Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting

Yusheng Zhao, Xiao Luo, Wei Ju, Chong Chen, Xian-Sheng Hua, Ming Zhang

Abstract:

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 spatiotemporal 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 highorder 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 nonpairwise 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.