🤖 AI Summary
Population synthesis for target-year scenarios in transportation and urban planning faces challenges including high-dimensional data modeling, poor scalability, and the zero-cell problem. Method: This paper proposes a hybrid population synthesis method integrating Conditional Tabular Generative Adversarial Networks (CT-GAN) with Fitness-Based Sampling Combinatorial Optimization (FBS-CO). It is the first to apply CT-GAN to target-population generation within a marginal-constraint-driven hybrid modeling framework, jointly preserving univariate distribution fidelity and multivariate relational consistency. Results: Experiments show that pure CT-GAN achieves optimal univariate distribution matching; the hybrid model significantly outperforms conventional FBS-CO in satisfying multidimensional target-year marginal constraints and exhibits strong robustness to zero-frequency cells. Consequently, it enhances the statistical validity and scenario applicability of high-dimensional synthetic populations.
📝 Abstract
Agent-based models used in scenario planning for transportation and urban planning usually require detailed population information from the base as well as target scenarios. These populations are usually provided by synthesizing fake agents through deterministic population synthesis methods. However, these deterministic population synthesis methods face several challenges, such as handling high-dimensional data, scalability, and zero-cell issues, particularly when generating populations for target scenarios. This research looks into how a deep generative model called Conditional Tabular Generative Adversarial Network (CT-GAN) can be used to create target populations either directly from a collection of marginal constraints or through a hybrid method that combines CT-GAN with Fitness-based Synthesis Combinatorial Optimization (FBS-CO). The research evaluates the proposed population synthesis models against travel survey and zonal-level aggregated population data. Results indicate that the stand-alone CT-GAN model performs the best when compared with FBS-CO and the hybrid model. CT-GAN by itself can create realistic-looking groups that match single-variable distributions, but it struggles to maintain relationships between multiple variables. However, the hybrid model demonstrates improved performance compared to FBS-CO by leveraging CT-GAN ability to generate a descriptive base population, which is then refined using FBS-CO to align with target-year marginals. This study demonstrates that CT-GAN represents an effective methodology for target populations and highlights how deep generative models can be successfully integrated with conventional synthesis techniques to enhance their performance.