SemaPop: Semantic-Persona Conditioned and Controllable Population Synthesis

ArXi:2602.11569v2 Announce Type: replace 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 scenario-driven or target-oriented population synthesis. This study proposes SemaPop, a semantic-conditioned and controllable population synthesis framework that.