  Urban traffic speed prediction aims to estimate the future traffic speed for
improving urban transportation services. Enormous efforts have been made to
exploit Graph Neural Networks (GNNs) for modeling spatial correlations and
temporal dependencies of traffic speed evolving patterns, regularized by graph
topology.While achieving promising results, current traffic speed prediction
methods still suffer from ignoring topology-free patterns, which cannot be
captured by GNNs. To tackle this challenge, we propose a generic model for
enabling the current GNN-based methods to preserve topology-free patterns.
Specifically, we first develop a Dual Cross-Scale Transformer (DCST)
architecture, including a Spatial Transformer and a Temporal Transformer, to
preserve the cross-scale topology-free patterns and associated dynamics,
respectively. Then, to further integrate both topology-regularized/-free
patterns, we propose a distillation-style learning framework, in which the
existing GNN-based methods are considered as the teacher model, and the
proposed DCST architecture is considered as the student model. The teacher
model would inject the learned topology-regularized patterns into the student
model for integrating topology-free patterns. The extensive experimental
results demonstrated the effectiveness of our methods.
