Airline-Driven Ground Delay Programs: A Benefits Assessment

Generating day-of-operation probabilistic capacity scenarios from weather forecasts

Scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM.

This paper builds on top of a collaborative ATFM framework previously published, which demonstrated a potential in significant pre-tactical system delay reduction over the current European ATFM practices. In this paper, we aim to raise the technology readiness level (TRL) of the original works, focusing on improving the tractability of its DCB optimizer. To this end, we apply a Dantzig-Wolfe (DW) decomposition approach and use high performance computing (HPC) techniques to attempt the solution in a short time. The main contribution of this paper is to mitigate the computational burden faced by previous approaches, which will make all the contributions demonstrated therein more operationally feasible.

Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices

A study on basic research priorities and development suggestions for the digital transformation of air traffic management

The Ration by Schedule (RBS) algorithm has been accepted by the aviation community as the basis of Collaborative Decision Making (CDM) in strategic Traffic Flow Management (TFM) initiatives such as Ground Delay Programs (GDPs) and Airspace Flow Programs (AFPs). However, applications of RBS to date have been limited to strategic metering applications in which the slots to be allocated to flights are all of equal duration, and the flights to be assigned to slots do not need to be differentiated. Decision support capabilities for the general Air Traffic Management (ATM) problem require planning and scheduling algorithms that properly recognize different flight characteristics such as weight class, runway, departure direction and other Air Traffic Control (ATC) procedural issues in determining the slot size that is required for each flight. In this paper, we describe our design and implementation of the Generalized RBS algorithm for a surface traffic management scheduling application which involves detailed flight-by-flight slot time assignment. This algorithm has been designed and developed to support the Collaborative Departure Queue Management (CDQM) component of the Surface Trajectory Based Operations (STBO) project. CDQM provides an equitable allocation of departure capacity to each flight operator at an airport for dynamic use in management of departure queues. The principles of the RBS algorithm are achieved through an initial planning pass based on scheduled data, followed by a second planning pass in which dynamic flight status is incorporated into the planning results.

The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting. To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines.

The rapid advancement of cutting-edge technology has reshaped the global dynamics of the airline sector, elevating the significance of service quality, effectiveness, and protection. As a vital contributor to national economic growth and a public utility, aviation's expansion has directly impacted businesses in the service sector, leading to increased operational opportunities for hotels, restaurants, and travel agencies. This article addresses the challenges within Air Traffic Management (ATM), focusing on the aviation industry's primary concerns. With the continuous growth of aviation traffic, there is a pressing need to enhance security, productivity, and environmental sustainability. The article aims to stimulate business innovation and collaboration in advanced study areas such as dynamic airspace management (DAM), air traffic flow management (ATFM), and collaborative/non-collaborative surveillance. The proposed frameworks, based on Clean Sky and NextGen, incorporate 4D Trajectory Optimization techniques, advanced surveillance technologies, and data link connections to establish a foundation for ATM industry development. Additionally, the research explores adaptive Human-Machine Interface and Interaction (HMI2) forms to automate the assessment and negotiation of aircraft intentions, thereby improving the efficiency and security of ATM operations. The study also addresses specific requirements for cooperative and non-cooperative Detect-and-Avoid (DAA) systems for Remotely Piloted Aircraft Systems (RPAS) within the evolving CNS+A process, ensuring their safe and unrestricted access to all airspace types.

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

Air Traffic Management (ATM) is defined as a broad domain that consists of complex activities such as Air Traffic Flow Management (ATFM). Within this context this paper presents two prototypes of a module (MAAD) assembled in a decision support device that is based on Artificial Intelligence, which generates suggestions for air traffic flow control according to each traffic saturation scenario. The device known as Distributed Decision Support System for Air Traffic Flow Management (SISCONFLUX) was built with a computational agent based on Reinforcement Learning (Module MAAD), which uses Q-learning algorithm. Two prototypes were built and one of them incorporated the specialist's (human agent) experience. The performance of the prototypes was evaluated through a case study modeled from real traffic demands verified on certain dates. The results obtained in the case study are promising; the behavior of the prototypes demonstrated that the Q-learning algorithm converged in a satisfying way. The prototypes generated actions that contributed effectively to the reduction of saturation in the air traffic scenarios under test.

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Two-Stage Scalable Air Traffic Flow Management Model Under Uncertainty</b> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>G. G. N. Sandamali, R. Su, K. L. K. Sudheera, Y. Zhang, and Y. Zhang</i> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In order to efficiently balance the current and future air traffic demands with the system capacity, a proper air traffic flow management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the air transportation system (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, the authors propose an ATFM framework for scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, the authors propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an integer linear programming (ILP) problem, and a mixed-integer linear programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a maximum weighted independent set (MWIS) finding problem.

Many Cyber-Physical Systems (CPSs) demonstrate high-dimensional environmental uncertainties that modulate the physical system dynamics and complicate the computational decision-making tasks. As an example, strategic air traffic flow management (ATFM) aims to resolve air traffic congestion through managing traffic flows at a long look-ahead time. The convective weather, a dominant factor of traffic congestion, is very uncertain and significantly complicates the decision-making process. Other uncertainties such as traffic demands further expand the uncertainty space and complicate the design of optimal control solutions that are robust to uncertainties. In this paper, we formulate the strategic ATFM as a stochastic optimal control problem, and use scalable sampling based dynamic programming and Q-learning approaches to address high-dimensional uncertainties. Simulation studies demonstrate the effectiveness of these approaches. These approaches generally apply to CPSs that operate under high-dimensional environmental uncertainties.

Air traffic flow management (ATFM) attempts to maintain a safe and efficient flow of aircraft given demand-capacity mismatches while ensuring an equitable distribution of delays among stakeholders. There has been extensive research addressing network effects (such as the presence of multiple airports, sectors, and connectivity requirements) in ATFM, but it has not explicitly incorporated the equitable distribution of delays, as well as work on the equitable distribution of delays in a single-airport setting, such as ration-by-schedule (RBS) as introduced under the collaborative decision-making paradigm. In this paper, we develop a two stage approach for network ATFM that incorporates fairness and airline collaboration. In Stage 1, we propose a discrete optimization model that attempts to incorporate an equitable distribution of delays among airlines by introducing a notion of fairness in network ATFM models—controlling the number of reversals and total amount of overtaking, which is a natural generalization of RBS. For two flights f and f′, a reversal occurs when flight f′ arrives before f, when f was scheduled to arrive before f′. In the event a reversal occurs, the number of time periods between the arrival times constitutes overtaking. In Stage 2, we allow for airline collaboration by proposing a network model for slot reallocation. We provide extensive empirical results of the proposed optimization models on national-scale, real-world data sets spanning six days that show interesting trade-offs between fairness and efficiency. We report computational times of less than 30 minutes for up to 25 airports and provide theoretical evidence that illuminates the strength of our approach.

&amp;lt;p&amp;gt;Convective weather&amp;amp;#160;represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms&amp;amp;#160;are the cause for a substantial amount of delay in&amp;amp;#160;both the en-route and airport&amp;amp;#160;environment. Before the day of operations, poor prediction capability of convective weather prohibits traffic managers from considering weather mitigation strategies during the pre-tactical phase of ATFM planning. As a result, convective weather is mitigated tactically, possibly leading to excessive delays. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The skill of weather forecasting has greatly improved in recent years. Hi-resolution weather models can predict the future state of the atmosphere for some weather parameters. However, incorporating the output from these sophisticated weather products into an ATFM solution that provides easily interpreted information by the air traffic managers remains a challenge.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This paper combines data from high-resolution&amp;amp;#160;numerical&amp;amp;#160;weather&amp;amp;#160;predictions&amp;amp;#160;with actual storm observations from lightning detecting and satellite images. It applies supervised machine learning techniques such as binary classification, multiclass classification, and regression to train neural networks to predict the occurrence, severity, and altitude of thunderstorms. The model predictions are given up to 36hr in advance, within timeframes necessary for pre-tactical planning of ATFM, providing traffic managers with valuable information for developing weather mitigation plans.&amp;amp;#160;&amp;lt;/p&amp;gt;