  Traffic forecasting is a core element of intelligent traffic monitoring
system. Approaches based on graph neural networks have been widely used in this
task to effectively capture spatial and temporal dependencies of road networks.
However, these approaches can not effectively define the complicated network
topology. Besides, their cascade network structures have limitations in
transmitting distinct features in the time and space dimensions. In this paper,
we propose a Multi-adaptive Spatiotemporal-flow Graph Neural Network (MAF-GNN)
for traffic speed forecasting. MAF-GNN introduces an effective Multi-adaptive
Adjacency Matrices Mechanism to capture multiple latent spatial dependencies
between traffic nodes. Additionally, we propose Spatiotemporal-flow Modules
aiming to further enhance feature propagation in both time and space
dimensions. MAF-GNN achieves better performance than other models on two
real-world datasets of public traffic network, METR-LA and PeMS-Bay,
demonstrating the effectiveness of the proposed approach.
