  Regional traffic forecasting is a critical challenge in urban mobility, with
applications to various fields such as the Internet of Everything. In recent
years, spatio-temporal graph neural networks have achieved state-of-the-art
results in the context of numerous traffic forecasting challenges. This work
aims at expanding upon the conventional spatio-temporal graph neural network
architectures in a manner that may facilitate the inclusion of information
regarding the examined regions, as well as the populations that traverse them,
in order to establish a more efficient prediction model. The end-product of
this scientific endeavour is a novel spatio-temporal graph neural network
architecture that is referred to as WEST (WEighted STacked) GCN-LSTM.
Furthermore, the inclusion of the aforementioned information is conducted via
the use of two novel dedicated algorithms that are referred to as the Shared
Borders Policy and the Adjustable Hops Policy. Through information fusion and
distillation, the proposed solution manages to significantly outperform its
competitors in the frame of an experimental evaluation that consists of 19
forecasting models, across several datasets. Finally, an additional ablation
study determined that each of the components of the proposed solution
contributes towards enhancing its overall performance.
