Incremental, Probabilistic Decision Making for En Route Traffic Management

Abstract

This paper provides a method of incremental decision making using prediction uncertainty as a more efficient and effective means of aircraft congestion resolution. Standard manual en route airspace congestion planning often suffers from unplanned variables such as convective weather and thus systemic delays result. The proposed scheme uses a Monte Carlo decision method simulation technique to provide support for incremental probabilistic decision making. Researchers aim to have this simulation assist air traffic control (ATC) in solving various air traffic congestion contingencies by showing cost-benefit analyses of various decisions involved. All decisions made by the simulation method take into account quantitative evaluations that are based upon expected delay cost distributions for certain actions based on individual flight paths as opposed to the more standard approach based upon flows. It is noted that the best approach solves problems at increments of 90 minutes and 30 minutes prior to a congestion problem.