Reward functions for learning to control in air traffic flow management

Air Traffic Flow Management (ATFM) is a complex decision-making process with multiple stakeholders involved. In this decision loop, a Multi-agent system is developed for both simulation and daily operations to support human decisions. Considering human factors in ATFM, the method of Reinforcement Learning (RL) is suitable in the acquirement of the knowledge and experience of the controllers to assist them in the next control activities. The paper presents the recent development of reinforcement learning and its reward structure for ATFM decision making. Two types of reward functions are proposed for agent-based RL in the application of air traffic management: (1) Reward function considering safety separation and fairness impact among different commercial entities in Ground Holding Problem (GHP) and (2) Reward function considering safety separation in Air Holding Problem (AHP). Real case studies in Brazil are described to show the effectiveness and efficiency of the developed reward functions in the controller decision process of ATFM.

• Record URL:
  https://doi.org/10.1016/j.trc.2013.06.010
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X13001320
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier.
• Authors:
  • Cruciol, Leonardo L B V
  • de Arruda, Antonio C
  • Weigang, Li
  • Li, Leihong
  • Crespo, Antonio M F
• Publication Date: 2013-10

Language

• English

Media Info

• Media Type: Print
• Features: Figures; Maps; References; Tables;
• Pagination: pp 141-155
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 35
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Air traffic control; Case studies; Decision support systems; Flow control (Air traffic control); Human factors engineering; Machine learning; Multi-agent systems
• Geographic Terms: Brazil
• Subject Areas: Aviation; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01499546
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 21 2013 9:21AM