  Traffic prediction has drawn increasing attention in AI research field due to
the increasing availability of large-scale traffic data and its importance in
the real world. For example, an accurate taxi demand prediction can assist taxi
companies in pre-allocating taxis. The key challenge of traffic prediction lies
in how to model the complex spatial dependencies and temporal dynamics.
Although both factors have been considered in modeling, existing works make
strong assumptions about spatial dependence and temporal dynamics, i.e.,
spatial dependence is stationary in time, and temporal dynamics is strictly
periodical. However, in practice, the spatial dependence could be dynamic
(i.e., changing from time to time), and the temporal dynamics could have some
perturbation from one period to another period. In this paper, we make two
important observations: (1) the spatial dependencies between locations are
dynamic; and (2) the temporal dependency follows daily and weekly pattern but
it is not strictly periodic for its dynamic temporal shifting. To address these
two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in
which a flow gating mechanism is introduced to learn the dynamic similarity
between locations, and a periodically shifted attention mechanism is designed
to handle long-term periodic temporal shifting. To the best of our knowledge,
this is the first work that tackles both issues in a unified framework. Our
experimental results on real-world traffic datasets verify the effectiveness of
the proposed method.
