Flexible Tube-Based Network Control

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.

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.

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.

is a programme to modernise the European Air Traffic Management (ATM) System. It is currently in its feasibility definition study phase which is being co-funded by the European Commission and EUROCONTROL. It is addressing collectively the technological, economic and regulatory aspects associated with ATM. Following the successful completion of definition study phase, it will use the Single European Sky (SES) legislation to synchronise the plans and actions of the different stakeholders in ATM and federate the resources needed for the development and implementation of the required improvements throughout Europe; this applying to both the applicable airborne and ground systems domains. The study is being conducted over a 2-year period (which started in March 2006) by the SESAR Consortium, a grouping which, for the first time in ATM history, has brought together the major stakeholders in European aviation to develop a common vision of the future ATM System. The SESAR Consortium draws upon the expertise of the major organisations within the aviation industry. This includes airspace users, air navigation service providers (ANSPs), airport operators and the supply industry (European and non-European), plus a number of associated partners, including safety regulators, military organisations, staff associations (including pilots, controllers and engineers) and research centres which work together with significant expertise being provided by EUROCONTROL. The study will produce an agreed Master Plan of the research, development and implementation plans and actions needed to be taken by the different stakeholders to realise the future European ATM System. This Plan is developed through the production of 6 main deliverables over the 2 years and covers all aspects of the future System, including its supporting institutional framework. The scope of each the 6 deliverables (D1 to D6) is as follows : D1: Air Transport Framework – the Current Situation D2: Air Transport Framework – the Performance Target D3: Definition of the future ATM Target Concept D4: Selection of the “Best” Deployment Scenario D5: Production of the ATM Master Plan D6: Work Programme for 2008–2013. This paper will summarise the progress made to date by describing the results achieved within the deliverables D1 to D3. In outline terms, these cover : a summary snapshot of the current situation to identify the main areas of improvement; an assessment of the future air transport market place into which future ATM services will be delivered; the management and performance frameworks which will govern the future operation of the System and its further evolution; an initial set of performance requirements which the future System must meet; the nature of the Future ATM Target Concept, this comprising the concept of operations to be adopted; structure of the overall architecture needed for the System; set of technologies to be developed and used to implement it.

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

With the continuous expansion of air traffic flow, the increasingly serious aviation network security threats have a significant and far-reaching impact on the safe operation of the aviation industry. The networked air traffic management (ATM) system is an integrated space–air–ground network integrating communication, network, satellite, and data chain technology, which adopts a network-centric structure to provide network-enabled services and applications for the air transportation system. In view of the serious network threats faced by networked ATM, this paper studies the basic theories and key technologies of ATM information security assurance and designs a credible security architecture to provide comprehensive and systematic security assurance for networked ATM. Starting from the problems and development trend of ATM security assurance, this paper investigates the dynamic and complex data processing process of ATM system, analyzes the complex interaction between its cyber and physical system, and then constructs the networked ATM cyber–physical fusion system model and threat model. Furthermore, the causal relationship between ATM security threat alert information is mapped into a Bayesian network, and the game model of networked ATM is proposed using Bayesian Nash equilibrium strategy. At last, the information security assurance architecture of networked ATM system based on blockchain technology is formed by establishing a distributed co-trust mechanism and multi-chain storage structure. We expect this paper to bring some inspiration to the related research in academia and aviation so as to provide useful reference for the construction of ATM safety and security system and the development of technology.

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.

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 existing Air Traffic Management (ATM) system can evolve into a Space and Air Traffic Management System (SATMS), as space operations become a more common event. An assessment tool has been developed as a catalyst for this evolution; the Dynamic Flight Path Tool was developed to utilize real time situational information to minimize the impact of space flight to operations while at the same time ensuring a level of safety acceptable to the public. This tool capitalizes on methods that rapidly compute the debris clouds within a four-dimensional space (3D space plus time). The tool has two modes of operation: 1) Pre- mission Planning and 2) Real-time Mission Mode. The Dynamic Flight Path Tool has been applied to a range of sample scenarios that demonstrate how the impact of space launch operations on air traffic can be significantly reduced. 1 . An assessment tool has been developed as a catalyst for this evolution; the Dynamic Flight Path Tool, a Research and Development prototype, was developed to utilize real time situational information to minimize the impact of space flight to operations while at the same time ensuring a level of safety acceptable to the public. This tool capitalizes on methods that rapidly compute the debris clouds within a four-dimensional space (3D space plus time). The tool has two modes of operation: 1. Pre-mission Planning. The Tool enables the planner to identify scheduled air traffic affected by a planned operation and potentially schedule a launch operation during the time of lowest air traffic density and thus, lowest overall risk and disruption. 2. Real-time Mission Mode. The Tool performs trajectory prediction to identify what air traffic will enter the operational area of interest. The Tool provides the Flight Safety Officer (FSO) and Air Traffic Control (ATC) with situational awareness information on the space and air traffic. This allows the FSO and ATC to dynamically manage the airspace as the launch operation progresses, as opposed to issuing a blanket NOTAM (Notice to Airmen) restricting a large area of airspace. The Dynamic Flight Path Tool has been applied to a range of sample scenarios that demonstrate how the impact of space launch operations on air traffic can be significantly reduced. While destruct events are very rare and the vast majority of launch operations are nominal, the dynamic understanding of the space and air traffic picture between the FSO and ATC facilitated by the Dynamic Flight Path Tool gives all users of the airspace considerably more flexibility in managing the needs of their respective airspace users while guaranteeing an acceptable level of safety. Presently the Federal Aviation Administration (FAA) considers the risk to flying and not how to respond to the real time situation that exists during a space launch. The FAA follows its strategic planning and does not formulate contingency plans - rather it relies on tactically moving aircraft - to account for the actual location of

In this paper we analyze alternative near-term Traffic Flow Management Operational Incentives (OPI). These OPIs are part of a larger effort to help accelerate airlines’ equipage efforts with respect to NextGen avionics in the areas of Communication, Navigation, and Surveillance. We analyze alternative operational incentives that can be implemented through the slot allocation process in a Ground Delay Program. Potential incentives can either provide natural benefits through capacity increase, or redistribute ground delays in a zero-sum fashion in favor of equipped flights. We argue that, due to the complex nature of airline operations, quantifying the benefits of these incentives can be challenging. Furthermore, as airlines compete for limited resources in the National Airspace System, incentives may trigger different actions among airlines. Thus we propose a marginal analysis with respect to varying equipage mixtures to study the local and system-wide ramifications of possible equipage decisions made by airlines in reaction to each incentive. This analysis framework can aid policymakers in selecting the appropriate operational incentive mechanisms to yield desirable outcomes. ull realization of NextGen benefits to the National Airspace System (NAS) relies upon aircraft being equipped with certain technological systems. However, the equipage process has faced impediments that have slowed equipage rates below targets. These impediments include high costs of acquisition, retrofitting, and crew training. Furthermore, uncertainty regarding return on investment in terms of the time until benefits start to accrue, as well as the actual magnitude of the benefits, complicates the decision-making process for the airlines to equip. Moreover, due to the highly competitive nature of airline operations, unresolved issues, such as early adopter versus free rider , exacerbate the uncertainty among airlines in making the decision to equip their fleets. It is believed that a combination of financial 1 and operational incentives 2,3 can help accelerate the equipage process by alleviating some uncertainty regarding the realization of the benefits associated with equipage. Previous studies 2,4 have indicated that strategic-level Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) and Airspace Flow Programs (AFP) can be used as a vehicle for Traffic Flow Management (TFM) Operational Incentives (OPI). Such incentives can be implemented by changing the resource allocation algorithms used to assign ground delays to flights. These alterations affect the distribution of benefits—in the form of delay reductions—among equipped and unequipped flights. The Joint Planning and Development Office (JPDO) offers an in-depth analysis of resource allocation and flight prioritization mechanisms in TMIs. 5 We classify TFM OPIs into two major categories based on how benefits are generated. The first category of incentives is associated with transitional benefits, in which equipage may not result in a near-term increase in capacity or throughput. The second category of incentives is associated with natural benefits, wherein increased capacity or throughput arises from the equipage and the consequent improvement in the aircraft performance.

Air Traffic Management decisions affect the interests of a diverse group of organizations and individuals. Agents, including individual aircraft, airlines, and ground control centers, look for conflictfree options that meet their own interests without depreciating other agents' operations. A cooperative structure for interactions of agents has been defined on the basis of Principled Negotiation. This method allows distributed decision-making for all users while providing a fair coordination through Air Traffic Management systems. The major interest of Principled Negotiation relies in its mutual gain approach to selection of options. Agents negotiate options with a traffic coordinator that ensures coordination of all users preferred operations while satisfying safety criteria. Thus, the model is flexible in responding to the needs and goals of agents, and it maintains safety. It provides users with different interests the freedom to optimize their operations, it includes Air Traffic Flow Management processes, and it certifies safe aircraft separations. A computer simulation dedicated to evaluating the benefits of such a cooperative structure is under development. The simulation provides trajectorybased traffic modeling with freedom of routing, communication, and negotiation modeling. It incorporates intelligent behavior modeling and incorporates airborne collision avoidance systems. First results show that Principled Negotiation is appropriate for coordination in a multi-agent/multi-interest system such as the Aircraft/Airspace System.

Flight cancellations, departure delays, congestion in taxi times and airborne holding delays are increasingly frequent problems that negatively impact the performance, fuel burn, emissions rate and customer satisfaction at major airports in the world. However, this is just a brushstroke of the future to come. The dramatic growth in the air traffic levels has become a problem of paramount importance, leading into an increased interest for enhancing the current Air Traffic Management (ATM) systems. The main objective is to being able to cope with the sustained air traffic growth under safe, economic, efficient and environmental friendly working conditions. The ADS-B (Automatic Dependent Surveillance — Broadcast) technology plays a major role in the new ATM systems, since it provides more accurate real-time positioning information than secondary radars, in spite of using a cheaper infrastructure. However, the main flaw in the use of ADS-B technology is the generation of large volumes of data, that, when merged with other flight-related information, faces important scalability issues. In this work, we start off from a previously developed data lake for the support of the full ADS-B data life-cycle in a scalable and cost-effective way, and propose a data architecture to integrate data from different providers and reconstruct flight trajectories that can ultimately be used to improve the efficiency in flight operations. This data architecture is also evaluated using a 2-week testbed which reports some interesting figures about its effectiveness.

in recent years, air traffic management bureaus have successively built air traffic control systems, airport surface monitoring system, electronic flight data system, digital clearance system, collaborative decision-making system, traffic management system, etc. These systems have accumulated a huge amount of air traffic control data that has potential value. Analyzing the data of air traffic control by using machine learning algorithms can provide a guarantee for safe production and enable the air traffic control system to provide services more safely and efficiently. Therefore, the establishment of aviation big data storage and analysis platform has become an important task of air traffic management. This paper design a big data platform which stores the data output by each system used by the air traffic management system. This paper introduces the connection of data sources and the structure of the big data platform. The platform provides decision making including flight delay analysis, operational efficiency, and experience routes.

ABSTRACTThe current Air Traffic Management (ATM) system in the United States supports a wide variety of users in providing a safe and efficient air transportation system. The ATM system has to be responsive to the changing needs of its users. One of the challenges in the ATM system evolution is to design the evolution of a Decision Support System (DSS)/automation architecture. This paper proposes a framework and a common language for understanding the different aspects of a DSS architecture.