  Predicting the supply and demand of transport systems is vital for efficient
traffic management, control, optimization, and planning. For example,
predicting where from/to and when people intend to travel by taxi can support
fleet managers to distribute resources; better predicting traffic
speeds/congestion allows for pro-active control measures or for users to better
choose their paths. Making spatio-temporal predictions is known to be a hard
task, but recently Graph Neural Networks (GNNs) have been widely applied on
non-euclidean spatial data. However, most GNN models require a predefined
graph, and so far, researchers rely on heuristics to generate this graph for
the model to use. In this paper, we use Neural Relational Inference to learn
the optimal graph for the model. Our approach has several advantages: 1) a
Variational Auto Encoder structure allows for the graph to be dynamically
determined by the data, potentially changing through time; 2) the encoder
structure allows the use of external data in the generation of the graph; 3) it
is possible to place Bayesian priors on the generated graphs to encode domain
knowledge. We conduct experiments on two datasets, namely the NYC Yellow Taxi
and the PEMS road traffic datasets. In both datasets, we outperform benchmarks
and show performance comparable to state-of-the-art. Furthermore, we do an
in-depth analysis of the learned graphs, providing insights on what kinds of
connections GNNs use for spatio-temporal predictions in the transport domain.
