Stnn: A spatial-temporal graph neural network for traffic prediction
Accurate traffic prediction is of great importance in Intelligent Transportation System. This
problem is very challenging due to the complex spatial and long-range temporal …
problem is very challenging due to the complex spatial and long-range temporal …
SAST-GNN: a self-attention based spatio-temporal graph neural network for traffic prediction
Y Xie, Y Xiong, Y Zhu - Database Systems for Advanced Applications: 25th …, 2020 - Springer
Traffic prediction, which aims at predicting future traffic conditions based on historical
observations, is of considerable significance in urban management. However, such tasks …
observations, is of considerable significance in urban management. However, such tasks …
Spatio-temporal attention-based graph convolution networks for traffic prediction
Y Chen, L Zheng, W Liu - 2022 IEEE International Conference …, 2022 - ieeexplore.ieee.org
Accurate traffic prediction is critical to the effectiveness of intelligent transportation systems.
However, traffic data are highly nonlinear with complicated dynamic spatio-temporal …
However, traffic data are highly nonlinear with complicated dynamic spatio-temporal …
ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction
Accurately predicting traffic flow characteristics is crucial for effective urban transportation
management. Emergence of artificial intelligence has led to the surge of deep learning …
management. Emergence of artificial intelligence has led to the surge of deep learning …
Multi-stage attention spatial-temporal graph networks for traffic prediction
Accurate traffic prediction plays an important role in Intelligent Transportation System. This
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
Dual dynamic spatial-temporal graph convolution network for traffic prediction
Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
Multicomponent Spatial‐Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data
Predicting traffic data on traffic networks is essential to transportation management. It is a
challenging task due to the complicated spatial‐temporal dependency. The latest studies …
challenging task due to the complicated spatial‐temporal dependency. The latest studies …
ISTGCN: Integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network
To effectively estimate traffic patterns, spatial-temporal information must consider the
complex spatial connections on road networks and time-dependent traffic information …
complex spatial connections on road networks and time-dependent traffic information …
Adaptive graph neural network for traffic flow prediction considering time variation
F Chen, X Sun, Y Wang, Z Xu, W Ma - Expert Systems with Applications, 2024 - Elsevier
Traffic prediction has drawn considerable attention due to its potential to optimize the
operational efficiency of road networks. Existing methods commonly combine graph neural …
operational efficiency of road networks. Existing methods commonly combine graph neural …
Time-adaptive graph convolutional network for traffic prediction
G Liu, Y Wu, D Zhao, H Zhou - Proceedings of the 2021 5th International …, 2021 - dl.acm.org
Traffic prediction is of great significance to route planning and transportation management.
Due to the complex nonlinear spatiotemporal dependence between traffic data and various …
Due to the complex nonlinear spatiotemporal dependence between traffic data and various …