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Intelligent computing methods in Air Traffic Flow Management
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DOI：10.1016/j.trc.2009.06.004.png)
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Abstract
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This research presents the application of intelligent computing models in Air Traffic Flow Management (ATFM). Firstly, multi-agent system in grid computing environment is applied to deal with the problem of ATFM synchronization. The developed system consists of software agents, which are implemented in a Computational Grid platform for congestions identification, conflicts resolution and agreements negotiation among the participating airports. A metric criterion, called Agent's Balancing Standard (ABS), is used as a basic index to measure the effectiveness of reducing both the amount of communication among agents and the delay of flights. Secondly, a brief discussion about the Meta-Level Control model is introduced in ATFM issues to improve the efficiency of communication among the agents. The system is developed to analyze the traffic flow information received, identify its importance and process it in the most adequate order. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Air Traffic Flow Management
Meta-Level Control
Multi-agent system
Intelligent computing
Real time traffic synchronization
Reinforcement learning
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