Scholarlink

<p>This paper introduces two novel deep generative frameworks for synthetic population generation that jointly model household and individual attributes. In leveraging Variational Autoencoders (VAEs), we propose herein the SVAE-Pop2 method, which employs a single VAE with fixed-size padded inputs, along with the MVAE-Pop2 method, which uses dedicated models for various household sizes. Evaluated on a French household travel survey dataset, our experiments reveal that while both approaches effectively reproduce the actual population’s characteristics, MVAE-Pop2 achieves greater fidelity in joint attribute distributions. The proposed methodologies suggest improvements in agent-based simulations and urban modeling by means of generating realistic, multi-layered synthetic populations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Population synthesis with deep generative model: a joint household-individual approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

查看全文
  • Abdoul Razac Sané,
  • Rachid Belaroussi,
  • Pierre Hankach,
  • Pierre-Olivier Vandanjon

摘要

This paper introduces two novel deep generative frameworks for synthetic population generation that jointly model household and individual attributes. In leveraging Variational Autoencoders (VAEs), we propose herein the SVAE-Pop2 method, which employs a single VAE with fixed-size padded inputs, along with the MVAE-Pop2 method, which uses dedicated models for various household sizes. Evaluated on a French household travel survey dataset, our experiments reveal that while both approaches effectively reproduce the actual population’s characteristics, MVAE-Pop2 achieves greater fidelity in joint attribute distributions. The proposed methodologies suggest improvements in agent-based simulations and urban modeling by means of generating realistic, multi-layered synthetic populations.