.png){kind=icon}
返回
Node Connection Strength Matrix-Based Graph Convolution Network for Traffic Flow Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2023.3265300.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traffic flow prediction plays an integral role in intelligent transport systems, helping to manage and control urban traffic and improving the operational efficiency of road networks. Although the current mainstream traffic flow prediction models have achieved good accuracy, they cannot effectively utilize the unique characteristics of the traffic network where the importance of a node in the traffic network is positively correlated with the traffic flow through the node. Actually, the historical traffic properties of nodes will have a great influence on the future. With this background, in this paper, we propose a node connection strength index by network representation learning to utilize the historical traffic attributes of nodes. Then, we design a graph convolution network based on the node connection strength matrix to predict the traffic flow of the node. A novel Dynamics Extractor is designed to learn the various characteristics of the traffic flow. Experimental results demonstrate that the proposed scheme has a better performance by comparison with baseline methods.
Keyword:
Graph convolution network
network representation learning
node connection strength
traffic flow prediction
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions深度学习在交通预测中的应用: 方法、分析与未来发展方向
OAAI
OAAI分享收藏Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
OAAI分享收藏Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting流量图卷积循环神经网络: 用于网络规模流量学习和预测的深度学习框架OAAI
OAAI分享收藏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
查看更多内容