  Traffic forecasting problem remains a challenging task in the intelligent
transportation system due to its spatio-temporal complexity. Although temporal
dependency has been well studied and discussed, spatial dependency is
relatively less explored due to its large variations, especially in the urban
environment. In this study, a novel graph convolutional network model,
Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and
applied to two urban networks with contrasting geometric constraints. The model
conducts graph convolution operations on speed data with multi-weighted
adjacency matrices to combine the features, including speed limit, distance,
and angle. The spatially isolated dimension reduction operation is conducted on
the combined features to learn the dependencies among the features and reduce
the size of the output to a computationally feasible level. The output of
multi-weight graph convolution is applied to the sequence-to-sequence model
with Long Short-Term Memory units to learn temporal dependencies. When applied
to two urban sites, urban-core and urban-mix, MW-TGC network not only
outperformed the comparative models in both sites but also reduced variance in
the heterogeneous urban-mix network. We conclude that MW-TGC network can
provide a robust traffic forecasting performance across the variations in
spatial complexity, which can be a strong advantage in urban traffic
forecasting.
