capture the spatial topological relationships of the road network. Finally, a novel ...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 论文笔记,程序员大本营,技术文章内容聚合第一站。
Eachindicates a frame of current traffic status at time stept, which is recorded in a graph-structured data matrix. Network Structure Fig. 2 Architecture of spatio-temporal graph convolutional networks. The framework STGCN consists of two spatio-temporal convolutional blocks (ST-Conv blocks) and ...
最近,我在找寻关于时空序列数据(Spatio-temporal sequential data)的预测模型。偶然间,寻获论文Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting,甚喜!因此想基于这个模型,改为我所用。但是,我查询了网上的很多关于 STGCN 的解析,发现都不够详细,很多关键的细节部分一笔...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 论文笔记---GCN在交通领域的应用(二) 一、论文翻译: 1、摘要: 及时准确地交通预测对于城市交通控制和指导具有至关重要的意义。由于交通流量的非线性和复杂性,传统的方法不能满足中长期预测任务的需求,并且往往会忽略时...
the paper "Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting" 文章全部内容+对应ppt请查看:STGCN-keras 问题定义 如何准确的进行中长期的交通预测(中长期:over 30 minutes) 本篇论文主要是对地点的速度进行预测 ...
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Deep graph convolutional networks for wind speed prediction (2021) arXiv preprint arXiv:2101.10041 Google Scholar [47] Wang Fei, Chen Peng, Zhen Zhao, Yin Rui, Cao Chunmei, Zhang Yagang, Duić Neven Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-...