MAMCABM: A Data-Driven Stakeholder-Based Decision-Support System that Considers Uncertainties

In recent years, decision-making in mobility has increasingly relied on data support and consideration of uncertainty. However, conventional decision-making methods such as Multi-Criteria Decision Making (MCDM) and Multi-Criteria Group Decision Making (MCGDM) have limitations in accounting for the complexity and dynamics of real-world mobility situations. This has led to an interest in Agent-Based Modeling (ABM), which can capture the heterogeneity and interactions of individuals in a system. On the other hand, MCDM remains a legitimate method that allows for the consideration of conflicting interests simultaneously. Moreover, it is still valuable to involve stakeholders in the decision-making process, as they can provide important insights and perspectives that may not be captured by purely analytical methods.

This paper presents a novel decision-support system (DSS) that combines Multi-Attribute Multi-Criteria Analysis (MAMCA) and ABM to support mobility decision-making under conditions of uncertainty, called MAMCABM. The DSS provide stakeholders with a comprehensive decision making tool to assess and compare alternative scenarios based on different criteria, where ABM provides rich data support. Furthermore, MAMCABM also accounts for uncertainties that are generated in different steps. MAMCABM is demonstrated on a real-world case study of a road adjacent to a university campus, where different types of vehicles, cyclists and pedestrians interact in complex ways. The results of the MAMCABM analysis highlight the importance of considering multiple criteria and uncertainty in mobility decision-making, and provide valuable insights for improving the road situation by taking into account the preferences of different stakeholders.