A data-driven framework for agent-based modeling of vehicular travel using publicly available data

Abstract

This study presents a methodology for creating a synthetic travel demand, encompassing households and individuals and their daily activities, to support agent-based modeling (ABM) in urban planning and travel analysis. Unlike previous studies, which often rely on proprietary data, our approach is entirely based on open data, ensuring replicability by the broader research community. The research is among the first to propose the entire framework for travel demand synthesis and ABM. Results are validated against ground truth from the Census and other public data sources. The ABM results are compared to an Information Minimization (IM) model, which is an aggregated model capturing commuting patterns by race. The study contributes to the field by offering a comprehensive and replicable data foundation for ABM, serving as a valuable resource for evaluating population and travel demand synthesis algorithms.