  Traffic flow forecasting is a crucial task in urban computing. The challenge
arises as traffic flows often exhibit intrinsic and latent spatio-temporal
correlations that cannot be identified by extracting the spatial and temporal
patterns of traffic data separately. We argue that such correlations are
universal and play a pivotal role in traffic flow. We put forward {spacetime
interval learning} as a paradigm to explicitly capture these correlations
through a unified analysis of both spatial and temporal features. Unlike the
state-of-the-art methods, which are restricted to a particular road network, we
model the universal spatio-temporal correlations that are transferable from
cities to cities. To this end, we propose a new spacetime interval learning
framework that constructs a local-spacetime context of a traffic sensor
comprising the data from its neighbors within close time points. Based on this
idea, we introduce local spacetime neural network (STNN), which employs novel
spacetime convolution and attention mechanism to learn the universal
spatio-temporal correlations. The proposed STNN captures local traffic
patterns, which does not depend on a specific network structure. As a result, a
trained STNN model can be applied on any unseen traffic networks. We evaluate
the proposed STNN on two public real-world traffic datasets and a simulated
dataset on dynamic networks. The experiment results show that STNN not only
improves prediction accuracy by 4% over state-of-the-art methods, but is also
effective in handling the case when the traffic network undergoes dynamic
changes as well as the superior generalization capability.
