Impact of Traffic-Following on Order of Autonomous Airspace Operations

TL;DR

This paper explores how autonomous airspace agents can dynamically balance traffic-following behavior to optimize efficiency and order, using a novel traffic pattern map and trade-off analysis.

Contribution

It introduces a methodology for creating a dynamic traffic pattern map and analyzing traffic-following trade-offs in autonomous airspace operations.

Findings

Low traffic-following levels maintain efficiency with improved order.

High traffic-following increases travel times but reduces airspace entropy.

The approach enables dynamic adjustment of traffic-following behavior based on trade-offs.

Abstract

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.