A modeling framework to evaluate the costs and benefits of implementation of Controller Pilot Data Link Communication (CPDLC), and Air Traffic Management (ATM) decision support tools is proposed in this paper. The benefit/cost evaluation is carried out for four key alternatives namely alternative A: Do nothing scenario (only voice channel), alternative B: Voice channel supplemented with CPDLC, alternative C: Alternative B with ATM tools in a non-integrated scenario and finally alternative D: Alternative B with ATM tools in an integrated scenario. It is a bi-level model that captures the linkages between various technologies at a lower microscopic level using a daily microscopic model (DATSIM) and transfers the measures of effectives to a higher macroscopic level. DATSIM stands for Data Link and Air Traffic Technologies SIMulation and it simulates air traffic in the enroute sector and terminal airspace for a single day and captures the measures of effectiveness at a microscopic level and feeds its output to the macroscopic annual model which then runs over the entire life cycle of the system. Airspace dwell time benefit data from the microscopic model is regressed into three dimensional benefit surfaces as a function of the equipage level of aircraft and aircraft density and embedded into the macroscopic model. The main function of the annual model is to ascertain economic viability of any deployment schedule or alternative over the entire life cycle of the system. The life cycle cost model is composed of four modules namely: Operational benefits module, Safety benefit module,Technology cost module and Training cost module. Analysis using the model showed that an enroute sector gets congested at aircraft densities greater 630 per day. This is mainly because the controller workload gets saturated at that traffic volume per day. Benefits realized in alternatives B, C and D as compared to alternative A increased exponentially at traffic densities greater than 630 i.e. when controller workload for alternative A becomes saturated. / Master of Science

This report describes how an aircraft creates and flies its User Preferred Trajectory from take-off to landing, based on the objectives and constraints the aircraft is subjected to from a technological and operational viewpoint. A basic description of commercial aircraft operation is given, with an emphasis on identifying the different stakeholders (Air Navigation Service Providers, Airline Operation Center, Pilot/Aircraft, Airport and Civil Aviation Authority). A general description of Instrument Flight Rules operations is also given, together with an explanation of the capabilities of modern flight management systems. The objectives and constraints of the trajectory building process from an aircraft and air traffic management viewpoint are described in Chapter 4. Those are instrumental in understanding how the user preferred trajectory is built. The initial and detail route planning process is then described. The initial route planning is performed long before the flight and usually by the airline operating center, while detail flight planning, including take-off, runway and departure procedure is performed later by the crew. This process is re-performed minutes before take-off, and usually iterated during the flight when the details of approach and landing are communicated to the aircraft crew. The implementation of this user preferred trajectory is explained in terms of the options that the pilots have in the aircraft avionics to perform the mission. The implementation explained in this report is based on the avionics suite of a Boeing 737NG aircraft equipped with the most advanced flight management systems. An implementation of a user preferred trajectory, where the aircraft crew is able to best fulfill their objectives is composed of an idle or near idle descent from the cruise altitude. This type of descent, called an advanced continuous descent approach has been implemented by some air navigation service providers, airlines and airports, based on advanced technology that will be further described in this paper. Those procedures are called Green Approaches. In the last part of this report, the benefits of flying Green Approach procedures are analyzed by means of aircraft simulations. The analysis describes in detail the lateral and vertical trajectories of the Green Approaches at Stockholm’s Arlanda Airport and Brisbane Airport (Australia), together with the calculated advantages in term of fuel consumption, noise and gas emissions. / QC 20101119

No description available.

No description available.

This dissertation contributes to the ongoing national project concerning the \emph{Next Generation Air Transportation System} (NextGen) that endeavors, in particular, to reshape the management of air traffic in the continental United States. Our work is part of this effort and mainly concerns modeling and algorithmic enhancements to the Airspace Planning and Collaborative Decision-Making Model (APCDM). First, we augment the APCDM to study an \emph{Airspace Flow Program} (AFP) in the context of weather-related disruptions. The proposed model selects among alternative flight plans for the affected flights while simultaneously (a) integrating slot-exchange mechanisms induced by multiple Ground Delay Programs (GDPs) to permit airlines to improve flight efficiencies through a mediated bartering of assigned slots, and (b) considering issues related to sector workloads, airspace conflicts, as well as overall equity concerns among the involved airlines in regard to accepted slot trades and flight plans. More specifically, the APCDM is enhanced to include the following: a. The revised model accommodates continuing flights, where some flight cannot depart until a prerequisite flight has arrived. Such a situation arises, for example, when the same aircraft will be used for the departing flight. b. We model a slot-exchange mechanism to accommodate flights being involved in multiple trade offers, and to permit slot trades at multiple GDP airports (whence the flight connection constraints become especially relevant). We also model flight cancelations whereby, if a flight assigned to a particular slot is canceled, the corresponding vacated slot would be made available for use in the slot-exchange process. c. Alternative equity concepts are presented, which more accurately reflect the measures used by the airlines. d. A reduced variant of the APCDM, referred to as \textbf{APCDM-Light}, is also developed. This model serves as a fast-running version of APCDM to be used for quick-turn analyses, where the level of modeling detail, as well as data requirements, are reduced to focus only on certain key elements of the problem. e. As an alternative for handling large-scale instances of APCDM more effectively, we present a \emph{sequential variable fixing heuristic} (SFH). The list of flights is first partitioned into suitable subsets. For the first subset, the corresponding decision variables are constrained to be binary-valued (which is the default for these decision variables), while the other variables are allowed to vary continuously between 0 and 1. If the resulting solution to this relaxed model is integral, the algorithm terminates. Otherwise, the binary variables are fixed to their currently prescribed values and another subset of variables is designated to be binary constrained. The process repeats until an integer solution is found or the heuristic encounters infeasibility. f. We experiment with using the APCDM model in a \emph{dynamic, rolling-horizon framework}, where we apply the model on some periodic basis (e.g., hourly), and where each sequential run of the model has certain flight plan selections that are fixed (such as flights that are already airborne), while we consider the selection among alternative flight plans for other imminent flights in a look-ahead horizon (e.g., two hours). These enhancements allow us to significantly expand the functionality of the original APCDM model. We test the revised model and its variants using realistic data derived from the \emph{Enhanced Traffic Management System} (ETMS) provided by the \emph{Federal Aviation Administration} (FAA). One of the new equity methods, which is based on average delay per passenger (or weighted average delay per flight), turns out to be a particularly robust way to model equity considerations in conjunction with sector workloads, conflict resolution, and slot-exchanges. With this equity method, we were able to solve large problem instances (1,000 flights) within 30 seconds on average using a 1\% optimality tolerance. The model also produced comparable solutions within about 20 seconds on average using the Sequential Fixing Heuristic (SFH). The actual solutions obtained for these largest problem instances were well within 1\% of the best known solution. Furthermore, our computations revealed that APCDM-Light can be readily optimized to a 0.01\% tolerance within about 5 seconds on average for the 1,000 flight problems. Thus, the augmented APCDM model offers a viable tool that can be used for tactical air traffic management purposes as an airspace flow program (particularly, APCDM-Light), as well as for strategic applications to study the impact of different types of trade restrictions, collaboration policies, equity concepts, and airspace sectorizations. The modeling of slot ownership in the APCDM motivates another problem: that of generating detoured flight plans that must arrive at a particular slot time under severe convective weather conditions. This leads to a particular class of network flow problems that seeks a shortest path, if it exists, between a source node and a destination node in a connected digraph $G(N,A)$, such that we arrive at the destination at a specified time while leaving the source no earlier than a lower bounding time, and where the availability of each network link is time-dependent in the sense that it can be traversed only during specified intervals of time. We refer to this problem as the \emph{reverse time-restricted shortest path problem} (RTSP). We show that RTSP is NP-hard in general and propose a dynamic programming algorithm for finding an optimal solution in pseudo-polynomial time. Moreover, under a special regularity condition, we prove that the problem is polynomially solvable with a complexity of order $O(|N / A|)$. Computational results using real flight generation test cases as well as random simulated problems are presented to demonstrate the efficiency of the proposed solution procedures. The current airspace configuration consists of sectors that have evolved over time based on historical traffic flow patterns. \citet{kopardekar_dyn_resect_2007} note that, given the current airspace configuration, some air traffic controller resources are likely under-utilized, and they also point out that the current configuration limits flexibility. Moreover, under the free-flight concept, which advocates a relaxation of waypoint traversals in favor of wind-optimized trajectories, the current airspace configuration will not likely be compatible with future air traffic flow patterns. Accordingly, one of the goals for the \emph{NextGen Air Transportation System} includes redesigning the airspace to increase its capacity and flexibility. With this motivation, we present several methods for defining sectors within the \emph{National Airspace System} (NAS) based on a measure of sector workload. Specifically, given a convex polygon in two-dimensions and a set of weighted grid points within the region encompassed by the polygon, we present several mixed-integer-programming-based algorithms to generate a plane (or line) bisecting the region such that the total weight distribution on either side of the plane is relatively balanced. This process generates two new polygons, which are in turn bisected until some target number of regions is reached. The motivation for these algorithms is to dynamically reconfigure airspace sectors to balance predicted air-traffic controller workload. We frame the problem in the context of airspace design, and then present and compare four algorithmic variants for solving these problems. We also discuss how to accommodate monitoring, conflict resolution, and inter-sector coordination workloads to appropriately define grid point weights and to conduct the partitioning process in this context. The proposed methodology is illustrated using a basic example to assess the overall effect of each algorithm and to provide insights into their relative computational efficiency and the quality of solutions produced. A particular competitive algorithmic variant is then used to configure a region of airspace over the U.S. using realistic flight data. The development of the APCDM is part of an ongoing \emph{NextGen} research project, which envisages the sequential use of a variety of models pertaining to three tiers. The \emph{Tier 1} models are conceived to be more strategic in scope and attempt to identify potential problematic areas, e.g., areas of congestion resulting from a severe convective weather system over a given time-frame, and provide aggregate measures of sector workloads and delays. The affected flow constrained areas (FCAs) highlighted by the results from these \emph{Tier 1} models would then be analyzed by more detailed \emph{Tier 2} models, such as APCDM, which consider more specific alternative flight plan trajectories through the different sectors along with related sector workload, aircraft conflict, and airline equity issues. Finally, \emph{Tier 3} models are being developed to dynamically examine smaller-scaled, localized fast-response readjustments in air traffic flows within the time-frame of about an hour prior to departure (e.g., to take advantage of a break in the convective weather system). The APCDM is flexible, and perhaps unique, in that it can be used effectively in all three tiers. Moreover, as a strategic tool, analysts could use the APCDM to evaluate the suitability of potential airspace sectorization strategies, for example, as well as identify potential capacity shortfalls under any given sector configuration. / Ph. D.

Nos últimos anos, os países desenvolvidos vêm dedicando crescentes esforços para integrar o Veículo Aéreo Não Tripulado (VANT) no espaço aéreo controlado, visando sua utilização para fins civis. Embora este tema ainda não tenha consenso quanto aos critérios a serem adotados, é de comum acordo na comunidade que, no mínimo, devam ser mantidos os atuais níveis de segurança (\"Safety\") praticados pela aviação civil mundial. Neste cenário, a convivência entre aeronaves comerciais, com cada vez mais passageiros e aeronaves não tripuladas, traz sérias preocupações com relação à capacidade que o sistema atual de navegação, controle, vigilância e de Gerenciamento de Tráfego Aéreo tem para lidar com situações de perigo decorrentes da aproximação entre estas duas categorias de aeronaves. Neste contexto, esta tese propõe uma modelagem de um VANT, tendo-se como ponto de partida os conceitos de robô móvel, cujo modelo de inteligência é fundamentado em Inteligência Artificial Distribuída (IAD), implementável segundo o paradigma de Sistemas Multi-Agentes (SMA) e que leve em consideração os principais requisitos de \"Safety\" exigidos pelo \"Communication Navigation System/Air Traffic Management\" (CNS/ATM), de modo a permitir a futura inserção destas aeronaves no espaço aéreo controlado. / During the last years, developed countries are conducting efforts to integrate Unmanned Aircraft Vehicles (UAVs) to the controlled airspace, aiming at their civilian use. So far, there has been no common consensus on the criteria to be adopted by the community that should, at least, keep the minimum safety levels international aviation has already attained. In this scenario, commercial aircrafts - with more and more passengers - and UAVs will share the same space. There will be a lot of concern related to the actual navigation, control and surveillance system capacity as well as to the air traffic control management ability to handle potentially dangerous situations due to the approximation between aircrafts of these two categories. Based on this scenario, this thesis proposes an UAV modeling having as starting point the mobile robot concept, of which the intelligence model based on Distributed Artificial Intelligence, can be implemented by using the Multi Agent Systems paradigm. This paradigm should take the main safety requirements as an obligation, as defined by the Communication Navigation System/Air Traffic Management (CNS/ATM), as a way of handling the future insertion of UAVs into the controlled airspace.

Nos últimos anos, os países desenvolvidos vêm dedicando crescentes esforços para integrar o Veículo Aéreo Não Tripulado (VANT) no espaço aéreo controlado, visando sua utilização para fins civis. Embora este tema ainda não tenha consenso quanto aos critérios a serem adotados, é de comum acordo na comunidade que, no mínimo, devam ser mantidos os atuais níveis de segurança (\"Safety\") praticados pela aviação civil mundial. Neste cenário, a convivência entre aeronaves comerciais, com cada vez mais passageiros e aeronaves não tripuladas, traz sérias preocupações com relação à capacidade que o sistema atual de navegação, controle, vigilância e de Gerenciamento de Tráfego Aéreo tem para lidar com situações de perigo decorrentes da aproximação entre estas duas categorias de aeronaves. Neste contexto, esta tese propõe uma modelagem de um VANT, tendo-se como ponto de partida os conceitos de robô móvel, cujo modelo de inteligência é fundamentado em Inteligência Artificial Distribuída (IAD), implementável segundo o paradigma de Sistemas Multi-Agentes (SMA) e que leve em consideração os principais requisitos de \"Safety\" exigidos pelo \"Communication Navigation System/Air Traffic Management\" (CNS/ATM), de modo a permitir a futura inserção destas aeronaves no espaço aéreo controlado. / During the last years, developed countries are conducting efforts to integrate Unmanned Aircraft Vehicles (UAVs) to the controlled airspace, aiming at their civilian use. So far, there has been no common consensus on the criteria to be adopted by the community that should, at least, keep the minimum safety levels international aviation has already attained. In this scenario, commercial aircrafts - with more and more passengers - and UAVs will share the same space. There will be a lot of concern related to the actual navigation, control and surveillance system capacity as well as to the air traffic control management ability to handle potentially dangerous situations due to the approximation between aircrafts of these two categories. Based on this scenario, this thesis proposes an UAV modeling having as starting point the mobile robot concept, of which the intelligence model based on Distributed Artificial Intelligence, can be implemented by using the Multi Agent Systems paradigm. This paradigm should take the main safety requirements as an obligation, as defined by the Communication Navigation System/Air Traffic Management (CNS/ATM), as a way of handling the future insertion of UAVs into the controlled airspace.

Present day air traffc control is reaching its operational limits and accommodating future traffic growth will be a challenging task for air traffic service providers and airline operators. Free Flight is a proposed transition from a highly-structured and centrally-controlled air traffic system to a self-optimized and highly-distributed system. In Free Flight, pilots will have the flexibility of real-time trajectory planning and dynamic route optimization given airspace constraints (traffic, weather etc.). A variety of advanced air traffc management (ATM) concepts are proposed as enabling technologies for the realization of Free Flight. Since these concepts can be exposed to unforeseen and challenging scenarios in Free Flight, they need to be validated and evaluated in order to implement the most effective systems in the field. Evaluation of advanced ATM concepts is a challenging task due to the limitations in the existing scenario generation methodologies and limited availability of a common platform (air traffic simulator) where diverse ATM concepts can be modeled and evaluated. Their rigorous evaluation on safety metrics, in a variety of complex scenarios, can provide an insight into their performance, which can help improve upon them while developing new ones. In this thesis, I propose a non-propriety, non-commercial air traffic simulation system, with a novel representation of airspace, which can prototype advanced ATM concepts such as conflict detection and resolution, airborne weather avoidance and cockpit display of traffic information. I then propose a novel evolutionary computation methodology to algorithmically generate a massive number of conflict scenarios of increasing complexity in order to evaluate conflict detection algorithms. I illustrate the methodology in detail by quantitative evaluation of three conflict detection algorithms, from the literature, on safety metrics. I then propose the use of data mining techniques for the discovery of interesting relationships, that may exist implicitly, in the algorithm's performance data. The data mining techniques formulate the conflict characteristics, which may lead to algorithm failure, using if-then rules. Using the rule sets for each algorithm, I propose an ensemble of conflict detection algorithms which uses a switch mechanism to direct the subsequent conflict probes to an algorithm which is less vulnerable to failure in a given conflict scenario. The objective is to form a predictive model for algorithm's vulnerability which can then be included in an ensemble that can minimize the overall vulnerability of the system. In summary, the contributions of this thesis are: 1. A non-propriety, non-commercial air traffic simulation system with a novel representation of airspace for efficient modeling of advanced ATM concepts. 2. An Ant-based dynamic weather avoidance algorithm for traffic-constrained enroute airspace. 3. A novel representation of 4D air traffic scenario that allows the use of an evolutionary computation methodology to evolve complex conflict scenarios for the evaluation of conflict detection algorithms. 4. An evaluation framework where scenario generation, scenario evaluation and scenario evolution processes can be carried out in an integrated manner for rigorous evaluation of advanced ATM concepts. 5. A methodology for forming an intelligent ensemble of conflict detection algorithms by data mining the scenario space.

In ongoing efforts to balance air traffic demand and airspace capacity, airspace complexity stands as a fundamental research problem. Taking a more analytic approach, this thesis proposes that airspace complexity can be described in terms of how the airspace (together with the traffic inside it and the traffic control algorithm) responds to disturbances. The response of the airspace to a disturbance is captured by the degree of control activity required to accommodate such disturbance. Furthermore, since the response of the airspace depends on the disturbance, this thesis introduces a complexity map which shows how an airspace responses to a set of different disturbances. Among the many possible types of disturbances, this thesis considers an aircraft entering into the airspace, and the proposed method of describing airspace complexity is illustrated with examples. The time evolution of a complexity map is investigated using a statistical approach. In addition, the proposed method is illustrated in relation to current and future traffic flow management concepts. It is also shown that the proposed method can be applied to airspace design problems.

Afin de pouvoir subvenir aux futurs besoins en matière de transport aérien il est nécessaire d'augmenter la capacité de l'espace aérien. Les contrôleurs aériens, qui occupent une place centrale dans la gestion du trafic, doivent quotidiennement faire face à des situations conflictuelles (conflits) lors desquelles deux vols risquent de violer les normes de séparation en vigueur si aucune modification de trajectoire n'est envisagée. La détection et la résolution des conflits potentiels contribuent à augmenter la charge de travail des contrôleurs et peuvent potentiellement les conduire à diriger les vols vers des zones moins denses de l'espace aérien, induisant a posteriori un retard pour les vols. Le problème de la capacité de l'espace aérien peut donc être abordé en régulant les flux de trafic de façon réduire la quantité de conflits aériens. L'objectif de cette thèse est de mettre au point une méthodologie destinée à minimiser les risques de conflits aériens en modifiant légèrement les vitesses des appareils. Cette approche est principalement motivée par les conclusions du projet ERASMUS portant sur la régulation de vitesse subliminale. Ce type de régulation a été conçu de façon à ne pas perturber les contrôleurs aériens dans leur tâche. En utilisant de faibles modulations de vitesse, imperceptibles par les contrôleurs aériens, les trajectoires des vols peuvent être modifiées pour minimiser la quantité totale de conflits et ainsi faciliter l'écoulement du trafic dans le réseau aérien. La méthode retenue pour mettre en œuvre ce type de régulation est l'optimisation sous contrainte. Dans cette thèse, nous développons un modèle d'optimisation déterministe pour traiter les conflits à deux avions. Ce modèle est par la suite adapté à la résolution de grandes instances de trafic en formulant le modèle comme un Programme Linéaire en Nombres Entiers. Pour reproduire des conditions de trafic réalistes, nous introduisons une perturbation sur la vitesse des vols, destinée à représenter l'impact de l'incertitude en prévision de trajectoire dans la gestion du trafic aérien. Pour valider notre approche, nous utilisons un outil de simulation capable de rejouer des journées entières de trafic au dessus de l'espace aérien européen. Les principaux résultats de ce travail démontrent les performances du modèle de détection et de résolution de conflits et soulignent la robustesse de la formulation face à l'incertitude en prévision de trajectoire. Enfin, l'impact de notre approche est évalué à travers divers indicateurs propres à la gestion du trafic aérien et valide la méthodologie développée. / As global air traffic volume is continuously increasing, it has become a priority to improve air traffic control in order to deal with future air traffic demand. One of the current challenges regarding air traffic management is the airspace capacity problem, which is acknowledged to be correlated to air traffic controllers' workload. Air traffic controllers stand at the core of the traffic monitoring system and one of their main objective is to ensure the separation of aircraft by anticipating potential conflicts. Conflict detection and resolution are likely to increase workload and may lead them to reroute aircrafts to less dense areas, triggering off flight delay. The airspace capacity problem can hence be tackled by regulating air traffic flow in order to reduce the global conflict quantity. The objective of this thesis is to develop a methodology aiming at minimizing potential conflicts quantity by slightly adjusting aircraft speeds in real time. This approach is mainly motivated by conclusions of the ERASMUS project on subliminal speed control, which was designed to keep air traffic controllers unaware of the ongoing regulation process. By focusing on low magnitude speed modulations, aircraft trajectories can be modified to reduce the quantity of conflicts and smoothen air traffic flow in the airspace network. The method used to carry out this type of regulation is constraint optimization. In this thesis, we develop a deterministic optimization model for two-aircraft conflicts which is then adapted to large scale instances using Mixed-Integer Linear Programming. In order to reproduce realistic navigation conditions, uncertainty on aircraft speeds is introduced with the goal of modeling the impact of trajectory prediction uncertainty in air traffic management. To validate our approach, a simulation device capable of simulating real air traffic data over the European airspace is used. Main results of this work reveal a significant conflict quantity reduction and demonstrate the robustness of the developed model to the uncertainty in trajectory prediction. Finally, the impact of our model on air traffic flow is measured through several air traffic management indicators and validates the proposed methodology.