Graph neural network for traffic forecasting: A survey
W Jiang, J Luo - Expert Systems with Applications, 2022 - Elsevier
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 …
Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues
Traffic forecasting plays an important role of modern Intelligent Transportation Systems (ITS).
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have …
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have …
Multi-range attentive bicomponent graph convolutional network for traffic forecasting
Traffic forecasting is of great importance to transportation management and public safety,
and very challenging due to the complicated spatial-temporal dependency and essential …
and very challenging due to the complicated spatial-temporal dependency and essential …
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 …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
Adaptive spatial-temporal graph attention networks for traffic flow forecasting
Traffic flow forecasting, which requires modelling involuted spatial and temporal
dependence and uncertainty regarding road networks and traffic conditions, is a challenge …
dependence and uncertainty regarding road networks and traffic conditions, is a challenge …
Hierarchical graph convolution network for traffic forecasting
Traffic forecasting is attracting considerable interest due to its widespread application in
intelligent transportation systems. Given the complex and dynamic traffic data, many …
intelligent transportation systems. Given the complex and dynamic traffic data, many …
Spatio-temporal graph structure learning for traffic forecasting
As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting
inherently subjects to the following three challenging aspects. First, traffic data are physically …
inherently subjects to the following three challenging aspects. First, traffic data are physically …
Gated residual recurrent graph neural networks for traffic prediction
Traffic prediction is of great importance to traffic management and public safety, and very
challenging as it is affected by many complex factors, such as spatial dependency of …
challenging as it is affected by many complex factors, such as spatial dependency of …
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Dynamic Graph Neural Networks (DGNNs) have become one of the most promising
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …