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Found 5 papers, 2 papers with code
Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction
A sequence-aware graph neural network is proposed and integrated into the Gated Recurrent Unit (GRU) to learn non-fixed graphs at different time steps and capture local temporal relationships.
Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction
Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems.
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
However, a survey study of graph learning, spatial-temporal graph models for traffic, as well as a fair comparison of baseline models are pending and unavoidable issues.
Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
The key challenge is to effectively model complex spatial-temporal dependencies and correlations in modern traffic data.
Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Accurate and real-time traffic state prediction is of great practical importance for urban traffic control and web mapping services.
