Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
[HTML][HTML] Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and developing effective road safety …
are a prerequisite for devising countermeasures and developing effective road safety …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Graph neural networks for anomaly detection in industrial Internet of Things
The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
Crossgnn: Confronting noisy multivariate time series via cross interaction refinement
Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …
development and widespread applications across various fields. Transformer-based and …
A spatial attentive and temporal dilated (SATD) GCN for skeleton‐based action recognition
Current studies have shown that the spatial‐temporal graph convolutional network (ST‐
GCN) is effective for skeleton‐based action recognition. However, for the existing ST‐GCN …
GCN) is effective for skeleton‐based action recognition. However, for the existing ST‐GCN …
Joint-bone fusion graph convolutional network for semi-supervised skeleton action recognition
In recent years, graph convolutional networks (GCNs) play an increasingly critical role in
skeleton-based human action recognition. However, most GCN-based methods still have …
skeleton-based human action recognition. However, most GCN-based methods still have …
Environment-aware dynamic graph learning for out-of-distribution generalization
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-
temporal patterns on dynamic graphs. However, existing works fail to generalize under …
temporal patterns on dynamic graphs. However, existing works fail to generalize under …
Pattern expansion and consolidation on evolving graphs for continual traffic prediction
Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …