DCENet: A dynamic correlation evolve network for short-term traffic prediction
Graph neural networks (GNNs) have been extensively employed in traffic prediction tasks
due to their excellent capturing capabilities of spatial dependence. However, the majority of …
due to their excellent capturing capabilities of spatial dependence. However, the majority of …
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 …
[PDF][PDF] LSGCN: Long short-term traffic prediction with graph convolutional networks.
Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …
applications such as intelligent route planning, dynamic traffic management, and smart …
Graph dropout self-learning hierarchical graph convolution network for traffic prediction
Q Ni, W Peng, Y Zhu, R Ye - Engineering Applications of Artificial …, 2023 - Elsevier
Traffic prediction is a challenging topic in urban traffic construction and management due to
its complex dynamic spatial–temporal correlations. Currently, graph neural network …
its complex dynamic spatial–temporal correlations. Currently, graph neural network …
Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
Traffic forecasting is highly challenging due to its complex spatial and temporal
dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been …
dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been …
Adaptive graph convolutional recurrent network for traffic forecasting
Modeling complex spatial and temporal correlations in the correlated time series data is
indispensable for understanding the traffic dynamics and predicting the future status of an …
indispensable for understanding the traffic dynamics and predicting the future status of an …
PKET-GCN: prior knowledge enhanced time-varying graph convolution network for traffic flow prediction
Due to prediction on the traffic flow is influenced by the real environment and historical data,
the produced traffic graph may include significant uncertainty. The graph convolution …
the produced traffic graph may include significant uncertainty. The graph convolution …
An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction
R Xue, S Zhao, F Han - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Traffic prediction is an important part of modern intelligent transportation systems (ITS),
which helps transportation management and city planning. However, it is a very challenging …
which helps transportation management and city planning. However, it is a very challenging …
Spatio-temporal Fourier enhanced heterogeneous graph learning for traffic forecasting
W Zhang, H Wang, F Zhang - Expert Systems with Applications, 2024 - Elsevier
Traffic flow prediction is of paramount importance in the field of spatio-temporal forecasting.
In recent years, research efforts have primarily been directed towards developing intricate …
In recent years, research efforts have primarily been directed towards developing intricate …
STFGCN: Spatial–temporal fusion graph convolutional network for traffic prediction
H Li, J Liu, S Han, J Zhou, T Zhang… - Expert Systems with …, 2024 - Elsevier
Accurate traffic prediction plays a crucial role in improving traffic conditions and optimizing
road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic …
road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic …