.png){kind=icon}
返回
A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2023.109670.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate predictions of traffic flow within a road network. Traffic signals used for forecasting are usually generated by sensors along roads, which can be represented as nodes on a graph. These sensors typically produce normal signals representing normal traffic flows and abnormal signals indicating unknown traffic disruptions. Graph convolution networks are widely used for traffic prediction due to their ability to capture correlations between network nodes. However, existing approaches use a predefined or adaptive adjacency matrix that does not accurately reflect real-world relationships between signals. To address this issue, we propose a decomposition dynamic graph convolutional recurrent network (DDGCRN) for traffic forecasting. DDGCRN combines a dynamic graph convolution recurrent network with an RNN-based model that generates dynamic graphs based on time-varying traffic signals, allowing for the extraction of both spatial and temporal features. Additionally, DDGCRN separates abnormal signals from normal traffic signals and models them using a data-driven approach to further improve predictions. Results from our analysis of six real-world datasets demonstrate the superiority of DDGCRN compared to the current state-of-the-art. The source codes are available at: https://github.com/wengwenchao123/DDGCRN . (c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Traffic forecasting
Dynamic graph generation
Residual decomposition
Segmented learning
Graph convolution network
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Spectroscopic Study of Aqueous H2SO4 at Different Temperatures and Compositions: Variations in Dissociation and Optical Properties在不同温度和组成下对水溶液H2SO4 的光谱研究: 解离和光学性质的变化
PREAI
分享收藏Reverse Osmosis and Forward Osmosis for the Concentration of Fruit Juices用于浓缩果汁的反渗透和正渗透.png)
Fruit Juices
Fruit JuicesIF0
PREAI
分享收藏Pedestrian trajectory prediction with convolutional neural networks基于卷积神经网络的行人轨迹预测PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏分享收藏
查看更多内容