  Real-time and precise traffic flow prediction is vital for the efficiency of
intelligent transportation systems. Traditional methods often employ graph
neural networks (GNNs) with predefined graphs to describe spatial correlations
among traffic nodes in urban road networks. However, these pre-defined graphs
are limited by existing knowledge and graph generation methodologies, offering
an incomplete picture of spatial correlations. While time-varying graphs based
on data-driven learning have attempted to address these limitations, they still
struggle with adequately capturing the inherent spatial correlations in traffic
data. Moreover, most current methods for capturing dynamic temporal
correlations rely on a unified calculation scheme using a temporal multi-head
self-attention mechanism, which at some level might leads to inaccuracies. In
order to overcome these challenges, we have proposed a novel hybrid
time-varying graph neural network (HTVGNN) for traffic flow prediction.
Firstly, a novel enhanced temporal perception multi-head self-attention
mechanism based on time-varying mask enhancement was reported to more
accurately model the dynamic temporal dependencies among distinct traffic nodes
in the traffic network. Secondly, we have proposed a novel graph learning
strategy to concurrently learn both static and dynamic spatial associations
between different traffic nodes in road networks. Meanwhile, in order to
enhance the learning ability of time-varying graphs, a coupled graph learning
mechanism was designed to couple the graphs learned at each time step. Finally,
the effectiveness of the proposed method HTVGNN was demonstrated with four real
data sets. Simulation results revealed that HTVGNN achieves superior prediction
accuracy compared to the state of the art spatio-temporal graph neural network
models. Additionally, the ablation experiment verifies that the coupled graph
learning mechanism can effectively improve the long-term prediction performance
of HTVGNN.
