  Mobility-on-demand (MoD) systems represent a rapidly developing mode of
transportation wherein travel requests are dynamically handled by a coordinated
fleet of vehicles. Crucially, the efficiency of an MoD system highly depends on
how well supply and demand distributions are aligned in spatio-temporal space
(i.e., to satisfy user demand, cars have to be available in the correct place
and at the desired time). To do so, we argue that predictive models should aim
to explicitly disentangle between temporal} and spatial variability in the
evolution of urban mobility demand. However, current approaches typically
ignore this distinction by either treating both sources of variability jointly,
or completely ignoring their presence in the first place. In this paper, we
propose recurrent flow networks (RFN), where we explore the inclusion of (i)
latent random variables in the hidden state of recurrent neural networks to
model temporal variability, and (ii) normalizing flows to model the spatial
distribution of mobility demand. We demonstrate how predictive models
explicitly disentangling between spatial and temporal variability exhibit
several desirable properties, and empirically show how this enables the
generation of distributions matching potentially complex urban topologies.
