A multi-directional recurrent graph convolutional network model for reconstructing traffic spatiotemporal diagram
ABSTRACT The Time Space Diagram (TSD) can abstractly represent multiple data sources
and the macroscopic state of road traffic. However, the TSDs may be incomplete due to …
and the macroscopic state of road traffic. However, the TSDs may be incomplete due to …
Multi-view spatial–temporal graph neural network for traffic prediction
H Li, D Jin, XJ Li, HJ Huang, JP Yun… - The Computer …, 2023 - academic.oup.com
Spatial–temporal graph neural network has drawn more and more attention in recent years
and is widely used to various real-world applications. However, learning the spatial …
and is widely used to various real-world applications. However, learning the spatial …
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 …
Traffic flow prediction via spatial temporal graph neural network
Traffic flow analysis, prediction and management are keystones for building smart cities in
the new era. With the help of deep neural networks and big traffic data, we can better …
the new era. With the help of deep neural networks and big traffic data, we can better …
STN-GCN: Spatial and Temporal Normalization Graph Convolutional Neural Networks for Traffic Flow Forecasting
C Wang, L Wang, S Wei, Y Sun, B Liu, L Yan - Electronics, 2023 - mdpi.com
In recent years, traffic forecasting has gradually become a core component of smart cities.
Due to the complex spatial-temporal correlation of traffic data, traffic flow prediction is highly …
Due to the complex spatial-temporal correlation of traffic data, traffic flow prediction is highly …
Spatial–temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
Q Li, X Yang, Y Wang, Y Wu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers take
control measures and drivers to choose the optimal travel routes. Recently, graph …
control measures and drivers to choose the optimal travel routes. Recently, graph …
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 …
Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting
S Lee, C Park - arXiv preprint arXiv:2406.02726, 2024 - arxiv.org
Accurate traffic flow forecasting is a crucial research topic in transportation management.
However, it is a challenging problem due to rapidly changing traffic conditions, high …
However, it is a challenging problem due to rapidly changing traffic conditions, high …
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 …
GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling
Q Wang, W Liu, X Wang, X Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic flow prediction significantly affects the intelligent transportation for digitized urban
transportation management and urban traffic control. Considering the complexity and strong …
transportation management and urban traffic control. Considering the complexity and strong …