SemaPop: Semantic-Persona Conditioned and Controllable Population Synthesis
Zhenlin Qin, Yancheng Ling, Leizhen Wang, Francisco C\^amara Pereira, Zhenliang Ma

TL;DR
SemaPop introduces a novel framework for population synthesis that uses semantic conditioning via persona representations derived from survey data, enabling controllable and scenario-specific population generation.
Contribution
It presents a new semantic-conditioned population synthesis method using large language models and GANs, improving control and distributional accuracy over existing approaches.
Findings
SemaPop achieves closer alignment with target distributions.
Semantic conditioning allows systematic and interpretable population shifts.
The framework maintains diversity and feasibility in generated populations.
Abstract
Population synthesis is essential for individual-level simulation in transport planning and socio-economic analysis, yet remains challenging due to the need to capture both statistical dependencies and high-level behavioral semantics. Existing data-driven approaches predominantly rely on unconditional generation, limiting their ability to support scenario-driven or target-oriented population synthesis. This study proposes SemaPop, a semantic-conditioned and controllable population synthesis framework that introduces persona representations as conditioning signals for generation. By deriving persona text from survey data using large language models (LLMs) and encoding it into semantic embeddings, SemaPop enables controllable population generation under statistical constraints. We instantiate the framework using a GAN-based architecture with marginal regularization to preserve…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
