  In recent years, graph neural networks (GNNs) combined with variants of
recurrent neural networks (RNNs) have reached state-of-the-art performance in
spatiotemporal forecasting tasks. This is particularly the case for traffic
forecasting, where GNN models use the graph structure of road networks to
account for spatial correlation between links and nodes. Recent solutions are
either based on complex graph operations or avoiding predefined graphs. This
paper proposes a new sequence-to-sequence architecture to extract the
spatiotemporal correlation at multiple levels of abstraction using GNN-RNN
cells with sparse architecture to decrease training time compared to more
complex designs. Encoding the same input sequence through multiple encoders,
with an incremental increase in encoder layers, enables the network to learn
general and detailed information through multilevel abstraction. We further
present a new benchmark dataset of street-level segment traffic data from
Montreal, Canada. Unlike highways, urban road segments are cyclic and
characterized by complicated spatial dependencies. Experimental results on the
METR-LA benchmark highway and our MSLTD street-level segment datasets
demonstrate that our model improves performance by more than 7% for one-hour
prediction compared to the baseline methods while reducing computing resource
requirements by more than half compared to other competing methods.
