  The significant increase in world population and urbanisation has brought
several important challenges, in particular regarding the sustainability,
maintenance and planning of urban mobility. At the same time, the exponential
increase of computing capability and of available sensor and location data have
offered the potential for innovative solutions to these challenges. In this
work, we focus on the challenge of traffic forecasting and review the recent
development and application of graph neural networks (GNN) to this problem.
GNNs are a class of deep learning methods that directly process the input as
graph data. This leverages more directly the spatial dependencies of traffic
data and makes use of the advantages of deep learning producing
state-of-the-art results. We introduce and review the emerging topic of GNNs,
including their most common variants, with a focus on its application to
traffic forecasting. We address the different ways of modelling traffic
forecasting as a (temporal) graph, the different approaches developed so far to
combine the graph and temporal learning components, as well as current
limitations and research opportunities.
