In this paper, we investigate the dynamic emergence of traffic order in a distributed multi-agent system, aiming to minimize inefficiencies that stem from unnecessary structural impositions. We introduce a methodology for developing a dynamically-updating traffic pattern map of the airspace by leveraging information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent can discern the degree to which it is advantageous to follow traffic by trading off utilities such as time and order. We show that for the traffic levels studied, for low degrees of traffic-following behavior, there is minimal penalty in terms of aircraft travel times while improving the overall orderliness of the airspace. On the other hand, heightened traffic-following behavior may result in increased aircraft travel times, while marginally reducing the overall entropy of the airspace. Ultimately, the methods and metrics presented in this paper can be used to optimally and dynamically adjust an agent's traffic-following behavior based on these trade-offs.

翻译：本文研究分布式多智能体系统中交通秩序的动态涌现现象，旨在减少因不必要的结构性约束导致的效率损失。我们提出一种方法，通过利用当前及历史交通流方向的一致性与频率信息，动态生成空域交通模式地图。基于该地图，智能体可通过权衡时间消耗与秩序性等效用指标，判断跟随交通流的获益程度。研究表明：在所研究的交通密度范围内，低程度交通跟随行为对飞行器航行时间的惩罚极小，但能显著提升空域整体有序性；而高程度交通跟随行为虽可小幅降低空域熵值，却可能导致航行时间增加。最终，本文提出的方法与度量标准可用于基于上述权衡动态优化智能体的交通跟随行为。