SemaPop: Semantic-Persona Conditioned and Controllable Population Synthesis

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 distributional consistency. Extensive experiments demonstrate that SemaPop substantially improves generative performance, yielding closer alignment with target marginal and joint distributions while maintaining sample-level feasibility and diversity under semantic conditioning. Counterfactual analyses further demonstrate that semantic interventions induce systematic and interpretable shifts in generated populations. These results highlight the potential of persona-based semantic conditioning for controllable and scenario-oriented population synthesis.