Multi-Agent Traffic Simulations with Agent Control Based on Logic Programming Languages

Environmental concerns, particularly those related to exhaust emissions from congested traffic and the growing popularity of electric and autonomous vehicles, are at the forefront of societal discussions. In response, various traffic management strategies have been proposed. However, evaluating the impact of these policies and regulations on real-world traffic flows is challenging. To address this, we developed MACiMA, a multi-agent traffic simulator designed to replicate intricate traffic patterns and assess the effectiveness of different traffic management approaches through simulation. While MACiMA effectively models traffic flows through agent interactions, defining the behavior of individual agents within the constraints of specific traffic policies presents a significant programming challenge. To overcome this, we explored an innovative approach by integrating MACiMA with PROLEG, a legal reasoning system based on the logic programming language Prolog. This integration enables us to model the decision-making processes of agents influenced by applicable traffic laws and policies. Our experiments, which involved autonomous vehicles and various policies across multiple areas, revealed that agents could dynamically adapt their behavior according to the implemented traffic policies. Furthermore, these experiments highlighted the impact of varying proportions of autonomous vehicles on traffic conditions and uncovered region-specific differences in response to different conditions.