  A long-standing question for urban and regional planners pertains to the
ability to describe urban patterns quantitatively. Cities' transport
infrastructure, particularly street networks, provides an invaluable source of
information about the urban patterns generated by peoples' movements and their
interactions. With the increasing availability of street network datasets and
the advancements in deep learning methods, we are presented with an
unprecedented opportunity to push the frontiers of urban modelling towards more
data-driven and accurate models of urban forms. In this study, we present our
initial work on applying deep generative models to urban street network data to
create spatially explicit urban models. We based our work on Variational
Autoencoders (VAEs) which are deep generative models that have recently gained
their popularity due to the ability to generate realistic images. Initial
results show that VAEs are capable of capturing key high-level urban network
metrics using low-dimensional vectors and generating new urban forms of
complexity matching the cities captured in the street network data.
