A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis

2026-07-03

AI Summary

Existing generative population synthesis models struggle to assess whether scenario targets are compatible with the learned joint population structure, often introducing structural biases. This work proposes an ensemble-based Bayesian updating framework that employs a population-aware conditional variational autoencoder to model the underlying structural distribution and treats scenario targets as probabilistic evidence in the form of aggregate statistics. The influence of these targets on structural uncertainty is quantified through posterior weights. Crucially, the method introduces effective sample size (ESS) as a novel metric to evaluate scenario compatibility, revealing that synthetic outcomes depend not only on the magnitude of the targets but more fundamentally on their alignment with the joint population structure. This approach provides an interpretable probabilistic diagnostic tool for assessing scenario feasibility in transportation planning and enables the identification of potential structural failure modes.

Abstract

Scenario-based transportation analysis specifies future assumptions through aggregate population targets, whereas generative population synthesis models produce detailed individual-level realizations. When scenario targets are imposed on generative models, current practice relies on deterministic marginal calibration, implicitly assuming that the targets are compatible with the model's learned structural support. However, whether scenario-level constraints lie within the generative support--and how strongly they distort structural uncertainty--remains largely unexamined. We propose an ensemble-based Bayesian updating framework to quantify scenario compatibility in conditional population synthesis. A population-aware conditional variational autoencoder is developed to learn a distribution over plausible population structures while preserving aggregate fidelity. An ensemble of realizations sampled from the learned prior provides an empirical approximation of structural uncertainty. Scenario targets are treated as probabilistic evidence over aggregate statistics, and posterior weights are obtained through Bayesian updating across the ensemble. Scenario compatibility is quantified using effective sample size (ESS), which measures posterior concentration and the compression of structural uncertainty induced by conditioning. Experiments demonstrate that scenario impact depends not only on target magnitude but also on alignment with the learned joint structure, and reveal structural failure modes when targets fall outside prior ensemble support. The proposed framework provides a probabilistic diagnostic model for evaluating scenario feasibility and structural consistency before downstream projection and transportation planning.

Problem

• scenario compatibility
• generative population synthesis
• structural uncertainty
• Bayesian evaluation
• transportation planning

Innovation

• Bayesian updating
• conditional variational autoencoder
• scenario compatibility
• structural uncertainty
• effective sample size