Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and developing effective road safety …

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 …

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 …

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 …

Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …

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 …

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 …

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-
temporal patterns on dynamic graphs. However, existing works fail to generalize under …

Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …