Reducing Flight Delays Through Better Traffic Management

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.

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.

A number of Federal Aviation Administration (FAA) automation systems and developmental programs interoperate to support the day-to-day operations of the National Airspace System (NAS). The integration of New Entrants (NEs), such as Remotely Piloted Aircraft (RPA) 1 and Space Vehicle (SV) operations, into the NAS will require modification to the current state of automation. Examining the relevant operational, technical, and procedural implications of integrated NE operations, in addition to exploring the potential impacts of accelerated RPA usage on NAS dynamics, are critically important efforts necessary to understanding operational impacts to NAS systems and users. Our paper presents the potential impacts of RPA integration on two of these systems: En Route Automation Modernization (ERAM) and Traffic Flow Management System (TFMS). A significant component of the impact on NAS systems comes from flight plan and related data processing since all SVs and many RPAs will fly non-standard flight patterns (e.g., RPA loitering and grid search). The processes and details of the non-traditional flight routes, as well different RPA performance specifications, need to be considered as missions are overlaid on the national jet route structure. Of particular concern and interest are the changes in air traffic control (ATC) and air traffic management (ATM) systems and sub-systems needed to address air traffic controller workload and workload complexity concerns associated with NE activities. One potential accommodation approach, presented in this paper, is the use of Dynamic Airspace Configuration both for RPA routine operations and contingency events. Additionally, we present an RPA 1 In this paper, UAS and RPA are used interchangeably without intending any difference per se. It is noted that the U.S. DoD and ICAO use RPA while NASA and the FAA use UAS. demand forecast tool developed under a National Aeronautics and Space Administration (NASA) project. This tool will assist the FAA in meeting its objectives for accommodation of RPA by providing accurate demand forecast information.

This paper addresses topics concerning the development of future Unmanned Aerial Vehicles (UAV) so that they can operate safely within the community of the Federal Aviation Administration (FAA) and the International Civil Aviation Organization (ICAO). Studies by the Defense Science Board; the Office of Science and Technology; Government Accountability Office; and the Congressional Research Service: Library of Congress; have all emphasized that soon there will be a significant number of UAVs operating sideby- side with manned civil aircraft in the FAA’s National Airspace System (NAS). It is anticipated that future UAVs will start to perform many of the dull, dirty, and dangerous civilian missions. In 2006, about 600 UAVs are expected to be manufactured in the US alone. Therefore, there is an urgent need for developers of UAVs to understand safety certification for operations in the NAS, and that safety certification starts with the safety requirements; safety design; safe development process; safety verification; and safe operating procedures in the planned operational environment. This paper presents and discusses the experiences and lessons gained by the authors on the development of end-toend aviation safety; from the safety requirements to their final safety certification in the planned operational environment. The authors’ previous and ongoing works include the Global Navigation Satellite Systems (GNSS) systems such as the GPS; WAAS; LAAS; MSAS; GPS III; and different operational environments at different airports such as Incheon International Airport. The FAA started with the two traditional safety certifications processes: airborne system certification and ground system certification; governed mostly by Federal Aviation Regulations (FAR) and FAA’s Acquisition Management System (AMS), respectively. However, when FAA started to implement GNSS, the lines between airborne and ground systems became blurred, and as a result many current Communications, Navigation, Surveillance, and Air Traffic Management (CNS/ATM) systems have to follow both processes. We will also discuss the current Safety Management Systems being developed by both FAA and ICAO as an integrated, systematic, and harmonized approach and process to sort out the differing safety requirements from FAA, SAE-ARP, MIL-STD, RTCA, ASTM, ICAO, etc. to provide the eventual safe operations within the NAS. The discussion will then focus on the real world of institutional, operational, and technical issues for safety certification worldwide. Despite the numerous standards and guidance documents, many past safety analyses and studies were guided by best practices that had been used successfully in the past to streamline the process of providing all the required documentations and verifications. One such area is in the use of the “Integrity Panel of Experts” that had been used successful in the implementation of safety for the FAA’s Wide Area Augmentation System (WAAS) and Local Area Augmentation System (LAAS) programs. In many areas of the world, safety certification from a technical point of view is only the first step to achieving safety certification to fly in their national and sovereign airspace. Finally, we will show the first steps that UAV manufactures need to consider if they want their UAVs to be approved to fly safely in different national civilian airspace.

Air traffic management decision support tools have shown the capability to incr ease arrival traffic throughput of congested Terminal Radar Approach Control (TRACON) facilities without significantly impacting air traffic controller workload. NASA Ames Research Center, in cooperation with the Federal Aviation Administration (FAA), is playing a leading role in identifying air traffic management problems, developing and prototyping concepts, and performing field trials for such decision support tools. The Center -TRACON Automation System (CTAS) is a suite of decision support tools devel oped by NASA Ames Research Center, and is included in the FAA's Free Flight Program. This paper describes the concept and development plan of the Expedite Departure Path (EDP) component of CTAS. EDP is a decision support tool aimed at providing TRACON Tr affic Management Coordinators (TMCs) with pertinent departure traffic loading and scheduling information, and radar controllers with advisories for tactical control of TRACON departure traffic. EDP employs the CTAS trajectory synthesis routine to provide conflict -free altitude, speed and heading advisories. These advisories will assist the TRACON departure controller in efficiently sequencing, spacing and merging departure aircraft into the en route traffic flow. The anticipated benefits of EDP include a reduction in airborne delay for departure aircraft, reduced fuel burn and reduced noise impact due to expedited climb trajectories. EDP will eventually share information with both surface and arrival decision support tools to form an integrated decision support system capable of planning, coordinating and executing highly efficient terminal airspace operations.

The International Civil Aviation Organization (ICAO) Manual on Flight and Flow Information for a Collaborative Environment (FF-ICE) outlines the components and requirements to achieve a common picture in global Air Traffic Management (ATM) for implementation readiness in 2020. To prepare for this global implementation by ATM stakeholders and ensure the technical feasibility and interoperability across implementations, the Federal Aviation Administration (FAA) initiated the International Interoperability Harmonization and Validation project (IIH&V) to validate the FF-ICE Planning Provisions [1], FF-ICE Implementation Guidance [2], and message exchanges in a lab environment. This paper explores the technical components of the FAA's IIH&V exercise while the complementary paper “An Operational Approach to FF-ICE Planning and Global Harmonization through IIH&V” explores operational components of the exercises. In order to effectively validate the FF-ICE Planning provisions and implementation guidance, it was required that the exercises focus on two areas. The first focus area was ensuring that future versions of the ICAO exchange models (primarily FIXM) were prepared to support FF-ICE. The second focus area was identifying system-level implementation decisions that could affect international interoperability, critical components to implementation readiness. Thus, the role of the ICAO exchange models, the role of data governance, and the feedback communicated to the FIXM Configuration Control Board (CCB) and the ATM Requirements Performance Panel (ATMRPP) are discussed herein. The FAA split the focus of the IIH&V project into three sequential validation exercises, with each exercise integrating more advanced systems into the lab infrastructure. For each exercise, the paper presents a system architecture diagram as well as an overview of the messages exchanged across systems. Several validation exercises have taken place in the context of a mixed mode environment in which only a subset of actors is FF-ICE capable, thus requiring the use of “bridging” capabilities such as data transformation to allow systems to remain interoperable despite differences in message type, version, etc. Validation 1 included the initial “mixed mode” environment envisioned in 2020 in which FF-ICE capable actors have implemented pre-departure FF-ICE message exchanges for pre-departure trajectory negotiations, while other actors are still using legacy capabilities. Validation 2 expanded trajectory negotiation into the post-departure portion of an active flight using bi-directional Air-to-Ground (A/G) System Wide Information Management (SWIM) data links, and single or bi-directional communications between the Flight Management System (FMS) and Electronic Flight Bag (EFB). Validation 3 further explored post-departure trajectory negotiation leveraging Data Communication (DataComm) messages in addition to A/G SWIM. The outputs and results of the IIH&V project outlined in this paper bring clarity to FF-ICE Planning Provisions and Implementation Guidance, as well as support the authoring organizations refining these documentations through input given to the ATMRPP. The IIH&V project also begins laying the groundwork for the development of FF-ICE Execution for post-departure.

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 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

For a system to be managed, it must be measured. The National Airspace System (NAS), the collection of airspace and airport resources in and under the control of the United States (U.S.), is a very complex system which presents great challenges of measurement and management. One key measure of en route airspace efficiency is flying time, i.e., the amount of time it takes for an aircraft to travel through en route airspace on its flight from origin to destination airport. A number of factors influence flying time, the most obvious being the winds. Other important factors are: traffic congestion, air traffic management (ATM) interventions, route structure, industry strategies, and weather. In this paper, we calculate en route flying time in the aggregate and compare a subset of data for the years 2001 and 2002. We account for aircraft equipment type and adjusted flying time for wind effects. In addition, we have selected for analysis a sample of only good weather days (15 in each of the 2 years). The results show that 2002 has slightly shorter flying times than 2001, on the order of 20–25 seconds shorter. Although we did not investigate the causes for the change, we conjecture that the lower levels of traffic after the Fall of 2001 are causing less congestion and less delay. New automation and procedural initiatives may also be contributing to the improvement. Introduction The Federal Aviation Administration (FAA) is charged with the safe, efficient movement of air traffic in U.S. airspace. In recent years, the FAA has begun initiatives to measure efficiency of airspace usage. The measurements are useful for determining the impact of automation and procedural enhancements. Such measurements are also useful for identifying problem areas in the NAS, which can be targeted for remediation. Changes in efficiency may be examined as a function of time. This study assessed system efficiency using en route flying times, comparing 2001 versus 2002. Although several studies have been performed in the past calculating changes in en route flying distance, the authors are familiar with one time-based analysis in the open literature. Bolczak, et al. used Estimated Time of Arrival (ETA) data to analyze a trend in flying time, and discover some year-to-year changes. Other studies of flight times are in the literature, though they have not compared actual or modeled values across years. An early simulation of Free Flight, Ball, et al. analyzed flying times for flights without the constraints of route structuring. Efficiency may be defined as the level of utilization of a resource, with consideration of the cost or effort undertaken to achieve that level. † Free Flight is an industry/government initiative which provides greater freedom for pilots and airlines to select planned and flown routes and take-off times. Free Flight supports collaboration between airspace managers and airspace users. 1 American Institute of Aeronautics and Astronautics Willemain examined sources of variability in flying times for certain city pairs. The Bureau of Transportation Statistics has collected flight time statistics and hosts a web site which allows the public to access this information. In this study, results are presented in aggregated form, with some detail with respect to city pairs. Differences were found in the flying time metrics: 2002 has slightly lower flying times than 2001. The causes for this difference are not discussed.

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

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

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.

One of the most restrictive aspects in today’s Air Traffic Management (ATM) system is that aircraft are still largely limited to flying routes defined by fixed, ground-based navaids. With current air travel experiencing increasing amount of delay, it is evident that this inflexible jet route system will not be able to handle future demand, especially when jet routes get closed by hazardous weather constraints. However, not having any route structure in the AS might not be a viable approach either as it could lead to high complexity and low traffic pattern predictability. For use in the ext Generation ATM system ( extGen), the etwork Flow Organizer ( FO) has been designed as a Traffic Management Initiative (TMI) to control the topology of the air traffic flow network and its flow properties. The FO alleviates en route congestion by creating airspace capacity through dense, automatically monitored traffic flows. This is in contrast to other TMIs that restrict capacity, such as Airspace Flow Program, miles-in-trail, ground delays and ground stops. The FO forms and dissolves flows based on demand and the necessity to resolve demand-capacity imbalances, thus providing structure only when needed without unnecessarily constraining traffic. Such flows allow parallel lanes of traffic, creating high traffic throughput with low complexity and workload. The flows are dynamically relocated throughout the day to be safe from weather hazards and to avoid Special Use Airspace. A concept for protected slot templates is established to allow conflict free intersection of flows and turning at intersections.

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.

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints