Agent-Based Collaborative Traffic Flow Management

We propose agent-based game-theoretic approaches for simulation of strategies involved in multi-objective collaborative traffic flow management (CTFM). Intelligent agents represent two types of entities / players: FAA Traffic Management Unit (TMU) representatives, and Airline Operations Center (AOC) coordinators. The software modules resulting from this work are intended to be part of the NASA ARC multi-agent simulation toolkit for CTFM. The goal is to utilize game theory to understand the behavior of AOCs so that the CTFM system may be designed to yield improved performance of the NextGen AirSpace without compromising safety. We consider a spectrum of information sharing cases, from complete information sharing to incomplete information sharing where AOCs limit the transparency of their strategies to the FAA TMU. This agent-based simulation software will enhance the ability of the FAA (and other parties including NASA) to design proper collaboration protocols and incentives by studying the effects of different strategies by both types of players. We call this a "co-opetition" simulation tool since it allows analysis of competitive strategies between AOCs, while providing insights into how the TMU can promote greater cooperation by the AOCs for the common good. We should emphasize that the intent of the proposed software is NOT to design effective AOC strategies since that will be determined by the individual airlines, and not the CTFM system designers.