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Minimizing the pain in air transportation: Analysis of performance and equity in ground delay programs.
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 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.
Ground Delay Programs (GDP) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may result in optimal outcomes for passengers -- especially with connecting itineraries. This paper proposes an original passenger-centric approach to GDP by leveraging data on passenger itineraries in flight networks. First, we identify analytical drivers of passenger-centric operations in transportation systems. Second, we develop an integer program that balances flight delays and passenger delays in large-scale GDP operations. A rolling procedure decomposes the problem while ensuring global feasibility, enabling the model's implementation in short computational times. Third, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly, enabling the model's implementation when passenger itineraries are unknown by air traffic managers. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs, and that these benefits are robust to imperfect knowledge of passenger itineraries. Results highlight two major levers of passenger-centric operations: (i) delay allocation (which flights to delay vs. prioritize), and (ii) delay introduction (whether to deliberately hold flights to avoid passenger misconnections).
The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. Kulkarni, Deepak and Wang, Yao and Sridhar, Banavar Ames Research Center NASA/TM-2013-216558, ARC-E-DAA-TN10153
Infrastructure capacity investment has been traditionally viewed as an important means to mitigate congestion and delay in the air transportation system. Given the huge amount of cost involved, justifying the benefit returns is of critical importance when making investment decisions. This dissertation proposes an equilibrium-based benefit assessment framework for aviation infrastructure capacity investment. This framework takes into consideration the interplays among key system components, including flight delay, passenger demand, flight traffic, airline cost, and airfare, and their responses to infrastructure capacity investment. We explicitly account for the impact of service quantity changes on benefit assessment. Greater service quantity is associated with two positive feedback effects: the so-called Mohring effect and economies of link/segment density. On the other hand, greater service quantity results in diseconomies of density at nodes/airports, because higher traffic density at the airport leads to greater airport delays. The capacity-constrained system equilibrium is derived from those competing forces. Two approaches are developed to investigate air transport system equilibrium and its shift in response to infrastructure capacity expansion. In Chapter 2, we first view the system equilibrium from the airline competition perspective. We model airlines' gaming behavior for airfare and frequency in duopoly markets, assuming that airlines have the knowledge of individuals' utility structure while making decisions, and that delay negatively affects individuals' utility and increases airline operating cost. The theoretical airline competition model developed in Chapter 2 provides analytical insights into the interactions among various system components. Under a symmetric Nash equilibrium, we find that the presence of flight delay increases passenger generalized cost and discourages air travel. Airlines would not pass delay cost entirely onto passengers through higher fare, but also account for the impact of service degradation on passenger willingness-to-pay and consequently passenger demand. To avoid exorbitant flight delays, airlines would use larger aircraft, meanwhile taking advantage of economies of aircraft size. The resulting unit cost reduction partially offsets operating delay cost increase. The equilibrium shift triggered by capacity expansion reduces both schedule delay and flight delay, leading to lower passenger generalized cost and higher demand, despite slightly increased airfare. Airlines will receive larger profit, and consumer welfare will increase, as a result of the expansion. Although delay reduction is less than expected because of induced demand, the overall benefit, which encompasses reduction in both schedule delay and flight delay, would be much greater than estimated from a purely delay-based standpoint. The equilibrium analysis can be alternatively approached from a traveler-centric perspective. The premise of an air transport user (i.e. traveler) equilibrium is that each traveler in the air transportation system maximizes his/her utility when making travel decisions. The utility depends upon market supply and performance characteristics, consisting of airfare, flight frequency, and flight delay. The extent of airline competition is implicitly reflected in the determination of airfare and flight frequency. Given the limited empirical evidence of the delay effect on air transportation system supply, two econometric models for airfare and flight frequency are estimated in Chapter 3. We find positive delay effect on fare, which should be interpreted as the net effect of airlines' tendency to pass delay cost to passengers while also compensating for service quality degradation. Higher delay discourages carriers from scheduling more flights on a segment. Both delay effects, however, are relatively small. The estimated fare and frequency models, together with passenger demand and airport delay models presented in Chapter 4, are integrated to formulate the air transport user equilibrium as fixed point and variational inequality problems. We prove that the equilibrium existence is guaranteed; whereas equilibrium uniqueness cannot be guaranteed. We apply the user equilibrium to a fully connected, hypothetical network with the co-existence of direct and connecting air services. Using a simple, heuristic algorithm, we find that the equilibrium is insensitive to initial demand values, suggesting that there may be a single equilibrium for this particular model instance. Hub capacity investment attracts spoke-spoke passengers from non-stop routes, and generates new demand on hub-related routes. At the market level, hub capacity expansion would result in greater total demand and consequently passenger benefits in almost all markets--except for ones where a predominant portion of passengers choose non-stop routes due to extremely high circuity for one-stop travel. In the latter set of markets, after capacity expansion passenger demand and benefits would be both reduced. This counter-intuitive result carries important implications that capacity increase does not necessarily benefit everyone in the system. Similar to the findings from the airline competition model, with changes in flight delay, schedule delay, airfare, and total demand, the user equilibrium model yields much higher passenger benefits from capacity investment than the conventional method; whereas hub delay saving is offset by traffic diversion and induced demand. With continuous capacity investment, the air transportation network will witness substantial changes in service supply and traffic patterns.
In this paper we analyze certain matching problems that arise in ground delay program planning. Ground delay programs are air traffic flow management initiatives put in place when airport arrival demand is expected to exceed arrival capacity for an extended length of time, e.g. 4 hours. Most of the problems we study can be modeled as assignment problems, where flights are assigned to arrival slots. In the context we analyze, however, these problems have important special structure, which allows us to develop special solution properties. In particular, solutions are measured both in terms of efficiency (delay minimization) and equity (spread of delay over all flights). We show that the theory of majorization provides a powerful tool in addressing solution equity. We consider problems with flight deletions and develop special solution properties and parametric methods.
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.