An Optimization Model For The Us Air Traffic System

This book combines the research activities of the authors, both of whom are researchers at Ecole Nationale de l’Aviation Civile (French National School of Civil Aviation), and presents their findings from the last 15 years. Their work uses air transport as its focal point, within the realm of mathematical optimization, looking at real life problems and theoretical models in tandem, and the challenges that accompany studying both approaches. The authors’ research is linked with the attempt to reduce air space congestion in Western Europe, USA and, increasingly, Asia. They do this through studying stochastic optimization (particularly artificial evolution), the sectorization of airspace, route distribution and takeoff slots, and by modeling airspace congestion. Finally, the authors discuss their short, medium and long term research goals. They hope that their work, although related to air transport, will be applied to other fields, such is the transferable nature of mathematical optimization. At the same time, they intend to use other areas of research, such as approximation and statistics to complement their continued inquiry in their own field. Contents 1. Introduction. Part 1. Optimization and Artificial Evolution 2. Optimization: State of the Art. 3. Genetic Algorithms and Improvements. 4. A new concept for Genetic Algorithms based on Order Statistics. Part 2. Applications to Air Traffic Control 5. Air Traffic Control. 6. Contributions to Airspace Sectorization. 7. Contribution to Traffic Assignment. 8. Airspace Congestion Metrics. 9. Conclusion and Future Perspectives. About the Authors Daniel Delahaye works for Ecole Nationale de l’Aviation Civile (French National School of Civil Aviation) in France. Stéphane Puechmorel works for Ecole Nationale de l’Aviation Civile (French National School of Civil Aviation) in France.

Finally, we present three stochastic integer programming models for managing inbound air traffic flow of an airport, when there is adverse weather impacting the arrival capacity of the airport along with its arrival fixes. These are the first models, for optimizing ATFM decisions, which address uncertainty of future capacities of multiple NAS resources.

Dealing with a wide range of topics and covering different aspects of current importance in ATM, the papers place particular emphasis on automation and application of mathematical models and computational algorithms for ATM systems. The volume thus offers readers a summary of recent progress in such important areas as new operational concepts for automated ATM, evolution of traffic characteristics, ground-holding algorithms, ATC simulation facilities and a number of other aspects of ATC flow management.

The U.S. National Airspace System (NAS) is inherently highly stochastic. Yet, many existing decision-support tools for air traffic flow management take a deterministic approach to problem solving. This study aims to focus on the random and dynamic nature of flight departure delays to provide a more ac-curate picture of the airspace traffic situation, improve the prediction of the airspace congestion, and advance the level of decision making in aviation systems. Several models were proposed in this work based on the trends and patterns demonstrated by the delays. These models show reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. They could further advance the Enhanced Traffic Management System that is currently adopted by the Federal Aviation Administration. Mathematical algorithms used in this work can be adapted to similar pro-blems in other fields. The book is addressed to professionals and researchers in Air Transportations and Statistics.

In this thesis, we investigate the issue of optimizing the aircraft operators' demand with the airspace capacity by taking into account uncertainty in air traffic management. In the first part of the work, we identify the main causes of uncertainty of the trajectory prediction (TP), the core component underlying automation in ATM systems. We study the problem of online parameter-tuning of the TP during the climbing phase with the optimization algorithm CMA-ES. The main conclusion, corroborated by other works in the literature, is that ground TP is not sufficiently accurate nowadays to support fully automated safety-critical applications. Hence, with the current data sharing limitations, any centralized optimization system in Air Traffic Control should consider the human-in-the-loop factor, as well as other uncertainties. Consequently, in the second part of the thesis, we develop models and algorithms from a network global perspective and we describe a generic uncertainty model that captures flight trajectories uncertainties and infer their impact on the occupancy count of the Air Traffic Control sectors. This usual indicator quantifies coarsely the complexity managed by air traffic controllers in terms of number of flights. In the third part of the thesis, we formulate a variant of the Air Traffic Flow and Capacity Management problem in the tactical phase for bridging the gap between the network manager and air traffic controllers. The optimization problem consists in minimizing jointly the cost of delays and the cost of congestion while meeting sequencing constraints. In order to cope with the high dimensionality of the problem, evolutionary multi-objective optimization algorithms are used with an indirect representation and some greedy schedulers to optimize flight plans. An additional uncertainty model is added on top of the network model, allowing us to study the performances and the robustness of the proposed optimization algorithm when facing noisy context. We validate our approach on real-world and artificially densified instances obtained from the Central Flow Management Unit in Europe.

The Next Generation Air Transportation System's (NextGen) goal is the transformation of the U.S. national airspace system through programs and initiatives that could make it possible to shorten routes, navigate better around weather, save time and fuel, reduce delays, and improve capabilities for monitoring and managing of aircraft. A Review of the Next Generation Air Transportation provides an overview of NextGen and examines the technical activities, including human-system design and testing, organizational design, and other safety and human factor aspects of the system, that will be necessary to successfully transition current and planned modernization programs to the future system. This report assesses technical, cost, and schedule risk for the software development that will be necessary to achieve the expected benefits from a highly automated air traffic management system and the implications for ongoing modernization projects. The recommendations of this report will help the Federal Aviation Administration anticipate and respond to the challenges of implementing NextGen.