Air Traffic Estimation and Decision Support for Stochastic Flow Management

Abstract

- † ‡ Development of a decision support system that uses real-time track data to estimate statistical parameters describing the stochastic traffic flow is described. Modern statistical decision theory is applied to optimize traffic flow. An advanced estimation algorithm provides the parameter estimates based on queuing network models of traffic flow. A hypothesis testing approach is developed for triggering traffic flow management initiatives in the terminal area, and a stochastic quadratic programming methodology is advanced to achieve flow control objectives such as runway load balancing. The use of this methodology is demonstrated using multi-day track data in the San Francisco terminal area. It is shown that the methodology can correctly identify the need for restricting the traffic flow into the terminal area, and provide decision support to balance the traffic flow at the runways under uncertain traffic flow conditions. The present approach can be extended to the creation of decision support tools for a wide variety of stochastic air traffic flow control situations.

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