Regulating air traffic flow with coupled agents

The ability to provide flexible, automated management of air traffic is critical to meeting the ever increasing needs of the next generation air transportation systems. This problem is particularly complex as it requires the integration of many factors including, updated information (e.g., changing weather info), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and very heavy traffic volume (e.g., over 40,000 daily flights over the US airspace). Furthermore, because the Federal Flight Administration will not accept black-box solutions, algorithmic improvements need to be consistent with current operating practices and provide explanations for each new decision. Unfortunately current methods provide neither flexibility for future upgrades, nor high enough performance in complex coupled air traffic flow problems. This paper extends agent-based methods for controlling air traffic flow to more realistic domains that have coupled flow patterns and need to be controlled through a variety of mechanisms. First, we explore an agent control structure that allows agents to control air traffic flow through one of three mechanisms (miles in trail, ground delays and rerouting). Second, we explore a new agent learning algorithm that can efficiently handle coupled flow patterns. We then test this agent solution on a series of congestion problems, showing that it is flexible enough to achieve high performance with different control mechanisms. In addition the results show that the new solution is able to achieve up to a 20% increase in performance over previous methods that did not account for the agent coupling.