Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy

As urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for emergency vehicles (EVs) and fairness for other vehicles. This model involves the Traffic Management Center (TMC) as the top-level authority, with emergency vehicles as the first-level leaders and regular vehicles (RVs) as the second-level followers. The multilevel decision-making structure enables real-time adjustments to prioritize critical traffic and ensure equitable treatment for regular traffic. Simulations were conducted under various traffic scenarios, including normal conditions, emergency vehicle priority, and peak traffic congestion. According to the results, the hierarchical Stackelberg model outperforms traditional models in terms of reducing average travel time, waiting time, and congestion. The model also incorporates fairness metrics such as Gini coefficients and skewness to ensure that regular vehicles are not disproportionately affected by emergency vehicle priority. According to these findings, the hierarchical Stackelberg model improves both traffic efficiency and fairness in complex urban environments, positioning it as a promising solution.