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Mobility-on-demand (MoD) systems represent a rapidly developing mode oftransportation wherein travel requests are dynamically handled by a coordinatedfleet of vehicles. Crucially, the efficiency of an MoD system highly depends onhow 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 placeand at the desired time). To do so, we argue that predictive models should aimto explicitly disentangle between temporal} and spatial variability in theevolution of urban mobility demand. However, current approaches typicallyignore this distinction by either treating both sources of variability jointly,or completely ignoring their presence in the first place. In this paper, wepropose recurrent flow networks (RFN), where we explore the inclusion of (i)latent random variables in the hidden state of recurrent neural networks tomodel temporal variability, and (ii) normalizing flows to model the spatialdistribution of mobility demand. We demonstrate how predictive modelsexplicitly disentangling between spatial and temporal variability exhibitseveral desirable properties, and empirically show how this enables thegeneration of distributions matching potentially complex urban topologies.

Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility