Fast-Time Demand-Capacity Balancing Optimizer for Collaborative Air Traffic Flow Management

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

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.

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.

This paper presents the methodology and results of a Human-In-The-Loop (HITL) simulation study conducted in the Airspace Operations Laboratory at NASA Ames Research Center. This study is a part of NASA's ongoing research into developing an Integrated Demand Management (IDM) concept, whose aim is to improve traffic flow management (TFM) by coordinating the FAA's strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The purpose of TFM is to regulate air traffic demand so that it is delivered efficiently through constrained airspace resources without exceeding their capacity limits. The IDM concept leverages a new TFMS capability called the Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for managing traffic tactically by generating precise arrival schedules. Unlike other TFM tools, CTOP gives flight operators the option of submitting a set of user-preferred alternative trajectories for each flight. CTOP can then use these trajectory option sets (or TOSs) to find user-preferred alternative routes to reduce demand on an overloaded resource. CTOP's effectiveness in redistributing demand is limited, however, by the availability of flights with alternative routes. The research presented in this paper focuses on evaluating the impact on TFM operations by varying the percentage of flights that submit a multiple-option TOS ('TOS participation levels'). Results show the impact on overall system performance and on the rerouted flights themselves. The simulation used a Newark (EWR) airport arrival scenario, with en route weather affecting traffic inbound from the west. Participants were asked to control each of the three arrival flows (north, west, and south) to meet their individual capacity constraints while simultaneously ensuring efficient utilization of the capacity at the destination airport. A large, permeable convective weather cell located southeast of Chicago severely reduced the capacity of the west flow. The study evaluated the impact of five different TOS participation levels on CTOP's ability to re-allocate traffic from the west and improve TFM performance in terms of delay assignment and traffic delivery rate to the airport. Overall, the results showed that increasing TOS submissions allowed the overall system delays to be reduced and fairly distributed among the three arrival flows, at the same time achieving the airport throughput rate. Moreover, it was found that aircraft who submitted a TOS saw a greater reduction in delay, even when they were assigned longer routes. This was particularly true when fewer aircraft submitted a TOS. The results confirm that the CTOP operations with higher TOS participation levels helped utilize the overall National Airspace System (NAS) resources as well as benefited the users who participated.

This paper explores how applying a basic management science principle to air traffic management operations can lead to revised roles and responsibilities that allow operations to be handled by the most appropriate participants. Previous research that applied the principle to local departure and arrival management is reviewed; the application is then expanded to regional and national air traffic management. The paper examines the operational and engineering implications of the resulting concept, contrasted with current operations and tools, and identifies where additional research or development is needed. Examples are provided that illustrate the resulting activities at the various air traffic management facilities across a broad range of look-ahead timeframes. I. Introduction HE High-Density Area Departure and Arrival Management (HDDAM) concept was developed in the context of arrival and departure air traffic management in high-density airspace such as Metroplex areas. The management science principles underlying HDDAM appear to be applicable to National Airspace System (NAS) operations at any level, including all phases of planning and flight. This paper explores some operational and engineering implications of such an application to local, regional, and NAS-wide air traffic operations. As described elsewhere, 1-3 HDDAM redefines the decision making processes within the Air Route Traffic Control Center (ARTCC), the Terminal Radar Approach Control (TRACON), and multiple Air Traffic Control Towers (ATCTs) for high-density area departure/arrival traffic management. The decision making authority proposed for each facility reflects the information available to each decision maker and the impact of the decision on their operating environment. Specifically, each facility is responsible for defining the capacity of its air traffic management resources (such as airports, airspace, airways) and requesting resources for flights to use. As shown in Fig. 1 for a single Metroplex, the TRACON has a central role in these interactions, serving as a “broker” between the facilities with the resources and the facilities with requests for use of those resources that are not within their scope of control, while also accounting for the TRACON’s ability to deliver the flights to the resources in question. ATCTs are responsible for requesting resources for departing flights and the ARTCC for requesting resources for arriving flights, as they each possess the best information regarding the status of these flights. Because of the uncertainties present in predicting individual flight departure readiness and inbound air traffic arrival times, the TRACON does not assign specific departure and arrival slots to individual flights. Instead, the TRACON allocates slots to the airports (for departures) and ARTCC (for arrivals) so they can make flight-specific assignments as deemed best for their individual operating environments. This effectively decouples the decisions of the individual ATCTs and the ARTCC, giving each facility increased flexibility to handle situations dynamically as they develop. Air traffic management operations are most effective when the decision makers work as a team. The underlying principle of HDDAM is that this can be accomplished best when all facilities’ roles and responsibilities are based on the management science principle that allows the appropriate level of autonomy at each facility. The principle states that decisions should be assigned to those who are already performing other tasks with the relevant data—those who know the most about the factors affecting the decision—and will be most directly affected by the consequences of 1 Lead Multi-Discipline System Engineer, TFM System Engineering and Evolution, 7515 Colshire Drive, Mailstop

The Federal Aviation Administration (FAA) is funding and encouraging new concepts for improving air traffic flow management (TFM) decision-making. The resulting automation capabilities will need to be operationally integrated into the existing air traffic management (ATM) system. Understanding how a new capability will interact with existing system components is challenging because of the range of possible real-world situations, which must be handled by the ATM system. Although there are fast-time traffic simulation tools available for modeling the impact of TFM actions, they are often developed as stand-alone tools, which are not extensible or flexible to work in concert with other advanced TFM capabilities for conducting integration studies or quantifying benefits. To address this gap, we have built a fast-time, distributed simulation platform integrating state-of-the-art traffic simulator and allows the plug-in of advanced TFM prototypes - or other experimental capabilities that already exist - so their interactions can be studied. In this paper, we discuss the requirements and the necessary components for building this platform, which requires an architecture that is flexible enough to support many different configurations of modeling tools and applications. We then use a TFM integration case study to demonstrate the utility of the platform. We show, with only minor effort, a proposed TFM prototype can be plugged into the platform and its benefits can be evaluated.

The nationwide air traffic flow management problem often encounters computational difficulty because it is generally modeled as an integer programming problem that requires computationally expensive optimization algorithms. This paper introduces a customized Spark-based optimization architecture for such large-scale integer programming problems to further speed up the modeling and optimization process, where Spark is a big data cluster-computing platform. First, a novel layered aggregate model is developed for handling flexible rerouting problem, which is not well handled in a previous link transmission model. As an aggregate linear model, the layered aggregate model has the nice features of computational efficiency and scalability, which make it suitable for Apache Spark. By applying a dual decomposition method, the original large-scale problem is decomposed into a number of small subproblems. The optimization proceeds by iteratively solving subproblems and updating Lagrange multipliers. This paper encapsulated the process into the Spark-based data processing model such that the optimization is automatically scheduled to run in parallel. Spark gains efficiency by means of in-memory computing and dynamic schedule allocation. This is demonstrated in the experimental results that are compared to an earlier Hadoop MapReduce-based model, where Hadoop MapReduce is a basic cloud computing framework; the Spark-based model is solved twice as fast as the Hadoop MapReduce-based model.

This paper considers the air traffic Flow Management Problem (FMP) for the airspace case (where bottlenecks are not only airports but could also be, for example, sectors). Only temporal solutions are allowed, which means that no altemative routes can be used, thus aircraft 'holds' are the management instruments to be used. The formulation of a general model is first proposed and then a more precise and realistic Binary Integer Programming-based model is developed. Solutions are obtained using Linear Programming supplemented by a heuristic, or a heuristic only. Some of the results of numerous experiments using real-life data are presented for illustrative purposes.

Decision support system in tactical air traffic flow management for air traffic flow controllers

In this paper, we develop an analytical approach to planning air traffic flow management (TFM) strategies under uncertain weather. To do that, we model the dynamics of an air traffic flow under uncertain weather as a discrete jump-linear system, which enacts the tracking of transient traffic congestion statistics. We then conduct a sensitivity analysis of traffic congestion to disturbances introduced by mean inflow rate and flow restriction constraint. The integrated framework of jump-linear modeling and sensitivity analysis permits optimal planning for flow redistribution and flow restriction redesign. We demonstrate the design procedure through an example of a major flow splitting into multiple flows that enter a bank of weather zones.

In order to solve the short-term air traffic flow management problem (ST-TFMP) efficiently, we present an algorithm using Lagrangian relaxation technique. The primal problem is decoupled by relaxing the air route capacity constraints, and then the corresponding dual problem is solved by subgradient method. We also propose a heuristic procedure in combination with the knowledge of air traffic management. It forces the dual solution from infeasible to feasible by adding non-coupled constraints. At last, the algorithm is validated by practical data from the Beijing ATC Center of the Civil Aviation Administration of China (CAAC). The computational results show that the algorithm can improve the performance of ST-TFMP.

This chapter reviews the International Civil Aviation Organization air traffic management (ATM) work programme along with ATM developments taking place around the world. It examines the various elements of the ATM system and suggests some of die research, trials and demonstrations. Airspace management is traditionally recognized mainly as involving a dynamic or tactical sharing of airspace by civil and military users. Air Traffic Services itself is comprised of several subelements, one of these being air traffic control. The increasing number of long-range interregional flights may require integration of regional Air Traffic Flow Management systems and procedures. Free Right is the guiding vision, mission and operational concept of the Federal Aviation Administration, based on Communications, Navigation, Surveillance/Air Traffic Management systems (CNS/ATM). Africa is composed of many delicate national economies separated by vast wilderness areas. The main scope of Airspace management will be toward a strategic planning function of airspace infrastructure, and flexibility of airspace use.

<p>Convective weather represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms are the cause for a substantial amount of delay in both the en-route and airport 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.  </p><p>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. </p><p>This paper combines data from high-resolution numerical weather predictions 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. </p>

Air traffic flow management (TFM) seeks to balance the demand for and the capacity of airspace and airport resources. Weather phenomena are major causes of diminished airport capacity, and an understanding of these phenomena will enhance national-level TFM. Because weather systems follow patterns that are, to some degree, similar to one another, it is reasonable to classify those patterns into types. A case-based reasoning approach could then be used to help traffic managers make associations between today’s predicted weather pattern and past patterns, in conjunction with accompanying TFM solution strategies. This article uses a year of daily weather data, and classifies like days into archetypes or “day types.” Seventeen day types were determined and are described in terms of weather severity in regions of the conterminous United States. © 1995 John Wiley & Sons, Inc.

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.