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Author: Diana Michalek Pfeil
Publisher:
ISBN:
Category :
Languages : en
Pages : 158
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Book Description
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.
Author: Diana Michalek Pfeil
Publisher:
ISBN:
Category :
Languages : en
Pages : 158
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Book Description
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.
Author: Ian S. Kincaid
Publisher: Transportation Research Board
ISBN: 030925857X
Category : Transportation
Languages : en
Pages : 147
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Book Description
This report provides a guidebook on how to develop air traffic forecasts in the face of a broad range of uncertainties. It is targeted at airport operators, planners, designers, and other stakeholders involved in planning, managing, and financing of airports, and it provides a systems analysis methodology that augments standard master planning and strategic planning approaches. This methodology includes a set of tools for improving the understanding and application of risk and uncertainty in air traffic forecasts as well as for increasing overall effectiveness of airport planning and decision making. In developing the guidebook, the research team studied existing methods used in traditional master planning as well as methods that directly address risk and uncertainty, and based on that fundamental research, they created a straightforward and transparent systems analysis methodology for expanding and improving traditional planning practices, applicable through a wide range of airport sizes. The methods presented were tested through a series of case study applications that also helped to identify additional opportunities for future research and long-term enhancements.
Author: Michael Joseph Frankovich
Publisher:
ISBN:
Category :
Languages : en
Pages : 140
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Book Description
The cost of air traffic delays is well documented, and furthermore, it is known that the significant proportion of delays is incurred at airports. Much of the air traffic flow management literature focuses on traffic flows between airports in a network, and when studies have focused on optimizing airport operations, they have focused largely on a single aspect at a time. In this thesis, we fill an important gap in the literature by proposing unified approaches, on both strategic and tactical levels, to optimizing the traffic flowing through an airport. In particular, we consider the entirety of key problems faced at an airport: a) selecting a runway configuration sequence; b) determining the balance of arrivals and departures to be served; c) assigning flights to runways and determining their sequence; d) determining the gate-holding duration of departures and speedcontrol of arrivals; and e) routing flights to their assigned runway and onwards within the terminal area. In the first part, we propose an optimization approach to solve in a unified manner the strategic problems (a) and (b) above, which are addressed manually today, despite their importance. We extend the model to consider a group of neighboring airports where operations at different airports impact each other due to shared airspace. We then consider a more tactical, flight-by-flight, level of optimization, and present a novel approach to optimizing the entire Airport Operations Optimization Problem, made up of subproblems (a) - (e) above. Until present, these have been studied mainly in isolation, but we present a framework which is both unified and tractable, allowing the possibility of system-optimal solutions in a practical amount of time. Finally, we extend the models to consider the key uncertainties in a practical implementation of our methodologies, using robust and stochastic optimization. Notable uncertainties are the availability of runways for use, and flights' earliest possible touchdown/takeoff times. We then analyze the inherent trade-off between robustness and optimality. Computational experience using historic and manufactured datasets demonstrates that our approaches are computationally tractable in a practical sense, and could result in cost benefits of at least 10% over current practice.
Author: David A. Powner
Publisher: DIANE Publishing
ISBN: 1437941168
Category : Nature
Languages : en
Pages : 35
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Book Description
This is a print on demand edition of a hard to find publication. The National Weather Service's (NWS) weather products are a vital component of the FAA¿s air traffic control system. In addition to providing aviation weather products developed at its own facilities, NWS also provides on-site staff at each of FAA's en route centers -- the facilities that control high-altitude flight outside the airport tower and terminal areas. NWS and FAA have been exploring options for improving the aviation weather services provided at en route centers. This report: (1) determines the status of the agencies' efforts to restructure aviation weather services; (2) assesses the agencies' progress in establishing performance baselines in order to measure the effect of any changes; and (3) evaluates plans to address key challenges. Ill.
Author: Electronic Navigation Research Institute
Publisher: Springer
ISBN: 4431564233
Category : Technology & Engineering
Languages : en
Pages : 252
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Book Description
This book is a compilation of selected papers from the 4th ENRI International Workshop on ATM/CNS (EIWAC2015). The work focuses on novel techniques for aviation infrastructure in air traffic management (ATM) and communications, navigation, surveillance, and informatics (CNSI) domains. The contents make valuable contributions to academic researchers, engineers in the industry, and regulators of aviation authorities. As well, readers will encounter new ideas for realizing a more efficient and safer aviation system.
Author: Heng Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Annual U.S. air travel demand has been growing steadily by 4-5% over the last decade, and it is estimated that the demand will nearly double in the next twenty years. It has also been estimated by the International Civil Aviation Organization that global demand for commercial aircraft will increase at an average annual rate of 4.1% by 2034 (IATA, 2014). However, airport expansions and aviation infrastructure upgrades have not kept pace with the increase in air traffic demand, as only 3% of all the new airport projects around the world are planned in the U.S. (CAPA, 2015). Thus, the operation rates at existing airports are likely to increase significantly, implying a greater need to increase the utilization of currently available runway capacity. With steadily increasing demand in air traffic and limited airport capacity, delay in air traffic is ubiquitous. Approximately 25% of flights experience delays of at least 15 minutes each year, resulting in significant passenger service issues and costs to airlines and society in general. Delays constitute the top service complaint for airlines, which has implications for the society as a whole - both economically and environmentally. Flight delays also increase airline costs directly, due to associated additional fuel, crew and maintenance costs. Recent studies show that the estimated cost of air transportation delay to the American economy ranges from $32.9 billion to $41 billion a year, of which, $8 billion are direct costs to airlines (Ball et al., 2010; Ferguson et al., 2013). Noting that more than 60% of delay is due to airport operations (Balakrishna et al., 2010), this thesis aims at helping reduce delay through better management of arrival and departure operations at airports, which can create relevant and significant value for the airlines and for the society. Arrival and departure operations inherently involve significant uncertainty. When an aircraft is approaching the runway, many factors affect its trajectory, such as weather, wind conditions, pilot behavior, aircraft weight, as well as the differences in types of aircraft and flight management systems. When an aircraft arrives at the gate, operating conditions, such as unplanned security checks, varied durations of deplaning and boarding, as well as the maintenance and fueling involved, could contribute to variations of actual departure time for the next flight. All of these stochastic factors involve uncertainty and they need to be taken into account while making operational decisions. On the other hand, stochastic treatment of such operational problems has not been common in the literature due to difficulties associated with the characterization of uncertainty and the computational tractability. I argue in this thesis that, with recent advances in computing power and data analysis tools, such stochastic treatments are more amenable for practical use. To this end, I study four novel operational problems related to flight arrivals and departures at airports under the uncertainty of operating conditions, and demonstrate the potential value that can be generated through stochastic models within the context of airline and airport operations. The problems I study involve both strategic and tactical decisions for airline service improvement and cost reduction. The first two problems consider managing arrival operations at airports, while the last two problems focus on departure operations. In the first and second problems, I focus on arrival operations in the context of optimized profile descent (OPD), which is a novel arrival procedure for the Next Generation Air Transportation System. In the first problem, I identify policies for managing arrival operations at the tactical level by developing a stochastic dynamic programming framework to manage the sequencing and separation of flights. I find that simple calculation based measures can be used as optimal decision rules during such operations, and that the expected annual savings can be around $29 million if such implementations are adapted by major airports in the U.S. Of these savings, $24 million are direct savings for airlines due to reduced fuel usage, corresponding to a potential savings of 10-15% in fuel consumption over current practice. I also find that optimal spacing of OPD flights is much more important than optimal sequencing of these flights. Furthermore, there is not much difference between the environmental costs of fuel-optimal and sustainably-optimal spacing policies. Hence, an airline-centric approach in improving OPD operations is likely to be not in conflict with objectives that might be prioritized by other stakeholders. In the second problem, I study the optimal design of arrival traffic management systems at airports at the strategic level. I claim that implementation of OPD operations requires effective metering configurations at airports due to the increased role of uncertainty in aircraft trajectories during descent. I develop stochastic models to further increase the value of OPD operations over conventional arrival procedures by optimizing metering point configurations, which include identification of the optimal number and locations of metering points to use. I provide numerical results based on actual traffic information at major U.S. airports, which indicate that the total potential savings in the top ten major airports could be up to $22 million per year if the proposed policies are implemented. I also find that the optimal metering configurations are mostly robust under different operating conditions. In addition, my results suggest that early spacing adjustments near the top of descent (TOD) are of more value for larger volumes of air traffic. In the third and fourth problems, I study optimal departure operations at airports under the context of departure metering, which is an airport surface management procedure that limits the number of aircraft on the runway by holding aircraft at a predesigned metering area. More specifically, in the third problem, I develop a stochastic dynamic programming framework for tactical management of pushback operations at gates and for determining the optimal number of aircraft to be directed to the runway from the metering areas. I introduce four easy-to-implement practical departure metering policies and implement a comparative analysis between these practical policies and the optimal numerical solutions. I also implement sensitivity analysis of the departure metering policies over state variable values. In the fourth problem, I study the optimal metering area capacity at the strategic level. Building on the dynamic programming framework mentioned in the third problem, I identify the optimal metering area capacity using marginal analysis to minimize expected overall costs. Numerical simulations are implemented and potential savings are identified for sample U.S. airports based on varying capacity levels. The optimal metering area capacity is then determined based on the numerical implementations to further improve overall efficiency and sustainability of departure operations. I also analyze the benefits to airlines in terms of annual savings due to such policies, and find that the annual savings could be $31 million if the optimal departure metering policies are implemented at the top ten major airports in the U.S. Overall, as one of the few studies on stochasticity in arrival and departure operations, I derive both tactical and strategic policies to improve efficiency and sustainability for airlines and the society, which can enhance service quality and strengthen market position for the airlines involved.
Author: Yi-Hsin Lin (S.M.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
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Book Description
As demand for air transportation grows, the existing air traffic control system is being pushed to capacity. This is especially true during weather events. However, the degree to which weather impacts airspace capacity, particularly within the terminal region, is not well understood. Understanding how weather impacts terminal area capacity will be important for quantifying the uncertainty inherent in weather forecasting and developing an optimal mitigation strategy. In this thesis, we identify and analyze operational features that may impact whether a pilot chooses to fly through severe weather. In doing so we build upon the work done at MIT Lincoln Laboratory on terminal area Weather Avoidance Fields (WAF) for arriving aircraft. This model predicts the probability of pilot deviation around weather, based solely on weather features. The terminal area WAF was calibrated based on historical pilot behavior during weather encounters near the destination airport. Our model extends the WAF by incorporating operational factors such as prior delays and existing congestion in the terminal airspace. Instead of predicting the probability of deviation, our model will predict the maximum WAF level penetrated by the pilot, using the operational features as input. The thesis combines predictive modeling with case studies to identify relevant features and determine their predictive skill. An understanding of how operational factors impact weather avoidance will allow researchers to better quantify weather forecasting uncertainty and to understand when precision in forecasting is important. In turn, this will improve our ability to find optimal strategies for delay mitigation.
Author: Gurkaran Buxi
Publisher:
ISBN:
Category :
Languages : en
Pages : 178
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Book Description
Airport arrival capacity, referred to here as the airport acceptance rate (AAR), is strongly influenced by the weather in the vicinity of the airport and thus AAR prediction necessitates an airport-specific weather forecast. Weather forecasts, however, are seldom accurate in predicting the actual weather conditions. Strategic decisions, for example arrival rates in a ground delay program (GDP), must be made ahead of time, usually more than two hours, when there is an uncertainty about the future capacity. This research uses probabilistic capacity scenarios to represent the uncertainty in the future arrival capacity. A probabilistic capacity scenario is defined as a time series of AAR values with which a certain probability of realization is associated. A set of probabilistic capacity scenarios may be used to represent the uncertainty in arrival capacity at an airport over the course of the day. There has been considerable research in developing GDP models that determine efficient ground delay decisions and require probabilistic capacity scenarios as inputs. It is assumed that the capacity scenarios can be developed from weather forecasts or can be obtained from the expertise of the air traffic managers. There is, however, considerably less literature on the development of specific day-of-operation probabilistic capacity scenarios from weather forecasts. This limits the use of these GDP models in real- world application. This thesis fills that gap and presents methodologies to generate probabilistic capacity scenarios from weather forecasts. In this thesis we develop methodologies for generating probabilistic capacity scenarios using a widely available airport-specific weather forecast called the Terminal Aerodrome Forecast (TAF). These methodologies require the issued TAF forecast and the realized capacity for days in the past. We apply and assess the performance of these methodologies on four US airports: San Francisco International Airport, Boston Logan International Airport, Chicago O'Hare International Airport and Los Angeles International Airports. Though we have focused on these airports as case studies, the TAF-based scenario generation techniques can be applied to any airport. In the first methodology, TAF Clustering, the scenarios are representative capacity profiles for days having similar TAFs. Groups of similar TAFs are found using K-means clustering and the number is verified using Silhouette value. In the second methodology, Dynamic Time Warping (DTW) Scenarios, the scenarios are the actual realized capacity profiles for days that have similar TAFs. The similarity between TAFs is determined using a statistical technique for comparing multidimensional time series called DTW. DTW Scenarios uses three airport specific input parameters. These parameters control the numbers and the probabilities of the scenarios. We determine the values of the parameters through optimization to maximize the performance of the scenarios through minimizing average delay costs. The optimal values are determined through a specialized algorithm designed for situations where evaluating the objective function is computationally expensive. For San Francisco International Airport we also use another forecast: the San Francisco Marine Initiative forecast (STRATUS) to develop the scenarios. In this methodology called, Fog burn-off clustering, the scenarios are representative capacity profiles for days that have the fog burn-off time in the same quarter hour. We measure the efficacy of the various scenario generation methodologies in a real world setting based on 45 historic days for each of the four case-study airports. For each day, the generated scenarios are provided as inputs to a static stochastic ground delay model (SSGDM) that determines the series of planned arrival rates that minimize the sum of ground delay costs and expected air delay costs, assuming that the plan is not adjusted to evolving information. The ground delay is determined directly from the SSGDM whereas the realized air delay is determined from a queuing diagram based on the planned arrival rate and the realized arrival capacity. The realized delay costs are averaged over 45 days for each airport, and is the metric used to compare the different scenario generation methodologies. Employing this approach, we compare the different methods for capacity scenario generation against each other and against two other reference cases. Under the first reference case, Naïve Clustering, the scenarios are developed from historical capacity data without the use of the weather forecast. Groups of similar arrival profiles are determined though K-means clustering. In the second reference case, Perfect Information, we assume that the GDP is planned based on perfect information about the future arrival capacity. Our results show that, on average, scenarios generated using the TAF-based DTW method results in the lowest delay cost amongst all scenario based methodologies. It is shown that capacity scenarios generated using day-of-operation weather forecasts can reduce the cost of delays by 5%-30% compared to scenarios that do not make use of weather forecast. The benefit of the TAF based approach is more pronounced on days that have a greater capacity-demand imbalance when compared to Naïve Clustering.
Author: Cynthia Barnhart
Publisher: Elsevier
ISBN: 0080467431
Category : Psychology
Languages : en
Pages : 796
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Book Description
This book contains eleven chapters describing some of the most recent methodological operations research developments in transportation. It is structured around the main transportation modes, and each chapter is written by a group of well-recognized researchers. Because of the major impact of operations research methods in the field of air transportation over the past forty years, it is befitting to open the book with a chapter on airline operations management. This book will prove useful to researchers, students, and practitioners in transportation and will stimulate further research in this rich and fascinating area. Volume 14 examines transport and its relationship with operations and management science 11 chapters cover the most recent research developments in transportation Focuses on main transportation modes-air travel, automobile, public transit, maritime transport, and more
Author: Prabhu Manyem
Publisher: Cambridge Scholars Publishing
ISBN: 152756925X
Category : Mathematics
Languages : en
Pages : 149
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Book Description
Disruptions to commercial airline schedules are frequent and can inflict significant costs. This is the first book to treat the optimisation of disruption management (irregular operations) in air traffic from a common good perspective that addresses the concerns of airlines, airports, air traffic service providers and, most importantly of all, the travelling public. It describes a number of disruption management systems which are already in place at air traffic service (ATC) providers such as Airservices Australia. As such, the book will be of immense value to ATC providers, and will serve as a reference point for planners at airline operations centres who control movements within a 48-hour window. The optimisation techniques described here will also be very useful to academics and postgraduate students in civil aviation and operations research.
