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.

中文翻译：

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使用变分自动编码器对城市网络建模

对于城市和区域规划者来说，一个长期存在的问题是关于定量描述城市格局的能力。城市的交通基础设施，特别是街道网络，提供了有关人民运动及其相互作用产生的城市格局的宝贵信息来源。随着街道网络数据集可用性的提高和深度学习方法的进步，我们面临着前所未有的机遇，可以将城市建模的前沿推向更数据驱动和更准确的城市形态模型。将深度生成模型应用于城市街道网络数据以创建空间明确的城市模型的工作。我们的工作基于变分自动编码器（VAE），这是一种深度生成模型，由于能够生成逼真的图像，最近已广受欢迎。初步结果表明，VAE能够使用低维矢量捕获关键的高层城市网络指标，并生成与街道网络数据中捕获的城市相匹配的复杂性的新城市形式。