  This paper investigates traffic forecasting, which attempts to forecast the
future state of traffic based on historical situations. This problem has
received ever-increasing attention in various scenarios and facilitated the
development of numerous downstream applications such as urban planning and
transportation management. However, the efficacy of existing methods remains
sub-optimal due to their tendency to model temporal and spatial relationships
independently, thereby inadequately accounting for complex high-order
interactions of both worlds. Moreover, the diversity of transitional patterns
in traffic forecasting makes them challenging to capture for existing
approaches, warranting a deeper exploration of their diversity. Toward this
end, this paper proposes Conjoint Spatio-Temporal graph neural network
(abbreviated as COOL), which models heterogeneous graphs from prior and
posterior information to conjointly capture high-order spatio-temporal
relationships. On the one hand, heterogeneous graphs connecting sequential
observation are constructed to extract composite spatio-temporal relationships
via prior message passing. On the other hand, we model dynamic relationships
using constructed affinity and penalty graphs, which guide posterior message
passing to incorporate complementary semantic information into node
representations. Moreover, to capture diverse transitional properties to
enhance traffic forecasting, we propose a conjoint self-attention decoder that
models diverse temporal patterns from both multi-rank and multi-scale views.
Experimental results on four popular benchmark datasets demonstrate that our
proposed COOL provides state-of-the-art performance compared with the
competitive baselines.
