The feasibility of developing a statistical decision support system for traffic flow management in the terminal area and runway load balancing was demonstrated in the Phase I research. The methodology employed an advanced estimation algorithm based on a queuing network model of the runway and the terminal area, and statistical decision theory to formulate traffic flow decisions. Radar data from the San Francisco terminal area was used in the feasibility demonstration. Component technologies developed in Phase I work can be used for synthesizing real-time statistical decision support tools for runway configuration management and arrival/departure scheduling. Phase II work will use the Phase I algorithms for developing decision support tools for NASA's System-Oriented Runway Management program elements. Queuing networks of runways, taxiways, gates, and terminal airspace will form the foundation of the decision support tool. Predicted demand, historic traffic data and real-time measurements will be combined in an estimator to generate the statistical distributions of the queuing network parameters. These will then be used in conjunction with methods from Statistical Decision Theory to generate actionable decisions. Phase II research will develop a software package implementing these algorithms, which can be evaluated in human-in-the-loop and operational settings during the Phase III work.