  This paper aims to unify spatial dependency and temporal dependency in a
non-Euclidean space while capturing the inner spatial-temporal dependencies for
traffic data. For spatial-temporal attribute entities with topological
structure, the space-time is consecutive and unified while each node's current
status is influenced by its neighbors' past states over variant periods of each
neighbor. Most spatial-temporal neural networks for traffic forecasting study
spatial dependency and temporal correlation separately in processing, gravely
impaired the spatial-temporal integrity, and ignore the fact that the
neighbors' temporal dependency period for a node can be delayed and dynamic. To
model this actual condition, we propose TraverseNet, a novel spatial-temporal
graph neural network, viewing space and time as an inseparable whole, to mine
spatial-temporal graphs while exploiting the evolving spatial-temporal
dependencies for each node via message traverse mechanisms. Experiments with
ablation and parameter studies have validated the effectiveness of the proposed
TraverseNet, and the detailed implementation can be found from
https://github.com/nnzhan/TraverseNet.
