论文标题
使用深层生成模型的城市居民建模的人口合成
Population synthesis for urban resident modeling using deep generative models
论文作者
论文摘要
新房地产开发的影响与其人口分布（家庭的类型和组成，收入，社会人口统计学）密切相关，该方面在诸如住宅类型学，价格，位置和地板水平等方面。本文提出了一种基于机器学习的方法，以模拟较大邻里/公寓设置中新建筑物即将到来的开发项目的人口分布。 我们使用来自越南河内的房地产开发项目Ecopark Township的真实数据集，在那里我们研究了来自深层生成模型文献中的两种机器学习算法，以创建一组合成剂：条件变化自动编码器（CVAE）和有条件的生成对抗性网络（CGAN）。进行了一项大型的实验研究，表明CVAE在估计新房地产开发项目的人口分布方面均优于经验分布，非平凡的基线模型和CGAN。
The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: Conditional Variational Auto-Encoder (CVAE) and Conditional Generative Adversarial Networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.
