Synthetic Population: A Reliable Framework for Analysis for Agent-Based Modeling in Mobility

This paper presents a comprehensive and innovative evaluation framework for identifying a reliable population synthesis for agent-based modeling-transportation-oriented simulations (ABM-TOS). We show, via this framework and different metrics for the analysis of the generated distribution of the individuals' attributes, that population synthesizers may fail to correctly replicate the real population heterogeneity owing to diverse control variables, data limitations, and post-simulation computation of certain parameter distributions. To show these shortcomings, the authors propose a systematic classification of different types of distributions crucial for mobility simulations. The proposed framework aims to provide a comprehensive overview of the population and serve as a rapid 'debugging' tool to identify and rectify any flaws in a specific population during the calibration of the activity-based mobility simulation models. To prove the effectiveness of this framework, we applied it to synthetic populations generated through MOBIUS (mobility optimization based on iterative user synthesis), a newly developed synthetic population generator, which in this case was employed to create different variants of the Luxembourg population (1%, 10%, 30%). The application of our framework to these populations not only provided an effective method for assessing their goodness of fit, but also helped highlight the distributions that are most critical to the successful implementation of the methodology.