AI summaryⓘ
The authors address how future transportation scenarios often rely on broad population targets, but current models generate detailed individual populations without checking if these targets actually fit the model's possibilities. They create a new method using a type of AI model called a variational autoencoder, combined with Bayesian statistics, to see how well these scenario targets match the detailed population structures the model can produce. Their approach measures compatibility by looking at how much the scenario limits the range of possible populations. Their experiments show not only how big the scenario targets are matters, but also how well these targets align with what the model has learned, helping identify when assumptions may not be realistic.
Scenario-based transportation analysisGenerative population synthesisConditional variational autoencoderBayesian updatingStructural uncertaintyEffective sample size (ESS)Aggregate population targetsPosterior weightingPopulation structureScenario compatibility
Authors
Zhenlin Qin, Leizhen Wang, Yancheng Ling, Zhenliang Ma
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.