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This volume is a compendium of papers presented during the International Workshop on Air Traffic Management, which took place in Capri, Italy, on September 26-30, 1999. The workshop was organized by Italian National Research Council in co-operation with the University of Rome "Tor Vergata", and the Massachusetts Institute of Technology (MIT). This was the fifth in a series of meetings held periodically over a ten-year span for the purpose of encouraging an exchange of views and fmdings by scientists in the field of Air Traffic Management (A TM). The papers presented at the workshop dealt with a wide range of topics and covered different aspects that are currently important in Air Traffic Control and Air Traffic Management. This volume contains only a subset of the papers presented, namely the ones that addressed the main area emphasis in the workshop, new concepts and methods. The subject of the first two papers is Collaborative Decision Making (CDM), a concept which embodies, to a large extent, the new philosophy of partial decentralization and increased delegation of responsibilities to users in A TM operations. In the first of these papers Wambsganss describes the original CDM project and its initial implementation in the form of the Ground Delay Program Enhancements. He also provides a brief description of some of the tools that have been developed as part of the CDM effort and identifies future research and development requirements.
We propose a tractable optimization framework for network Air Traffic Flow Management (ATFM) with an eye towards the future. The thesis addresses two issues in ATFM research: a) fairness and collaboration amongst airlines; and b) uncertainty inherent in capacity forecasts. A unifying attraction of the overall dissertation is that the Collaborative Decision-Making (CDM) paradigm, which is the current philosophy governing the design of new ATFM initiatives, is treated as the starting point in the research agenda. In the first part of the thesis, we develop an optimization framework to extend the CDM paradigm from a single-airport to a network setting by incorporating both fairness and airline collaboration. We introduce different notions of fairness emanating from a) First-Scheduled First-Served (FSFS) fairness; and b) Proportional fairness. We propose exact discrete optimization models to incorporate them. The first fairness paradigm which entails controlling number of reversals and total amount of overtaking is especially appealing in the ATFM context as it is a natural extension of Ration-By-Schedule (RBS). We allow for further airline collaboration by proposing discrete optimization models for slot reallocation. We provide empirical results of the proposed optimization models on national-scale, real world datasets that show interesting tradeoffs between fairness and efficiency. In particular, schedules close to the RBS policy (with single digit reversals) are possible for a less than 10% increase in delay costs. We utilize case studies to highlight the considerable improvements in the internal objective functions of the airlines as a result of slot exchanges. Finally, the proposed models are computationally tractable (running times of less than 30 minutes). In the second part, we address the important issue of capacity uncertainty by presenting the first application of robust and adaptive optimization in the ATFM problem. We introduce a weather-front based approach to model the uncertainty inherent in airspace capacity estimates resulting from the impact of a small number of weather fronts. We prove the equivalence of the robust problem to a modified instance of the deterministic problem; solve the LP relaxation of the adaptive problem using affine policies; and report extensive empirical results to study the inherent tradeoffs.
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.
A concise resource to the best practices and problem-solving ideas for understanding the airline network planning and scheduling process Airline Network Planning and Scheduling offers a comprehensive resource that is filled with the industry's best practices that can help to inform decision-modeling and the problem-solving process. Written by two industry experts, the book is designed to be an accessible guide that contains information for addressing complex challenges, problems, and approaches that arise on the job. The chapters begin by addressing the complex topics at a broad, conceptual level before moving on to more detailed modeling in later chapters. This approach follows the standard airline planning process and reflects the duties of the day-to-day job of network/schedule planners. To help gain a practical understanding of the information presented, each chapter includes exercises and data based on real-world case studies. In addition, throughout the book there are graphs and illustrations as well as, information on the most recent advances in airline network and planning research. This important resource: Takes a practical approach when detailing airline network planning and scheduling practices as opposed to a theoretical perspective Puts the focus on the complexity and main challenges as well as current practices and approaches to problem-solving and decision-making Presents the information in a logical sequence that begins with broad, conceptual topics and gradually delves into more advanced topics that address modeling Contains international standard airline planning processes, the day-to-day responsibilities of the job, and outlines the steps taken when building an airline network and schedule Includes numerous case studies, exercises, graphs, and illustrations throughout Written for professionals and academics, Airline Network Planning and Scheduling offers a resource for understanding best practices and models as well as the challenges involved with network planning and scheduling.
In this thesis, we develop multi-resource integer optimization formulations for coordinating Traffic Flow Management (TFM) programs with equity considerations. Our multi-resource approaches ignore aircraft connectivity between flights, but allow a single flight to utilize multiple capacity-controlled resources. For example, when both Ground Delay Programs (GDPs) and Airspace Flow Programs (AFPs) are simultaneously in effect, a single flight may be impacted by a GDP and one or more AFPs. We show that due to the similarity with current practice, our models can be applied directly in the current Collaborative Decision-Making (CDM) environment. In the first part of the thesis, we develop these formulations as extensions of a well-studied, existing nationwide TFM formulation and compare them to approaches utilized in practice. In order to make these comparisons, we first develop a metric, Time-Order Deviation, for evaluating schedule fairness in the multi-resource setting. We use this metric to compare approaches in terms of both schedule fairness and allocated flight delays. Using historical scenarios derived from 2007 data, we show that, even with limited interaction between TFM programs, our Ration-by-Schedule Exponential Penalty model can improve the utilization of air transportation system resources. Skipping ahead, in the last part of the thesis, we develop a three-stage sequential evaluation procedure in order to analyze the TFM allocation process in the context of a dynamic CDM environment. To perform this evaluation we develop an optimization-based airline disruption response model, which utilizes passenger itinerary data to approximate the underlying airline objective, resulting in estimated flight cancellations and aircraft swaps between flight legs. Using this three-stage sequential evaluation procedure, we show that the benefits of an optimization-based allocation are likely overstated based on a simple flight-level analysis. The difference between these results and those in the first part of the thesis suggests the importance of the multi-stage evaluation procedure. Our results also suggest that there may be significant benefits to incorporating aircraft flow balance considerations into the Federal Aviation Administration's (FAA's) TFM allocation procedures. The passenger itinerary data required for the airline disruption response model in the last part of the thesis are not publicly available, thus in the second part of the thesis, we develop a method for modeling passenger travel and delays. In our approach for estimating historical passenger travel, we develop a discrete choice model trained on one quarter of proprietary booking data to disaggregate publicly available passenger demand. Additionally, we extend a network-based heuristic for calculating passenger delays to estimate historical passenger delays for 2007. To demonstrate the value in this approach, we investigate how passenger delays are affected by various features of the itinerary, such as carrier and time of travel. Beyond its applications in this thesis, we believe the estimated passenger itinerary data will have broad applicability, allowing a passenger-centric focus to be incorporated in many facets of air transportation research. To facilitate these endeavors, we have publicly shared our estimated passenger itinerary data for 2007.
As demand for air travel increases over the years and many busy airports operate close to their capacity limits, congestion at some airports on any given day can quickly spread throughout the National Aviation System (NAS). It is therefore increasingly important to study the operation of large networks of airports as a group and to understand better the interactions among them under a wide range of conditions. This thesis develops a fundamental tool for this purpose, enhances it with several capabilities designed to address issues of particular interest, and presents some early insights and observations on the system-wide impacts of various scenarios of network-wide scope. We first describe an analytical queuing and network decomposition model for the study of delays and delay propagation in a large network of airports. The Airport Network Delays (AND) model aims to bridge the gap in the existing modeling tools between micro-simulations that track aircraft itineraries, but require extensive resources and computational effort, and macroscopic models that are simple to use, but typically lack aircraft itinerary tracking capabilities and credible queuing models of airport congestion. AND operates by iterating between its two main components: a queuing engine (QE), which is a stochastic and dynamic queuing model that treats each airport in the network as a M(t)/Ek(t)/1 queuing system and is used to compute delays at individual airports and a delay propagation algorithm (DPA) that updates flight schedules and demand rates at all the airports in the model in response to the local delays computed by the QE. We apply AND to two networks, one consisting of the 34 busiest airports in the United States and the other of the 19 busiest in Europe. As part of the development of AND, we perform a statistical analysis of the minimum ground turn-around times of aircraft, one of the fundamental variables that determine delay propagation. In addition, we show that the QE, with proper calibration, can model very accurately the airport departure process, predicting delays at two major US airports within 10% of observed values. We also validate the AND model on a network-wide scale against field data reported by the FAA. Finally, we present insights into the complex interactions through which delays propagate through a network of airports and the often-counterintuitive consequences. In the third part of the thesis, we present two important extensions of the AND model designed to expand its usability and applicability. First, in order to provide a more accurate representation of NAS operations, we develop an algorithm that replicates quite accurately the execution of Ground Delay Programs (GDPs). The algorithm operates consistently with the rules of the Collaborative Decision-Making (CDM) process under which GDPs are currently conducted in the United States. The second extension is the implementation in AND of a deterministic queuing engine (D(t)/D(t)/1) which can be used as an alternative to the original stochastic QE. This deterministic model can be used to study delay-related performance in a future system that operates at a higher level of predictability than the current one, as the one envisioned by FAA in the Next Generation Air Transportation System. In the final part of the thesis we describe a Mixed Integer optimization model for studying the impact of introducing slot controls at busy airports. The model generates new flight schedules at airports by reducing the number of available slots, while respecting all existing aircraft itineraries and preserving all passenger connections. We test the model at Newark Airport (EWR) and conclude that, with a small schedule displacement (less than 30 minutes for any flight during the day), it is possible to obtain a feasible schedule that obeys slot limits that are as low as the IFR capacity of the airport. We test the new schedule in AND and find that the local delay savings that would result from "slot-controlling" EWR in this way are of the order of 10% for arrivals and of 50% for departures, while we may also expect a reduction of 23% in propagated delays to the rest of the US network of airports.