Authors: Antonio Marcio Ferreira Crespo; Li Weigang; Alexandre Gomes de Barros
Addresses: Comando da Aeronáutica, Centro de Gerenciamento da Navegação Aérea, Praça Sen. Salgado Filho S/N, Centro Rio de Janeiro, 71615-600, RJ, Brazil. ' Department of Computer Science, University of Brasilia, Caixa Postal 4466, 70919-970 Brasilia, DF, Brazil. ' Department of Civil Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada
Abstract: Air traffic flow management (ATFM) is of crucial importance for the airspace control system, due to two factors: first, the impact of ATFM on air traffic control, including inherent safety implications on air operations; second, the possible consequences of ATFM measures on airport operations. Thus, it is imperative to establish procedures and develop systems that help traffic flow managers to take optimal actions. In this context, this work presents a comparative study of ATFM measures generated by a computational agent based on artificial intelligence (reinforcement learning). The goal of the agent is to establish delays upon takeoff schedules of aircraft departing from certain terminal areas so as to avoid congestion or saturation in the air traffic control sectors due to a possible imbalance between demand and capacity. The paper includes a case study comparing the ATFM measures generated by the agent autonomously and measures generated taking into account the experience of human traffic flow managers. The experiments showed satisfactory results.
Keywords: ATFM measures; agents; artificial intelligence; AI; reinforcement learning; tactical flow management; air traffic flow management; airspace control; air traffic control; agent-based systems; congestion; saturation; aircraft takeoff schedules; delays.
International Journal of Aviation Management, 2012 Vol.1 No.3, pp.145 - 161
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 02 Mar 2012 *