Ground Delay Program Research Articles

DEMATEL-AHP technique to minimise departure delays due to airspace congestion: a case in Mactan-Cebu International Airport

Airspace congestion has a direct effect on departure schedules of aircraft. In events when there is a constrained airspace due to various factors such as adverse weather, departure delays may be potentially serious. Due to the complexity of the problem of choosing the best possible option in addressing airspace congestion, a combined multiple criteria decision making (MCDM) approach is used for the decision-making process. Furthermore, this paper incorporates the views of experts representing air traffic service and airlines management which is significant in choosing a viable solution to both stakeholders operating air transport. It has been found that both air traffic service and airlines management give most priority to safety, thus, prefer to apply for ground delay program during airspace congestion.

DEMATEL-AHP technique to minimise departure delays due to airspace congestion: a case in Mactan-Cebu International Airport

Airspace congestion has a direct effect on departure schedules of aircraft. In events when there is a constrained airspace due to various factors such as adverse weather, departure delays may be potentially serious. Due to the complexity of the problem of choosing the best possible option in addressing airspace congestion, a combined multiple criteria decision making (MCDM) approach is used for the decision-making process. Furthermore, this paper incorporates the views of experts representing air traffic service and airlines management which is significant in choosing a viable solution to both stakeholders operating air transport. It has been found that both air traffic service and airlines management give most priority to safety, thus, prefer to apply for ground delay program during airspace congestion.

Airline-Driven Ground Delay Programs: A Benefits Assessment

Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines' diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compared to the state-of-the-research centralized approaches. Moreover, the cost reduction benefits of the resultant airline-driven GDP designs are equitably distributed across different airlines.

Methods for determining unimpeded aircraft taxiing time and evaluating airport taxiing performance

The objective of this study is to improve the methods of determining unimpeded (nominal) taxiing time, which is the reference time used for estimating taxiing delay, a widely accepted performance indicator of airport surface movement. After reviewing existing methods used widely by different air navigation service providers (ANSP), new methods relying on computer software and statistical tools, and econometrics regression models are proposed. Regression models are highly recommended because they require less detailed data and can serve the needs of general performance analysis of airport surface operations. The proposed econometrics model outperforms existing ones by introducing more explanatory variables, especially taking aircraft passing and over-passing into the considering of queue length calculation and including runway configuration, ground delay program, and weather factors. The length of the aircraft queue in the taxiway system and the interaction between queues are major contributors to long taxi-out times. The proposed method provides a consistent and more accurate method of calculating taxiing delay and it can be used for ATM-related performance analysis and international comparison.

Optimizing Key Parameters of Ground Delay Program with Uncertain Airport Capacity

The Ground Delay Program (GDP) relies heavily on the capacity of the subject airport, which, due to its uncertainty, adds to the difficulty and suboptimality of GDP operation. This paper proposes a framework for the joint optimization of GDP key parameters including file time, end time, and distance. These parameters are articulated and incorporated in a GDP model, based on which an optimization problem is proposed and solved under uncertain airport capacity. Unlike existing literature, this paper explicitly calculates the optimal GDP file time, which could significantly reduce the delay times as shown in our numerical study. We also propose a joint GDP end-time-and-distance model solved with genetic algorithm. The optimization problem takes into account the GDP operational efficiency, airline and flight equity, and Air Traffic Control (ATC) risks. A simulation study with real-world data is undertaken to demonstrate the advantage of the proposed framework. It is shown that, in comparison with the current GDP in operation, the proposed solution reduces the total delay time, unnecessary ground delay, and unnecessary ground delay flights by 14.7%, 50.8%, and 48.3%, respectively. The proposed GDP strategy has the potential to effectively reduce the overall delay while maintaining the ATC safety risk within an acceptable level.

Understanding the Trade-Off Between Maximum Passenger Throughput and Airline Equity in Allocating Capacity Under Severe Weather Conditions

When the capacity at an airport is reduced because of weather conditions, a ground delay program (GDP) is implemented to resolve the discrepancy between demand for arrival slots and the available arrival slots on a given day. GDPs currently ration the available arrival slots via the proportion of arrivals that exist in the schedule by airline (this practice is termed ration by schedule) with an emphasis on equity among the airlines. Existing rationing schemes do not explicitly consider the number of passengers delayed. This study examined the passenger impacts of a focus on seat throughput in reduced capacity conditions for a GDP at a single airport with consideration for airline equity. An optimization model was developed by using the number of seats available in an aircraft as a proxy for number of passengers and an equity term to estimate airline equity implications. A comparison of the current GDP rationing scheme with one focused on seat delay showed that with no change in the total flight delay time periods, passenger throughput could be improved with a threshold placed on equity. The trade-off between airline equity and passenger throughput and the implications of these results are discussed.

Data-Driven Planning for Ground Delay Programs

This paper provides a model-based approach to planning ground delay programs. Previous research on automated planning of ground delay programs has involved the use of mathematical programming techniques. This paper proposes a data-driven method that models the problem of choosing a traffic management initiative by using the framework of the multiarmed bandit decision problem. This approach makes greater use of the available data, and suggestions made by this procedure can be shown along with data that informed the decision. This combination of tools allows decision makers to more easily evaluate suggested decisions. The paper also provides simulations of the procedure on the basis of data from Newark (New Jersey) International Airport to evaluate its effectiveness.

Ground Delay Program Planning Using Markov Decision Processes

This paper compares three approaches for selecting planned airport acceptance rates in the single-airport ground-holding problem: the Ball et al. model, the Richetta–Odoni dynamic model, and an approach based on approximate dynamic programming. Selecting planned airport acceptance rates is motivated by current practice of ground delay program planning under collaborative decision making. The approaches were evaluated using real flight schedules and landing capacity data from Newark Liberty International and San Francisco International Airports. It is shown that planned airport acceptance rates can be determined from the decision variables of the Richetta–Odoni dynamic model. The approximate dynamic programming solution, introduced by the authors, is found by posing a model that evaluates planned airport acceptance as a Markov decision process. The dynamic Richetta–Odoni and approximate dynamic programming approaches were found to produce similar solutions, and both dominated the Ball et al. model. The Richetta–Odoni dynamic model is computationally more efficient, finding solutions 10 times faster than approximate dynamic programming, although the approximate dynamic programming approach can more easily incorporate complex objectives. Surprisingly, the performance of all three approaches did not change significantly when evaluated using different models for collaborative decision-making procedures. This observation suggests that modeling collaborative decision making may not be important for selecting near-optimal planned airport acceptance rates.

Incorporating Predictability Into Cost Optimization for Ground Delay Programs

This work introduces the goal of predictability into ground delay program (GDP) cost optimization under capacity uncertainty for a single airport case. This is accomplished by modifying traditional GDP delay cost functions so that they incorporate predictability. Extra premiums are assigned to unplanned delays, and planned but unincurred delays, due to their unpredictability. Two stochastic GDP models are developed based on deterministic queueing theory and continuous approximation to estimate the delay components in the cost functions: a static no-revision model and a dynamic model considering one GDP revision. The decision on the time when the planned airport arrival capacity moves from the low level to the normal level, T, is made to minimize the expected GDP cost. The case study shows that, by varying the unpredictability premiums within plausible ranges, the optimal value T* can be anywhere from the 33rd to the 87th percentile of the distribution of the actual capacity recovery time. Of the two unpredictability premiums, the one for unplanned delay has a stronger impact on T* than the one for planned but unincurred delay. In general, reducing GDP scope increases T* except when planned delay is almost equally costly whether or not it is incurred. Depending on the values of unpredictability premiums, cost savings of as much as 13% can be realized from properly valuing unpredictability in GDP decision making.

Airline-Driven Performance-Based Air Traffic Management: Game Theoretic Models and Multicriteria Evaluation

Defining air traffic management as the tools, procedures, and systems employed to ensure safe and efficient operation of air transportation systems, an important objective of future air traffic management systems is to support airline business objectives, subject to ensuring safety and security. Under the current model for designing air traffic management initiatives, the central authority overseeing and regulating air traffic management in a region makes trade-offs between specified performance criteria. The research presented in this paper aims instead to allow the airline community to set performance goals and thus make trade-offs between different performance criteria directly, before specific air traffic management strategies are determined. We propose several approaches for collecting inputs from airlines in a systematic way and for combining these airline inputs into implementable air traffic management initiatives. These include variants of averaging, voting, and ranking mechanisms. We also propose multiple criteria for evaluating the effectiveness of each approach, including Pareto optimality, airline profitability, system optimality, equity, and truthfulness of airline inputs. We apply a game-theoretic approach to examine the potential for strategic (gaming) behavior by airlines. We offer a broad evaluation of each approach, first by providing some theoretical insights, and then by simulating each of the approaches for a generic system using Monte Carlo methods, sampling values for input parameters from a wide range. We also provide an indication of how the approaches might perform in a real system by simulating ground delay programs at two airports in the New York City area. We first apply a simplified model that simulates the process of selecting only planned end times of a ground delay program, using Monte Carlo methods. Next, we apply a more detailed model that simulates the process of selecting planned end times and reduced airport arrival rates. Finally, we characterize the effectiveness of each of the considered approaches on the proposed criteria and identify the most desirable approaches. We conclude that voting schemes, which score highly on all criteria (including airline profitability, system optimality, and equity), represent the most promising approaches (among those considered) to elicit airline preferences, thereby allowing the central authority to design air traffic management initiatives that optimize system performance while respecting the objectives of airlines.

시뮬레이션 기반의 지상지연 프로그램 적용방안에 관한 연구 - 제주국제공항을 사례로 -

Being a cost-efficient solution for alleviating the traffic congestion in airspace, Air Traffic Flow Management (ATFM) has drawn more attentions from not only air traffic controllers but also researchers in the field of Air Traffic Management (ATM). Among other ATFM initiatives, it is believed that Ground Delay Program (GDP) could be effectively applied to reduce the congestion particularly in the relatively small airspace with dense traffic demand. This paper introduces a novel way that suggests flights to be delayed on the departing airports together with amount of the delays (in time) for those flights to be delayed. Adopting a fast-time simulation for predicting airspace delay of each flight for a given flight plan, the method is designed to iteratively and incrementally adjust the departure times in the plan towards reducing total airspace delays. Applying the method to Jeju airport with a hypothetically high demand, the paper demonstrates the airspace delay could be significantly reduced by applying GDP at Gimpo airport where more than 60% of Juju-bound flights departure. Although the simulation model needs to be calibrated and validated for the real-world application, the results clearly shows that the approach can possibly implemented as a tool for preparing the daily plan at the pre-tactical stage defined in the ICAO ATFM manual.

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

Historical data are used to build two types of models that predict Ground Delay Program implementation decisions and produce insights into how and why those decisions are made. More specifically, behavioral cloning and inverse reinforcement learning models are built that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. The developed random forest models are substantially better at predicting hourly Ground Delay Program implementation for these airports than the developed inverse reinforcement learning models. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. The structure of the models are also investigated in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that Ground Delay Program implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours.

Refinements to a Procedure for Estimating Airfield Capacity

This paper presents a method for obtaining airfield capacity estimates using historical data from FAA's Aviation System Performance Metrics (ASPM) database. The process first involves merging individual flights and quarter-hour airport runway operations data sets from ASPM to create a new data set. Data for Newark Liberty International Airport (EWR) in New Jersey and San Diego International Airport in California from 2006 to 2011 were used. Then, filters for meteorological condition, runway configuration, called rates, and fleet mix were applied to the two airport data sets. The filtered data sets were then used in a censored regression model of capacity that included queue length (number of aircraft waiting to arrive or depart) and arrival–departure throughput count splits as independent variables. These attributes were found to affect airfield capacity at statistically significant levels, and parameters had expected signs and magnitudes. Additionally, capacities under ideal conditions were found to be reasonably close to other sources. The model also confirmed that average capacities at EWR during hours when a ground delay program (GDP) was running were lower than when there was no GDP in effect. The method described in this paper could be used to more precisely quantify airfield capacities in specific conditions of particular interest to air traffic controllers and airport operators to better facilitate decisions that rely heavily on a good understanding of capacity in these conditions. The data exploration and preparation undertaken as part of the study reveal some of the finer points of the ASPM data and how they can be used in a more meaningful way for airfield capacity estimation.

Research on Traffic Flow Optimization of Flight Route and Ground Delay Program

This paper mainly discusses the traffic flow optimization of flight route and the problem of ground delay program. the author introduces the concept, procedure, method and the meaning of air traffic flow management. The author utilizes Eulerian method to build the model of air traffic networks, and solves the problem with adjoint method, then uses it to optimize the flow of the route, The author also builds the model of ground delay program with fair factor, and solves it with particle swarm optimization method. The author also uses the real data to prove the feasibility of the above two methods. Also, the author designs the original system of air traffic flow management, and gives the flow of operation and the output of interface. In the end, the author summarizes the study meaning of the paper and puts forward the opinion of the research of future air traffic flow management.

Improved Method on Traffic Flow Optimization Ground Delay Program

This article develops a flow model of high altitude traffic in the National Airspace System using an Eulerian description of the network with hyperbolic partial differential equations. Existence and uniqueness (well-posedness) of a solution to the system of partial differential equations on a network is established. Subsequently, an optimal control problem is studied with the junction coefficients as control variables. We use a fully discretised adjoint approach and we implement it on a network with 16 links and 5 junctions created from the enroute high altitude jetways between the Oakland and Salt Lake City air traffic control centers. The aircraft flows on the final link of the network with and without control are compared and the results demonstrate the efficiency of the method developed in the article.

Rescheduling of flights during ground delay programs with consideration of passenger and crew connections

We address the rescheduling problem by an airline when a ground delay program (GDP) is issued with mathematical programming techniques. The objective is to minimize delay measures, cost for crew and passenger misconnections, and cost of flight cancelations subject to several restrictions. We present a new linear integer model that incorporates all objectives. Using real-world and random data we present extensive computations to evaluate the model that is solved with standard software. High quality solutions are found quickly, i.e. within seconds. We show the significant effect of setting cost values for misconnections and cancelations on the new slot assignments.

Clustering Days and Hours with Similar Airport Traffic and Weather Conditions

On any given day, constraints in the National Airspace System (for instance, weather) necessitate the implementation of traffic flow management initiatives, such as ground delay programs. The goal of this study is to take a preliminary step toward informing future decision making by applying data-mining techniques to identify similar days in the National Airspace System in terms of the cause and location of historically implemented ground delay programs. In the first part of this study, a modified -means clustering algorithm was applied to all days from 2010 through 2012, resulting in the identification of 45 national-level daily clusters that represent unique combinations of historically implemented ground delay programs. The second part of this study focused on verifying the stated causes of the historical ground delay programs. Findings from this initial study indicated that it is possible to identify similar days under which the National Airspace System operates, and clustering techniques appear to be promising methods for identifying the major causes of ground delay programs.

Operating cost based cruise speed reduction for ground delay programs: Effect of scope length

Ground delay programs typically involve the delaying of aircraft that are departing from origin airports within some set distance of a capacity constrained destination airport. Long haul flights are not delayed in this way. A trade-off exists when fixing the distance parameter: increasing the ‘scope’ distributes delay among more aircraft and may reduce airborne holding delay but could also result in unnecessary delay in the (frequently observed) case of early program cancellation. In order to overcome part of this drawback, a fuel based cruise speed reduction strategy aimed at realizing airborne delay, was suggested by the authors in previous publications. By flying slower, at a specific speed, aircraft that are airborne can recover part of their initially assigned delay without incurring extra fuel consumption if the ground delay program is canceled before planned. In this paper, the effect of the scope of the program is assessed when applying this strategy. A case study is presented by analyzing all the ground delay programs that took place at San Francisco, Newark Liberty and Chicago O’Hare International airports during one year. Results show that by the introduction of this technique it is possible to define larger scopes, partially reducing the amount of unrecovered delay.

Decision Modeling Framework to Minimize Arrival Delays from Ground Delay Programs

Convective weather and other constraints create uncertainty in air transportation, leading to costly delays. A Ground Delay Program (GDP) is a strategy to mitigate these effects. Systematic decision support can increase GDP efficacy, reduce delays, and minimize direct operating costs. In this study we construct a decision analysis (DA) model combining a decision tree and Bayesian belief network. Through a case study of LaGuardia Airport, the DA model demonstrates that larger GDP scopes including more flights in the program, hourly rates between 30-34 operations, and lead times greater than two hours trigger the fewest delays, a savings monetized up to $1,850 per flight. Furthermore, when convective weather is predicted, forecast weather confidences and scheduled traffic remain the same level or greater nearly 70% of the time, supporting more strategic decision making. Thus, the DA model enables quantification of uncertainties and insights on causal relationships, providing support for future GDP decisions.

Evaluation of the Performance of Ground Delay Programs

Ground delay programs (GDPs) are frequently used to keep U.S. air transportation safe and efficient. Most research on GDPs has focused on optimal design and implementation, with little attention given to retrospective performance evaluation. This research fills that gap by identifying GDP performance criteria, developing associated performance metrics, and evaluating GDP performance metrics across airports and over time. GDP performance criteria are established, and associated performance metrics are specified for five performance goals: capacity utilization, efficiency, predictability, equity, and flexibility. By defining multiple performance metrics, this research enables FAA traffic managers and flight operators to review GDP performance after the fact in a comprehensive way and to uncover GDP performance trends across airports and over time. Through use of data from the FAA Aggregate Demand List and the FAA Aviation System Performance Metrics, historical GDP performance is assessed for San Francisco International Airport (SFO) in California and Newark Liberty International Airport (EWR) in New Jersey for 2006 and 2011. For both airports, capacity utilization and efficiency scores are high, on average, and reflect the importance that FAA and the flight operator community attach to making effective use of available capacity and keeping air transport efficient and safe. In contrast, predictability performance is weaker and more variable. Lack of consensus on how predictability should be measured or valued could have diminished the importance of this measure in GDP decision making. On average, SFO GDPs have higher capacity utilization and predictability, whereas EWR GDPs are more efficient, equitable, and flexible. A comparison of results for 2006 and 2011 shows that GDPs were more predictable but capacity was used less effectively in 2011.