Baud, C., and Bierlaire, M. (2026)

From Priors to Data: A Flexible Bayesian Framework for Panel Synthetic Population Generation.�

14th Symposium of the European Association for Research in Transportation, Paris, France

Most methods for generating synthetic populations rely on cross-sectional snapshots or pseudopanels, which do not track individuals consistently over time. This paper proposes a general framework for constructing synthetic populations whose panel structure is specified by design. Individuals are represented through life-based trajectories defined independently of calendar time, and a deterministic mapping recovers their state at any time t, allowing the reconstruction of panel data and population distributions at arbitrary points in time. The framework is model-agnostic and enforces internal consistency through structural constraints embedded in the life representation. We further introduce a Bayesian updating mechanism that incorporates information from observed cross-sectional datasets. When data are available, the synthetic population is sampled from the posterior distribution, combining prior knowledge with the evidence contained in the observations. This allows cross-sectional information, such as census data, to inform the generation of coherent longitudinal populations.