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This paper introduces a data driven model for predicting airport arrival capacity with 2-8 hour look-ahead forecast data. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport’s arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system.

Aviation Data Integration System (ADIS) project explores methods and techniques for integrating heterogeneous aviation data to support aviation problem-solving activity. Aviation problem-solving activities include: engineering troubleshooting, incident and accident investigation, routine flight operations monitoring, flight plan deviation monitoring, safety assessment, maintenance procedure debugging, and training assessment. To provide optimal quality of service, ADIS utilizes distributed intelligent agents including data collection agents, coordinator agents and mediator agents. This paper describes the proposed agent-based architecture of the Aviation Data Integration System (ADIS).