Optimization of airport terminal-area air traffic operations under uncertain weather conditions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 153-158).Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Although Air Traffic Flow Management algorithms exist to schedule and route traffic in the face of disruptions, they require reliable forecasts of airspace capacity. However, there exists a gap between the spatial and temporal accuracy of aviation weather forecasts (and existing capacity models) and what these algorithms assume. In this thesis we consider the problem of integrating currently available convective weather forecasts with air traffic management in terminal airspace (near airports). We first demonstrate how raw convective weather forecasts, which provide deterministic predictions of the Vertically Integrated Liquid (the precipitation content in a column of airspace) can be translated into reliable and accurate probabilistic fore- casts of whether or not a terminal-area route will be blocked. Given a flight route through the terminal-area, we apply techniques from machine learning to determine the probability that the route will be open in actual weather. This probabilistic route blockage predictor is then used to optimize terminal-area operations. We develop an integer programming formulation for a 2-dimensional model of terminal airspace that dynamically moves arrival and departure routes to maximize expected capacity. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked while the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes are indeed open (on average) in the weather that materializes. In the final part of the thesis we consider more realistic models of terminal airspace routing and structure. We develop an A*-based routing algorithm that identifies 3-D routes through airspace that adhere to physical aircraft constraints during climb and descent, are conflict-free, and are likely to avoid convective weather hazards. The proposed approach is aimed at improving traffic manager decision-making in today's operational environment.by Diana Michalek Pfeil.Ph.D

Identification of Robust Terminal-Area Routes in Convective Weather

Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic flow management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the vertically integrated liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal area route will be blocked. Given a flight route through the terminal area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminalarea operations by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast to be blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, whereas the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materialize