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 …
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 …
A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
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 …
Trafformer: unify time and space in traffic prediction
Traffic prediction is an important component of the intelligent transportation system. Existing
deep learning methods encode temporal information and spatial information separately or …
deep learning methods encode temporal information and spatial information separately or …
Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction
Deep learning models have emerged as a promising way for traffic prediction. However, the
requirement for large amounts of training data remains a significant issue for achieving well …
requirement for large amounts of training data remains a significant issue for achieving well …