A novel hybrid time-varying graph neural network for traffic flow forecasting
CoRR(2024)
摘要
Real-time and accurate traffic flow prediction is the foundation for ensuring
the efficient operation of intelligent transportation systems.In existing
traffic flow prediction methods based on graph neural networks (GNNs),
pre-defined graphs were usually used to describe the spatial correlations of
different traffic nodes in urban road networks. However, the ability of
pre-defined graphs used to describe spatial correlation was limited by prior
knowledge and graph generation methods. Although time-varying graphs based on
data-driven learning can partially overcome the drawbacks of pre-defined
graphs, the learning ability of existing adaptive graphs was limited. For
example, time-varying graphs cannot adequately capture the inherent spatial
correlations in traffic flow data.In order to solve these problems, we have
proposed a hybrid time-varying graph neural network (HTVGNN) for traffic flow
prediction.
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