Benefits of Traffic-Following in High-Density Autonomous Airspace Operations

In this paper, we explore the dynamic emergence of traffic order within a distributed multi-agent system, focusing on minimizing inefficiencies stemming from unnecessary impositions of structural rules. We leverage a methodology for creating a dynamically updating traffic pattern map of the airspace. This map utilizes information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent may adjust the degree of traffic-following behavior it exhibits. We show that at low densities, traffic following behavior results in a decrease in entropy of the airspace with low penalties in terms of travel times. As the density of an airspace increases, substantial gains in both airspace entropy and travel times are seen as the degree of traffic following behavior increases. 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 the density of traffic within an airspace.