A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis
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 s...
| Authors | Zhenlin Qin, Leizhen Wang, Yancheng Ling et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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