GraphSAGE-based dynamic spatial–temporal graph convolutional network for traffic prediction
T Liu, A Jiang, J Zhou, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …
such dependencies is critical to improving prediction accuracy. Recently, many deep …
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 …
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 …
Mfdgcn: Multi-stage spatio-temporal fusion diffusion graph convolutional network for traffic prediction
Z Cui, J Zhang, G Noh, HJ Park - Applied Sciences, 2022 - mdpi.com
Traffic prediction is a popular research topic in the field of Intelligent Transportation System
(ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve …
(ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve …
Dstgcn: Dynamic spatial-temporal graph convolutional network for traffic prediction
J Hu, X Lin, C Wang - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Traffic prediction is an important part of building a smart city. Reasonable traffic prediction
can help the relevant departments to make important decisions and help people to plan their …
can help the relevant departments to make important decisions and help people to plan their …
Multi-graph fusion based graph convolutional networks for traffic prediction
Traffic prediction is significant for transportation management and travel route planning, and
it is challenging as the spatial dependencies are complex and temporal patterns are …
it is challenging as the spatial dependencies are complex and temporal patterns are …
PGCN: Progressive graph convolutional networks for spatial–temporal traffic forecasting
The complex spatial-temporal correlations in transportation networks make the traffic
forecasting problem challenging. Since transportation system inherently possesses graph …
forecasting problem challenging. Since transportation system inherently possesses graph …
DSTAGCN: Dynamic spatial-temporal adjacent graph convolutional network for traffic forecasting
Q Zheng, Y Zhang - IEEE Transactions on Big Data, 2022 - ieeexplore.ieee.org
Capturing complex and dynamic spatial-temporal dependencies of traffic data is of great
importance for accurate and real-time traffic forecasting in intelligent transportation systems …
importance for accurate and real-time traffic forecasting in intelligent transportation systems …
T-GCN: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and traffic …
Automated dilated spatio-temporal synchronous graph modeling for traffic prediction
Accurate traffic prediction is a challenging task in intelligent transportation systems because
of the complex spatio-temporal dependencies in transportation networks. Many existing …
of the complex spatio-temporal dependencies in transportation networks. Many existing …