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机制，旨在最小化因不必要的结构性约束而产生的低效问题。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，构建动态更新的空域交通模式图。基于此地图，智能体能够通过权衡时间与秩序等效用指标，判断遵循交通流的优势程度。研究表明，在所述交通流量水平下，较低程度的交通跟随行为可在几乎不影响航空器飞行时间的同时，显著提升空域整体有序性；而过度增强的交通跟随行为虽能略微降低空域整体熵值，却可能导致航空器飞行时间增加。本文提出的方法与度量指标，最终可用于基于上述权衡关系，对智能体的交通跟随行为进行动态优化调整。