  Substantial efforts have been devoted to the investigation of spatiotemporal
correlations for improving traffic speed prediction accuracy. However, existing
works typically model the correlations based solely on the observed traffic
state (e.g. traffic speed) without due consideration that different correlation
measurements of the traffic data could exhibit a diverse set of patterns under
different traffic situations. In addition, the existing works assume that all
road segments can employ the same sampling frequency of traffic states, which
is impractical. In this paper, we propose new measurements to model the spatial
correlations among traffic data and show that the resulting correlation
patterns vary significantly under various traffic situations. We propose a
Heterogeneous Spatial Correlation (HSC) model to capture the spatial
correlation based on a specific measurement, where the traffic data of varying
road segments can be heterogeneous (i.e. obtained with different sampling
frequency). We propose a Multi-fold Correlation Attention Network (MCAN), which
relies on the HSC model to explore multi-fold spatial correlations and leverage
LSTM networks to capture multi-fold temporal correlations to provide
discriminating features in order to achieve accurate traffic prediction. The
learned multi-fold spatiotemporal correlations together with contextual factors
are fused with attention mechanism to make the final predictions. Experiments
on real-world datasets demonstrate that the proposed MCAN model outperforms the
state-of-the-art baselines.
