基于Copula的可迁移合成人口生成模型
Copula-based transferable models for synthetic population generation
AI总结：
提出基于copula的框架，在仅已知边缘分布时生成合成人口，引入空间成分并支持跨地理级别迁移，实验表明其能增强机器学习方法并优于传统IPF算法。
摘要：
人口合成旨在生成目标人口微智能体的合成但真实的表示，用于行为建模和仿真。传统方法通常依赖目标人口样本（如人口普查数据或出行调查），由于成本高和样本量小，特别是在较小的地理尺度上，面临局限性。我们提出了一种基于copulas的新颖框架，用于在仅已知经验边缘分布的情况下生成目标人口的合成数据。该方法利用来自具有相似边缘依赖性的不同人群的样本，在人口合成中引入空间成分，并考虑各种信息源以构建更真实的生成器。具体而言，该过程包括对数据进行归一化并将其视为给定copula的实现，然后在结合目标人群边缘分布信息之前训练生成模型。我们利用American Community Survey数据，通过标准化均方根误差（SRMSE）和所谓的采样零来评估框架的性能。我们重点关注其将从一个群体学习到的模型迁移到另一个群体的能力。我们的实验包括在同一地理级别的区域之间以及向较低地理级别的迁移测试，从而评估框架在不同空间环境中的适应性。我们比较了Bayesian Networks、Variational Autoencoders和Generative Adversarial Networks，包括单独使用以及与我们的copula框架结合使用。结果表明，copula增强了机器学习方法在匹配参考数据边缘分布方面的能力。此外，在可迁移性实验中，它在SRMSE方面始终优于Iterative Proportional Fitting，同时引入了原始训练样本中未发现的独特观测值。
英文摘要：
Population synthesis involves generating synthetic yet realistic representations of a target population of micro-agents for behavioral modeling and simulation. Traditional methods, often reliant on target population samples, such as census data or travel surveys, face limitations due to high costs and small sample sizes, particularly at smaller geographical scales. We propose a novel framework based on copulas to generate synthetic data for target populations where only empirical marginal distributions are known. This method utilizes samples from different populations with similar marginal dependencies, introduces a spatial component into population synthesis, and considers various information sources for more realistic generators. Concretely, the process involves normalizing the data and treating it as realizations of a given copula, and then training a generative model before incorporating the information on the marginals of the target population. Utilizing American Community Survey data, we assess our framework's performance through standardized root mean squared error (SRMSE) and so-called sampled zeros. We focus on its capacity to transfer a model learned from one population to another. Our experiments include transfer tests between regions at the same geographical level as well as to lower geographical levels, hence evaluating the framework's adaptability in varied spatial contexts. We compare Bayesian Networks, Variational Autoencoders, and Generative Adversarial Networks, both individually and combined with our copula framework. Results show that the copula enhances machine learning methods in matching the marginals of the reference data. Furthermore, it consistently surpasses Iterative Proportional Fitting in terms of SRMSE in the transferability experiments, while introducing unique observations not found in the original training sample.
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