  This paper studies the problem of traffic flow forecasting, which aims to
predict future traffic conditions on the basis of road networks and traffic
conditions in the past. The problem is typically solved by modeling complex
spatio-temporal correlations in traffic data using spatio-temporal graph neural
networks (GNNs). However, the performance of these methods is still far from
satisfactory since GNNs usually have limited representation capacity when it
comes to complex traffic networks. Graphs, by nature, fall short in capturing
non-pairwise relations. Even worse, existing methods follow the paradigm of
message passing that aggregates neighborhood information linearly, which fails
to capture complicated spatio-temporal high-order interactions. To tackle these
issues, in this paper, we propose a novel model named Dynamic Hypergraph
Structure Learning (DyHSL) for traffic flow prediction. To learn non-pairwise
relationships, our DyHSL extracts hypergraph structural information to model
dynamics in the traffic networks, and updates each node representation by
aggregating messages from its associated hyperedges. Additionally, to capture
high-order spatio-temporal relations in the road network, we introduce an
interactive graph convolution block, which further models the neighborhood
interaction for each node. Finally, we integrate these two views into a
holistic multi-scale correlation extraction module, which conducts temporal
pooling with different scales to model different temporal patterns. Extensive
experiments on four popular traffic benchmark datasets demonstrate the
effectiveness of our proposed DyHSL compared with a broad range of competing
baselines.
