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 …
The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
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 …
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 …
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
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 …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …
to the time-varying traffic patterns and the complicated spatial dependencies on road …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …