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Found 5 papers, 1 papers with code
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)
In HUGAT, heterogeneous urban graph (HUG) incorporates both the geo-spatial and temporal people movement variations in a single graph structure.
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting
In this study, we propose a novel traffic forecasting framework called Progressive Graph Convolutional Network (PGCN).
A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting
Then, the spatial feature extraction layers in the models were substituted with graph convolution and graph attention.
Short-term Traffic Prediction with Deep Neural Networks: A Survey
2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks.
Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting
The output of multi-weight graph convolution is applied to the sequence-to-sequence model with Long Short-Term Memory units to learn temporal dependencies.
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