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Since the phasing-out of the High Density Rule, access to major commercial airports in the United States has been unconstrained or, in the case of the airports of New York, weakly constrained. This largely unregulated demand combined with capacity constraints led to record delay levels in 2007, whose costs were estimated as in excess of $30 billion a year. Mitigating airport congestion may be achieved through demand management measures. Quantifying the benefits of such measures requires careful modeling of flight delays as a function of flight schedules. This thesis applies a stochastic and dynamic queuing model to analyze operations at JFK and Newark (EWR), two of the most congested airports in the United States. Two models are used to approximate the dynamics of the queuing system: a numerical model called DELAYS and a new Monte Carlo simulation model, which combines time-varying stochastic models of demand and capacity. These two models are then calibrated and validated using historical records of operations. In particular, they provide estimates of the average throughput rate at JFK and EWR under different weather conditions. The models are then shown to predict accurately both the magnitude of the delays and their evolution over the course of a day of operations. In addition, the Monte Carlo simulation model evaluates reasonably well the variability of the delays between successive days of operations. These two models are then applied to a study of recent trends in scheduling and ontime performance at JFK and EWR. The analysis indicates that the significant delay reductions observed between 2007 and 2010 can be largely attributed to the relatively small reduction of airport demand over this period. In particular, it demonstrates the strongly nonlinear relationship between demand and delays when airports operate close to capacity. It also shows that, for a given daily number of flights, the more evenly they are distributed in a day, the lower the resulting delays are likely to be.
This book presents selected papers from the International Conference of Aerospace and Mechanical Engineering 2019 (AeroMech 2019), held at the Universiti Sains Malaysia's School of Aerospace Engineering. Sharing new innovations and discoveries concerning the Fourth Industrial Revolution (4IR), with a focus on 3D printing, big data analytics, Internet of Things, advanced human-machine interfaces, smart sensors and location detection technologies, it will appeal to mechanical and aerospace engineers.
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.
Increased air traffic demand over the past two decades has resulted in significant increases in surface congestion at major airports in the United States. The overall objective of this thesis is to mitigate the adverse effects of airport surface congestion, including increased taxi-out times, fuel burn, and emissions. The thesis tackles this objective in three steps: The first part deals with the analysis of departure operations and the characterization of airport capacity; the second part develops a new model of the departure process; and the third part of the thesis proposes and tests, both on the field and in simulations, algorithms for the control of the departure process. The characterization and estimation of airport capacity is essential for the successful management of congestion. This thesis proposes a new parametric method for estimating the departure capacity of a runway system, the most constrained element of most airports. The insights gained from the proposed technique are demonstrated through a case study of Boston Logan International Airport (BOS). Subsequently, the methodology is generalized to the study of interactions among the three main airports of the New York Metroplex, namely, John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR) and LaGuardia Airport (LGA). The individual capacities of the three airports are estimated, dependencies between their operations are identified, and the capacity of the Metroplex as a whole is characterized. The thesis also identifies opportunities for improving the operational capacity of the Metroplex without significant redesign of the airspace. The proposed methodology is finally used to assess the relationship between route availability during convective weather and the capacity of LGA. The second part of the thesis develops a novel analytical model of the departure process. The modeling procedure includes the estimation of unimpeded taxi-out time distributions, and the development of a stochastic and dynamic queuing model of the departure runway(s), based on the transient analysis of D(t)=Ek(t)=1 queuing systems. The parameters of the runway service process are estimated using operational data. Using the aircraft pushback schedule as input, the model predicts the expected runway schedule and the takeoff times. It also estimates the expected queuing delay and its variance for each light, along with the congestion level of the airport, sizes of the departure queues, and the departure throughput. The model is trained using data from EWR in 2011, and is subsequently used to predict taxi-out times at EWR in 2007 and 2010. The final part of this thesis proposes dynamic programming algorithms for controlling the departure process, given the current operating environment. These algorithms, called Pushback Rate Control protocols, predict the departure throughput of the airport, and recommend a rate at which to release pushbacks from the gate in order to control congestion. The thesis describes the design and field-testing of a variant of Pushback Rate Control at BOS in 2011, and the development of a decision-support tool for its implementation. The analysis shows that during 8 four-hour test periods, fuel use was reduced by an estimated 9 US tons (2,650 US gallons), and taxi-out times were reduced by an average of 5.3 min for the 144 flights that were held at the gate. The thesis concludes with simulations of the Pushback Rate Control protocol at Philadelphia International Airport (PHL), one of the most congested airports in the US, and a discussion of the potential benefits and implementation challenges.
The combination of air traffic growth and airport capacity limitations has resulted in significant congestion throughout the US National Airspace System, which imposes large costs on the airlines, passengers and society. Absent opportunities for capacity expansion, the mitigation of air traffic congestion requires improvements in (i) the utilization of airport capacity to enhance operating efficiency at the tactical level (i.e., over each day of operations), and/or (ii) the allocation of airport capacity to the airlines to limit over-capacity scheduling at the strategic level (i.e., months in advance of the day of operations). This thesis develops an integrated approach to airport congestion mitigation that jointly optimizes the utilization of airport capacity and the design of airport capacity allocation mechanisms. First, we focus on airport capacity utilization. We formulate an original Dynamic Programming model that optimizes, at the tactical level, the selection of runway configurations and the balancing of arrival and departure service rates to minimize congestion costs, for any given schedule of flights. The model integrates the stochasticity of airport operations into a dynamic decision-making framework. We implement exact and approximate Dynamic Programming algorithms that, in combination, enable the real-time implementation of the model. Results show that optimal policies are path-dependent, i.e., depend on prior decisions and on the stochastic evolution of the system, and that the model can reduce congestion costs, compared to advanced heuristics aimed to replicate typical decisions made in practice and to existing approaches based on deterministic queue dynamics. Second, we integrate the model of airport capacity utilization into a macroscopic queuing model of airport congestion. The resulting model quantifies the relationships between flight schedules, airport capacity and flight delays at the strategic level, while accounting for the way airport capacity utilization procedures can vary tactically to maximize operating efficiency. Results suggest that the model estimates the average departure queue lengths, the variability of departure queue lengths and the average arrival and departure delays at the three major airports in the New York Metroplex relatively well. The application of the model shows that the strong nonlinearities between flight schedules and flight delays observed in practice are captured by the model. Third, we develop an Integrated Capacity Utilization and Scheduling Model (ICUSM) that jointly optimizes scheduling interventions for airport capacity allocation at the strategic level and airport capacity utilization at the tactical level. Scheduling interventions start with a schedule of flights provided by the airlines, and reschedule a selected set of flights to reduce imbalances between demand and capacity, while minimizing interference with airline competitive scheduling. The ICUSM optimizes such interventions, while accounting for the impact of changes in flight schedules on airport operations. It relies on an original modeling architecture that integrates a Stochastic Queuing Model of airport congestion, our Dynamic Programming model of capacity utilization, and an Integer Programming model of scheduling interventions. We develop an iterative solution algorithm that converges in reasonable computational times. Results suggest that substantial delay reductions can be achieved at busy airports through limited changes in airline schedules. It is also shown that the proposed integrated approach to airport congestion mitigation performs significantly better than a typical sequential approach where scheduling and operating decisions are made separately. Last, we build upon the ICUSM to design, optimize and assess non-monetary mechanisms for scheduling interventions that ensure inter-airline equity and enable airline collaboration. Under the proposed mechanism, the airlines would provide their preferred schedules of flights, their network connections, and the relative scheduling flexibility of their flights to a central decision-maker, who may then consider scheduling adjustments to reduce anticipated delays. We develop a lexicographic architecture that optimizes such interventions based on efficiency (i.e., meeting airline scheduling preferences), equity (i.e., balancing scheduling adjustments fairly among the airlines), and on-time performance (i.e., mitigating airport congestion) objectives. Theoretical and computational results suggest that inter-airline equity can be achieved at no, or small, losses in efficiency, and that accounting for airline scheduling preferences can significantly improve the outcome of scheduling interventions.
The air transportation system is a significant "engine" of the U.S. economy providing rapid, safe, secure, affordable transportation over large geographic distances. Growth in passenger and cargo transportation demand (i.e. flights) in excess of the growth in air transportation capacity (i.e. runways, airspace sectors) has resulted in massive systemic delays. These delays are estimated in 2007 to have cost passengers up to $12 billion, and to have cost the airlines $19 billion in excess direct operating costs. With the current trend in rising fuel prices, the economic impact of these delays is expected to strain the U.S. economy even more. These delays also contribute to local air and water quality issues and to global climate change. Systematic solutions to address the imbalance between scheduled demand and forecast capacity include: (1) increasing capacity through the construction of new airports and additional runways at existing airports, (2) better utilization of existing capacity by increasing throughput productivity through advanced satellite-based navigation and 4-D trajectory planning, (3) demand management through administrative measures (such as the High Density Rule) and market-based mechanisms (such as congestion pricing and auctions of airport and airspace slots). Solutions 1 and 2 are capital intensive and require decades of planning and development. Solution 3 can be implemented rapidly but faces strong political opposition. In the absence of scheduling flights within the constraints of the capacity, flights arriving at an airport in excess of the airport arrival capacity are delayed until an arrival slot is available. Traditionally, flights that needed to be delayed were required to fly "holding patterns" above the airport until an arrival slot became available. To avoid these foreseen airborne holding delays, and to increase safety, the U.S. Air Traffic Control system runs a Ground Delay Program (GDP). The GDP holds the flights on the ground at their origin airports, allowing them to depart only when arrival slots will be available at the time the °ight is estimated to arrive at the constrained destination airport. Although the GDP was originally designed to manage reductions in capacity due to weather, over the last decade the GDP is routinely used to manage systemically over-scheduled arrivals. The GDP rations the available airport arrival capacity based on scheduled arrival times of flights (i.e. first-scheduled, first served). Special care is taken to equitably distribute delays between airlines. The Ration-by-Schedule approach is "airline flight-centric" and does not explicitly take into account passenger trip delays, fuel flow efficiency, and emissions. Previous research evaluated alternate rationing rules using airline-flight centric metrics. The objective of this research is to examine the impact of alternative GDP rationing rules on the performance and equity to airlines and passengers. The hypothesis is that alternate GDP rationing rules can maximize the mutual interests of both airlines and passengers. This dissertation describes the GDP Rationing Rule Simulator (GDP-RRS) that was developed to evaluate alternate rationing rules. The dissertation also describes the results of three experiments conducted for flights affected by GDPs in 2007 for arrivals at the three New York Metroplex airports (Newark Liberty (EWR), LaGuardia (LGA) and John F. Kennedy (JFK) airports). The first experiment compared the performance and equity of five alternate rationing rules to the Ration-by-Schedule rationing rule. The second experiment evaluated the impact of substitution strategies in the GDP rationing rules. The third experiment investigated the impact of GDP scope on performance and equity for airlines and passengers. The major findings of the research are: It is not possible to maximize the mutual interests of airlines and passengers. There exists a tradeoff between GDP performance and equity (see below). When only performance is considered (and equity for both airlines and passengers are ignored), the best rationing rule is Ration-by-Passengers. This rule maximizes passenger throughput. Passengers experience a reduction in passenger delays of 23% at EWR, 20% at LGA, 15% at JFK relative to the Ration-by-Schedule rule. Airlines experience savings of 57% fuel burn at EWR, 63% at LGA, 42% at JFK relative to the Ration-by-Schedule rule. When only equity due to flight and passenger delays are considered (and performance of both airlines and passengers are ignored), the rule that provides the best equity is Ration-by-Schedule. When performance and equity of flight delays for airlines are considered (and performance and equity for passengers are ignored), the rules that provide the best performance differs by airport: Ration-by-Passengers at EWR, Ration-by-Aircraft Size at LGA, and Ration-by-Distance at JFK. When performance and equity for passengers are considered (and performance and equity for airlines are ignored), the rules that provide the best performance differ by airport: Ration-by-Distance at EWR and LGA and Ration-by-Passengers or Ration- by-Fuel Flow High Precedence at JFK. When performance and equity for both airlines and passengers are considered, the rules that provide the best performance and equity differs by airport: Ration-by- Distance at EWR, Ration-by-Aircraft Size at LGA, and Ration-by-Passengers at JFK. Airline equity is determined by the flight schedule (i.e. position of flights throughout the day) and the aircraft type (i.e. fleet mix). Passenger equity is determined by the flight cancellations. Airlines with a small number of operations and airports with a small number of enplanements, experience disproportional performance and equity penalties. Airline substitution strategies do not change the relative performance and equity of the alternate rationing rules. Changes in GDP scopes do not change the relative performance and equity of the alternate rationing rules. Scope is the distance range of the GDP. The selection of the GDP rationing rule requires the unambiguous definition of the National Air Transportation System objectives (and the weights for the performance and equity). The relative weighting of objectives is a social and political activity. The application of alternate GDP rationing rules has broader implications. GDP rationing rules create priority queues which give preference to the compliant flights. As a consequence the rationing rules incentivize airline behavior related to scheduling and fleet mix. For example, the Ration-by-Passengers rule could, in the long-run, result in the migration of airline fleets to larger sized aircraft that would increase the passenger flow capacity. This would improve the efficiency of the air transportation system. This incentive would result in an increase in aircraft size, which would lead to reduced frequency, which would yield lower delays
Modelling and Managing Airport Performance provides an integrated view of state-of-the-art research on measuring and improving the performance of airport systems with consideration of both airside and landside operations. The considered facets of performance include capacity, delays, economic costs, noise, emissions and safety. Several of the contributions also examine policies for managing congestion and allocating sparse capacity, as well as for mitigating the externalities of noise, emissions, and safety/risk. Key features: Provides a global perspective with contributing authors from Europe, North and South America with backgrounds in academia, research institutions, government, and industry Contributes to the definition, interpretation, and shared understanding of airport performance measures and related concepts Considers a broad range of measures that quantify operational and environmental performance, as well as safety and risk Discusses concepts and strategies for dealing with the management of airport performance Presents state-of-the-art modelling capabilities and identifies future modelling needs Themed around 3 sections – Modelling Airport Performance, Assessing Airport Impacts, and Managing Airport Performance and Congestion Modelling and Managing Airport Performance is a valuable reference for researchers and practitioners in the global air transportation community.
"TRB's Airport Cooperative Research Program (ACRP) Report 104: Defining and Measuring Aircraft Delay and Airport Capacity Thresholds offers guidance to help airports understand, select, calculate, and report measures of delay and capacity. The report describes common metrics, identifies data sources, recommends metrics based on an airport's needs, and suggests ways to potentially improve metrics."--Publisher's description.
This book aims to provide comprehensive coverage of the field of air transportation, giving attention to all major aspects, such as aviation regulation, economics, management and strategy. The book approaches aviation as an interrelated economic system and in so doing presents the “big picture” of aviation in the market economy. It explains the linkages between domains such as politics, society, technology, economy, ecology, regulation and how these influence each other. Examples of airports and airlines, and case studies in each chapter support the application-oriented approach. Students and researchers in business administration with a focus on the aviation industry, as well as professionals in the industry looking to refresh or broaden their knowledge of the field will benefit from this book.