The MAMCABM framework for the evaluation of mobility decision‐making problems: theory and practice

Abstract

In the constantly evolving transportation and mobility industry, objective and reliable decision‐support systems (DSS) are crucial for addressing complex issues such as transit planning, mode selection, and policy formulation. This paper presents multiactor multicriteria analysis plus agent‐based modeling (MAMCABM), a novel framework that combines multiactor multicriteria analysis (MAMCA) and agent‐based modeling (ABM) to provide a comprehensive DSS. MAMCA excels in facilitating stakeholder‐centric evaluations, while ABM, enhanced by data analytics, adeptly models intricate, interactive systems. The combination of MAMCA and ABM enhances adaptability and precision in decision making. This integration utilizes data analytics and optimization algorithms to provide solutions that consider multifaceted criteria and diverse stakeholder perspectives in dynamic and uncertain contexts. The study outlines the mathematical underpinnings of MAMCABM and offers a practical guide for its implementation. The framework's efficacy is demonstrated through an empirical investigation that addresses mobility challenges in the Brussels Capital Region of Belgium. Compared to the previous study, this approach leverages simulated quantitative data alongside qualitative judgments from stakeholders. The integration of a consensus‐reaching algorithm further enhances the robustness of outcomes and effectively addresses uncertainties.