.png){kind=icon}
返回
Temporal-aware structure-semantic-coupled graph network for traffic forecasting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2024.102339.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The spatial-temporal graph neural networks have been a critical approach to capturing the complicated spatial- temporal dependencies inherent in traffic series for more accurate forecasting. However, the issue of graph indistinguishability demands further attention, as graphs learned by existing methods tend to converge to implicit and indistinguishable representations, deviating from the genuine distribution. This issue can be attributed to the lack of three primary factors within graphs: the intrinsic graph features, the temporaldistinct features, and the node -distinct features. Aiming to address this problem, we propose a Temporal -Aware Structure -Semantic -Coupled Graph Network (TASSGN) in this paper. Firstly, we design a novel graph learning block to simultaneously learn the structural and semantic aspects of graphs, thereby capturing inherent graph features. Secondly, we propose an innovative Self -Sampling method to sample the relevant history series and present a Temporal -Aware Graphs Encoder to explicitly incorporate temporal information into graph learning and capture temporal -distinct features. Thirdly, sparse graphs are intentionally generated to capture nodedistinct features. By combining these three key components together, our method is capable of overcoming the problem of graph indistinguishability and achieving state-of-the-art performances in traffic forecasting.
Keyword:
Traffic forecasting
Spatial-temporal graph neural networks
Graph indistinguishability
Temporal-aware graphs
期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
机构
引用论文
Knowledge, Skills, and Abilities in the Precision Agriculture Workforce: An Industry Survey精准农业劳动力的知识、技能和能力: 行业调查
OAAI
OAAI分享收藏Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting基于时空数据嵌入的动态图卷积网络交通流预测
PREAI
分享收藏Generic Dynamic Graph Convolutional Network for traffic flow forecasting用于交通流预测的通用动态图卷积网络.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏
查看更多内容