  Providing efficient human mobility services and infrastructure is one of the
major concerns of most mid-sized to large cities around the world. A proper
understanding of the dynamics of commuting flows is, therefore, a requisite to
better plan urban areas. In this context, an important task is to study
hypothetical scenarios in which possible future changes are evaluated. For
instance, how the increase in residential units or transportation modes in a
neighborhood will change the commuting flows to or from that region? In this
paper, we propose to leverage GMEL, a recently introduced graph neural network
model, to evaluate changes in commuting flows taking into account different
land use and infrastructure scenarios. We validate the usefulness of our
methodology through real-world case studies set in two large cities in Brazil.
