Modelling urban networks using Variational Autoencoders

TL;DR

This paper explores using Variational Autoencoders to model urban street networks, enabling the generation of realistic urban forms and capturing key network metrics from data.

Contribution

It introduces a novel application of VAEs for urban network modeling, demonstrating their ability to generate complex, realistic urban street patterns.

Findings

VAEs can capture high-level urban network metrics

Generated urban forms match the complexity of real cities

Low-dimensional vectors effectively represent urban network features

Abstract

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.