This document provides a comprehensive historical account of the International Advisory Group - Air Navigation Services (ANSA) and shows the achievements, organizational changes, and contributions to the aviation industry. Established in 1967 by German air traffic controllers from the Rhein Control upper airspace ATC center, ANSA is a non-profit organization dedicated to improving air traffic control systems and procedures. Initially formed to support German air navigation authorities and EUROCONTROL, the group expanded its membership to include experts from over 20 countries. In 1985, ANSA moved its legal seat to Switzerland, continuing its mission to enhance flight safety and modernize ATC systems.

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Increasing automation in aviation is impacting the role of the air traffic controller (ATCO). New support tools and changing work environments require the monitoring of multiple display systems and the detection of potential system failures. When training these requirements, eye tracking holds great promise for gaining a deeper insight into trainees’ perceptual and cognitive processes. Because there are hardly any studies on the effects of training on gaze behavior in air traffic control (ATC), the aim of the present work was to evaluate the applicability of the method in this domain. Three experimental studies were conducted with novices with no ATC experience. These investigated whether training effects are reflected not only in common performance measures such as accuracy and speed, but also in gaze parameters such as relative fixation count, time to first fixation, and normalized entropy. They further examined to what extent future monitoring tasks can be trained and what kind of additional factors play a role in this. An adapted version of the abstract monitoring test (MonT) was used to investigate the research questions. Each study consisted of three test blocks in which air traffic had to be monitored in up to three automatically controlled airspaces. In the first study (N = 60), the adapted simulation environment was evaluated, and initial results on the effect of practice were collected. Improvements, which occurred primarily at the beginning of the test, were reflected in a more accurate failure detection performance and a more strategic gaze behavior. The traffic load, and therefore the amount of information to be monitored, played a decisive role in the results. The second study (N = 139) investigated the influence of different interventions for directing attention. Highlighting relevant information (bottom-up approach) moderated the effect of practice significantly more than an attention strategy (top-down approach) or no intervention (control). Relevant information was viewed more frequently and failures were anticipated more easily—even when a manual control task was added. Repeating the test after an average of four months showed little to no significant changes in performance and gaze behavior (N = 19). Overall, with an average detection rate of 83%, the results indicate that future monitoring can be trained to a high level. However, the design of the system, the difficulty of the task, and the prior knowledge of the individual must always be considered. Because it was shown that gaze behavior predicted performance, the recording of eye movements in future ATC training is encouraged. In this context, current developments in the use of artificial intelligence promise to facilitate the classification of individual scan patterns and promote adaptive training.

The Multiple Remote Tower Center concept (mRTC) is a cutting edge project which allows one air traffic control officer (ATCO) to be in charge of multiple remotely situated airports simultaneously. When implemented, it will revolutionise how air traffic is managed at smaller airports and allow for increased efficiency and decreased operational costs. Consequently, at the time of writing a lot of effort is going into evaluating this new way of air traffic management from a safety perspective. Air traffic management has been defined as an issue maintaining situational awareness and exercising control. This thesis aims to investigate how the phenomenon target fascination affects the ATCOs ability to exercise control over its controlled airspace and maintain its situation awareness. It does so by creating a baseline scenario of work in a mRTC, and then comparing the ATCOs performance in the baseline scenario with its performance in the same corresponding scenario, but with elements of target fascination introduced. The differences in the scenarios are analysed using the Contextual Control Model, the Extended Control Model and a holistic framework for studying situation awareness. The analysis shows that target fascination does affect the ATCOs ability to maintain control, but not radically so, and only for a short period of time. The target fascination forces the ATCO to rely on information in the immediate environment to a higher degree than during regular work, as opposed to making decisions based on a holistic understanding of the situation and high level goals. However, once the understanding of the situation have been re-established, the level of control quickly returns to normal levels. Situation awareness is thus a key concept in maintaining control. The situation awareness analysis show that target fascination affects situation awareness by causing the ATCOs understanding of the situation to become outdated without the ATCOs knowledge. Because of this, there may be developments in the situation that the ATCO is not aware of, which hinders it from acting as it normally would. In some cases an intervention from an external actor or element may be necessary to break the fascination and re-establish the ATCOs understanding for the situation. As soon as the fascination is broken, the ATCO quickly takes steps to re-establish its situation awareness and return to normal operations.

In 2014, the Federal Aviation Administration (FAA) highlighted to the U.S. Senate the need to focus on air traffic control (ATC) training to meet job qualification and attrition rates within the career field. One U.S. Department of Defense military service assists the FAA in providing worldwide ATC services. This service is referred to as the agency throughout this paper to ensure confidentiality. The agency's ATC career field manager echoed the FAA's call for action in his 2014 Strategic/Action Plan. In August 2013, the agency's ATC trainer program was published. As of December 2015, the program had not been evaluated. The purpose of this study was to ascertain if the program facilitated the learning of critical ATC on-the-job training skills. An ad hoc expertise-oriented evaluation was conducted using the lenses of andragogy, experiential learning, and instructional system design (ISD). Purposeful sampling procedures were used to select 20 participants across the subgroups of supervisors, trainers, managers, and training developers from 7 focus sites. The semi-structured interviews queried 4 topical areas derived from Kirkpatrick's 4 levels of evaluation model. Data collected via documents and interviews were analyzed using descriptive, emotion, eclectic, and pattern coding. Key findings indicated that the program was not developed compliant with ISD principles and did not promote adult learning as endorsed by andragogy and experiential learning theory. The implications for positive social change include providing stakeholders with data needed to make evidence-based decisions regarding the current and future state of the program. The evaluation report project can be shared with the FAA, an agency partner, and has the potential to create a platform for improved training practices focusing on optimum and successful adult learning transactions.

The aim of self-directed team learning initiatives is to provide a further level of defence against an eventuality by ensuring that air traffic controllers are aware of the sources of human fallibility, and by developing in the individual controllers and air traffic control teams the knowledge, skills and attitudes that will result in the successful management and containment of inadvertent error. To gain a deeper understanding of self-directed team learning, I investigated the role and contribution of self-directed team learning principles and strategies that were present in the South African Air Force air traffic control team-based work environment. This research study was directed by the following primary research questions: Does self-directed team learning impact on the air traffic control work environment, and what is the nature of self-directed team learning’s impact on the air traffic control work environment? Insights gained as a result of this study contributed to the body of research concerned with learning design, development, implementation and evaluation by self-directed teams as well as the air traffic control discipline. In this mixed-method study quantitative data collection was performed by means of a self-directed team learning questionnaire and a learning approach questionnaire, whereas qualitative data collection relied on individual interviews and focus group interviews. This study involved 25 South African Air Force air traffic controllers (from three operational air traffic control centres). The nature of self-directed team learning’s impact on the air traffic control work environment was illustrated by individual and collective (team) views and dynamics. The impact of air traffic control team performances was traced in terms of identified teamwork characteristics, activities, dynamics, performance measures and focus areas and reflective practices. Results of this study indicated that self-directed team learning offered opportunities to individuals and teams to influence air traffic control performances in an air traffic control work environment. A perceived positive relationship between self-directed team learning and air traffic control operational outputs could be traced. Lastly I concluded that self-directed learning by air traffic control teams had an impact on air traffic control operational outcomes, thus contributing towards a critical air traffic control goal – aviation safety. / Thesis (PhD (Currriculum Studies))--University of Pretoria, 2009. / Curriculum Studies / unrestricted

Background: Low morale is classified as a latent condition for performance variability in safety-critical environments. Morale management may assist in the control of performance variability as part of a systems approach to safety. A context-specific model for measuring and managing morale with reference to followership in a safety-critical air traffic control (ATC) environment could not be found. Purpose/Aim: The purpose of this study was to develop a model that enables the measurement and management of air traffic controller (ATCO) team morale. Research Design: An exploratory sequential mixed method design was adopted. A census approach to sampling was used to conduct 21 focus group sessions as the qualitative phase, providing the definition and drivers of morale. The Measure of Morale and its Drivers (MoMaD) survey instrument was created from qualitative data, then administered to 256 ATCOs in the quantitative phase. Statistical methods included exploratory factor analysis, correlation and regression analysis to construct the final MoMaD model. Results: A context-specific definition of morale is provided and communication management, team cohesion, leadership interaction, staff incentive, staffing level, workplace health and safety and mutual trust were found to be the drivers of morale in a safety-critical ATC environment. A single-item measure of perceived morale reflected the state of context-specific ATCO team morale more accurately than an existing generalisable multi-item measure. Conclusion: This study contributes to the body of knowledge by integrating applicable aspects of morale, followership, performance variability and organisational culture and climate in safety-critical ATC environments into a new theoretical framework. The MoMaD instrument is presented as a context-specific model for measuring and managing ATCO team morale in an ATC environment. Recommendations: Future research opportunities include the possible influence of morale as a predictor of morale in safety-critical environments and the development of a context-specific multi-item measure of morale for integration into the MoMaD model. / Business Management / D. B. L.

Der Luftverkehr stellt ein komplexes Gesamtsystem dar, in welchem eine Prozessoptimierung aufgrund zahlreicher und verschiedenartiger Arbeitsabläufe verschiedener Unternehmen nur durch eine übergeordnete Leistungsbewertung möglich ist. Hierfür wurde im Bereich des leistungsbasierten Flughafenmanagements - sowohl auf wissenschaftlicher, als auch industrieller Ebene - bereits eine hohe Zahl individueller Lösungsansätze entwickelt, die jedoch aufgrund ihrer unterschiedlichen Struktur schwer zu vergleichende Ergebnisse hervorbringen. Des Weiteren werden Wechselbeziehungen zwischen ausschließlich eindimensional betrachteten Leistungsindikatoren, welche die verschiedenen Prozessschritte einzeln abbilden, außer Acht gelassen - die Dynamik des Systems spiegelt sich auf diese Art nicht auf Datenebene wider. Abhängigkeiten beeinflussen jedoch maßgeblich das Bewertungsergebnis und sind damit bei der Implementierung von Optimierungsstrategien innerhalb heutiger Konzepte essentiell. Der Kern dieser Dissertation umfasst die erweiterte datenbasierte Betrachtung des Luftverkehrssystems innerhalb des leistungsbasierten Ansatzes und einer damit einhergehenden Entkopplung von Modellierungsansätzen. Dies bedeutet, dass das betrachtete Luftverkehrssystem nur durch Leistungskennwerte beschrieben werden soll (z. B. Verspätungen, meteorologische Messungen). Der Einsatz künstlicher neuronaler Netze erhöht die Möglichkeiten zur Abbildung und Erfassung nicht-linearer und abhängiger Wert-diskreter Zeitreihen, welche im ständigen Vergleichsprozess wesentlich für die Strategiebildung sind. Die Datenquellen beziehen sich einerseits auf mikroskopische Analysen im Bereich des Boardings und damit verbundenen wissenschaftlichen Ausarbeitungen, andererseits auf die Beispielflughäfen Flughafen Hamburg (HAM) und Gatwick Airport (LGW) der Jahre 2012 - 2015 im makroskopischen Fokus. Die Implementierung des Wetters erfolgt über aggregierte meteorologische Kennzahlen, welche auf realen Wettermeldungen des entsprechenden Zeitraums beruhen. Von der Wahl und Definition eines Systems (Boarding, HAM und LGW) ausgehend, erfolgt eine geeignete Datenaggregation, welche Daten zur anschließenden Wissensgenerierung bereitstellt und damit Optimierungsansätze ermöglicht. Im Sinne des wachsenden Interesses der Forschung im Bereich des leistungsbasierten Luftverkehrsmanagements und der heutigen Wichtigkeit von Entscheidungsunterstützungssystemen bei der Strategieentwicklung, fokussiert sich diese Arbeit damit auf die Durchführung multivariater nicht-linearer Zeitreihenanalyse und -vorhersage mit neuronalen Netzen. Die damit einhergehende Nachvollziehbarkeit solcher Datenreihen liefert Möglichkeiten zur Systemidentifikation (datenbasiertes Erlernen der Systemdynamik). Das identifizierte Systemabbild des Luftverkehrs kann folglich für Simulation bekannter Eingabegrößen, als auch für die optimierte Kontrolle des Systems herangezogen werden und umfasst damit wesentliches Erweiterungspotential für heutige Management-Konzepte, um Entwicklungen hin zu einem kooperativen Betrieb zu unterstützen. Ableitend aus der Differenzierung in mehrere gekoppelte Bearbeitungsschritte innerhalb dieser Arbeit, erfolgt eine Fokussierung auf drei Kernfragen: a) Ist eine auf Leistungskennwerten des Luftverkehrs basierende Systemidentifikation mit derzeitigen Paradigmen neuronaler Netze möglich? b) Welche Einschränkungen sind gemäß des unterschiedlichen Charakters der Datensätze zu beachten und wie kann diesen durch eine, das Training der neuronalen Netze vorbereitenden, Datenstrukturanalyse und -Anpassung entgegengewirkt werden? c) Ist auf Basis der trainierten Netze eine erweiterte Optimierung und Vorhersage innerhalb vorhandener Strukturen des leistungsbasierten Luftverkehrsmanagements möglich?:I Grundlagen 1 Leistungsbasiertes Luftverkehrsmanagement 1.1 Allgemeine Definitionen & Begrifflichkeiten 1.2 Leistungsbewertungsrahmenwerke des Luftverkehrs 1.2.1 Interdependenzen von Leistungsindikatoren 1.2.2 Einflussfaktoren auf die Leistungsfähigkeit von Verkehrsflughäfen 1.2.3 Flugmeteorologische Datenaggregation 1.3 Grundkonzepte leistungsbasierten Luftverkehrsmanagements 1.3.1 Airport Collaborative Decision Making (A-CDM) 1.3.2 Total Airport Management (TAM) 1.3.3 Performance Based Airport Management (PBAM) 2 Künstliche neuronale Netze 2.1 Grundverfahren der Computational Intelligence 2.2 Biologische neuronale Netze 2.3 Topologie & Bestandteile künstlicher neuronaler Netze 2.4 Lernvorgang & Fehlerevaluierung 2.5 Netzparadigmen 2.5.1 Feedforward Netzwerke 2.5.2 Rekurrente (rückgekoppelte) Netzwerke 2.5.3 Faltende Netzwerke 2.6 Taxonomie der Zeitreihenverarbeitung mit künstlichen neuronalen Netzen 2.6.1 Zeitreihenregression 2.6.2 Zeitreihenklassifikation II Anwendung künstlicher neuronaler Netze im Luftverkehr 3 Methodische Konzeption 3.1 Datenbasierte Erfassung des Luftverkehrssystems 3.1.1 Prinzip des virtuellen Luftverkehrssystems 3.1.2 Systemidentifikation, Kontrolle & Simulation 3.2 Anwendungsgebiet Turnaround Management 3.2.1 Status Quo 3.2.2 Stochastische Boardingsimulation & Aggregationsmetrik 3.3 Anwendungsgebiet Air Traffic Flow Management 3.3.1 Status Quo 3.3.2 Datengrundlage der Flughäfen Hamburg & London Gatwick 4 Systemidentifikation mit künstlichen neuronalen Netzen 4.1 Modularisierung der Systemidentifikation 4.2 Problemidentifikation & Systemauswahl 4.3 Datenstrukturanalyse & -anpassung 4.3.1 Datenvoranalyse 4.3.2 Datenvorverarbeitung & Fehlerbereinigung 4.3.3 Datenanpassung für maschinelles Lernen 4.4 Paradigmenauswahl & Modellinitialisierung 4.5 Modellanwendung & -Überwachung 4.6 Nachgelagerte Analyse & Modellanpassung 5 Simulation & Kontrolle durch künstliche neuronale Netze 5.1 Sequenzprediktion zur Vorhersage bei bekannten Eingabemengen 5.1.1 Fehlerfortschreitung & Prognoseevaluierung 5.1.2 Robustheitsschätzung der Vorhersage 5.2 Nutzung des Modells als adaptive Kontrollstruktur 5.2.1 Extraktion von Zusammenhängen der Eingabegrößen 5.2.2 Metaheuristische Optimierung der Eingabemengen III Anwendung 6 Anwendungsgebiet Boarding als Teil des Turnaround-Prozesses 6.1 Systemidentifikation des Boarding-Subsystems 6.1.1 Datenstrukturanalyse 6.1.2 Experimenteller Aufbau & Modellanwendung 6.2 Simulation & Kontrolle des Boarding-Subsystems 6.2.1 Vorhersage des Boardingvorgangs unter verschiedenen Randbedingungen 6.2.2 Robustheitsprüfung der adaptiven Kontrollstruktur 6.2.3 Ableitung von Handlungsempfehlungen innerhalb des Prozesses 7 Anwendungsgebiet Air Traffic Flow Management 7.1 Systemidentifikation des Flugbetriebs in Hamburg & London Gatwick 7.1.1 Datenstrukturanalyse 7.1.2 Experimenteller Aufbau & Modellanwendung 7.2 Simulation & Kontrolle des Flugbetriebs in Hamburg & London Gatwick 7.2.1 Vorhersage von Verspätungen unter Berücksichtigung des Wetters 7.2.2 Robustheitsprüfung der adaptiven Kontrollstruktur 7.2.3 Eingabeempfehlungen zur Minimierung der Gesamtverspätung 8 Schlussbetrachtungen 8.1 Zusammenfassende Auswertung der Basisszenarien 8.2 Ausblick

The thesis describes an investigation into Air Traffic Control (ATC) complexity as a contributory factor in changes of controllers' workload. It is considered that ATC complexity, together with equipment interface and procedural demands comprise the task demands imposed on the en-route controller to perform certain activities, which mediated by performance shaping factors create workload. The data used to study this relationship came from ATC real-time simulations completed at EUROCONTROL CRDS in Budapest: recorded flown trajectories, communication performed by the controller (whether with other controllers or with the pilots), data entries related to flight data management, and instantaneous self-assessment ratings of workload provided by the controllers were used. The ATC complexity factors that have been consistently found to be important in the previous studies (related to aircraft density, flight attributes of each individual aircraft, aircraft conflicts and traffic disorder) and for which detailed calculation formula have been reported were selected for further analysis. Since the established set of factors resulted from multiple researches conducted in this field, it was assumed that some of these factors are correlated with one another, overlapping and possibly measuring similar concepts. Therefore, a reduction of the initial set of factors was performed by combining information contained within these factors into a smaller number of new artificial variables and by deleting statistically redundant portions of these factors prior to conducting further analysis. The Principal Component Analysis (PCA), which is the statistical method applied to achieve required reduction, resulted in the overall set of 6 complexity components, whose interpretations are driven by the factors that showed the strongest correlation with that component. In order to establish a link between ATC complexity and a controller's subjective workload, multiple regression analysis was performed, using the complexity components identified in the PCA as predictors of the workload ratings. In addition, some measures of controller’s activity (data entries made by the controllers related to flight data management, cumulative duration of radio calls, i.e. frequency occupancy time, and average duration of single calls) were added to the analysis to test whether information about the controller’s activity could be also useful for predicting workload, once the effect of complexity had been considered, and to verify whether the effect of complexity on workload could be mediated by the effect of complexity on the controller’s activity. The analysis revealed that both ATC complexity and the activities that the controller performs to deal with a demand imposed on him/her give a unique contribution to the prediction of workload ratings and therefore the workload of the controller is determined by both ATC complexity and controller’s activities. In addition, it was assumed that the workload is differently impacted by individual components of complexity, and further statistical analyses were performed to test this assumption. Understanding these differences could in fact facilitate comparison of the complexity levels of a single sector under different conditions, but also comparison of complexity levels of different sectors under same conditions. Firstly the changes in the workload and activities of the controllers under different conditions were investigated using analysis of variance. Subsequently, in order to be able to map these changes on the complexity components, it was necessary also to investigate into the changes that the complexity components undergo when observed under different conditions. The results revealed different behaviour of single complexity components when mapped on the changes recorded in the activities of the controller and workload, demonstrating that changes in controller’s activities and perceived workload are driven by different complexity components in different sectors and under different operational conditions. Shedding light on these contributors to the workload experienced by a controller can greatly facilitate the introduction of any change envisaged for the airspace under consideration. Namely, in the current structure, whenever new procedures or new working methods are subject to possible deployment, the identified complexity components could support the estimation of the impact that those changes would impose on the workload of the controller and further on decision making processes. Additionally, the complexity components are also applicable in the validation of the new concepts and new technologies to be introduced in the system when designing simulation scenarios against which new concepts would be assessed. As also demonstrated by the analysis, the comparison of different sectors, or even different sector designs within the same airspace, could be compared and contribute to the improvement of airspace design. / Die vorliegende Arbeit untersucht die Komplexität der Flugverkehrskontrolle (Air Traffic Control, ATC) als einen wesentlichen Einflussfaktor auf die Arbeitsbelastung des Radarlotsen. Die zentrale Annahme ist dabei, dass die Komplexität der ATC zusammen mit den Anforderungen aus den betrieblichen Rahmenbedingungen (technische Systemschnittstellen und Prozeduren) den Lotsen zu bestimmten Abläufen zwingen, welche die Arbeitsbelastung signifikant beeinflussen. Für die durchgeführten Untersuchungen standen Daten von ATC-Echtzeitsimulationen von EUROCONTROL CRDS Budapest zur Verfügung, die folgende Informationen umfassen: abgeflogene Flugtrajektorien, Kommunikationsprotokolle der Lotsen (untereinander oder zwischen Lotse und Pilot), Daten aus dem flight-data Management und Daten aus der regelmäßigen Selbstbewertung der Lotsen bezüglich ihrer aktuell gefühlten Arbeitsbelastung. Die bereits in früheren Studien identifizierten Komplexitätsvariablen (insbesondere die lokale Flugzeugdichte, spezifische Flugzeugeigenschaften, Konfliktsituationen zwischen Flugzeugen und die Verkehrslage betreffend) sowie hierzu erarbeitete mathematische Vorschriften bilden die Grundlage für die weiterführenden, detaillierten Untersuchungen. Aufgrund der Vielzahl an Komplexitätsvariablen aus diversen wissenschaftlichen Quellen war davon auszugehen, dass Korrelationen unter den Variablen vorliegen. Aus diesem Grund wurden zunächst statistisch redundante Informationen der ursprünglich vorliegenden Variablen reduziert, sodass als Ergebnis neue voneinander unabhängige Faktoren klassifiziert werden konnten. Die hierfür verwendete Hauptkomponentenanalyse (Principal Component Analysis - PCA) führte zu sechs statistisch signifikanten Komplexitätsfaktoren, die anhand der höchsten Korrelation zur zugeordneten Komponente interpretiert wurden. Um die Verbindung zwischen der ATC Komplexität und der subjektiv empfundenen Arbeitsbelastung herzustellen, wurde eine multiple Regressionsanalyse zwischen den Komplexitätsfaktoren und den abgeleiteten Arbeitsbelastungszuständen durchgeführt. Zusätzlich lagen für die Analyse der Arbeitsbelastung auch Daten über die Arbeitsaufgaben des Lotsen vor (bspw. Dateneinträge des Lotsen, Gesamtlänge der Funkanweisungen, durchschnittliche Länge der Funkanweisungen), um zu untersuchen, inwieweit sich aus den aktuell durchgeführten Arbeitsaufgaben bei gegebener Verkehrsnachfrage eine verlässliche Vorhersage über die Arbeitsbelastung ableiten lässt. Die Analyse zur Vorhersage der Arbeitsbelastung konnte zeigen, dass sowohl die ATC Komplexität als auch die aktuellen Arbeitsaufgaben einen individuellen und signifikanten Einfluss haben. Weiterhin wurde unterstellt, dass die spezifischen Komplexitätsfaktoren einen unterschiedlichen Effekt auf die Arbeitsbelastung ausüben. Die Überprüfung dieser Annahme war ebenfalls Bestandteil der umfangreichen statistischen Untersuchungen. Tatsächlich könnte ein fundamentales Verständnis der Komplexitätsgrade den Vergleich einzelner Luftraumsektoren unter verschiedenen operativen Randbedingungen, als auch den Vergleich unterschiedlicher Luftraumsektoren mit vergleichbaren operativen Randbedingungen wesentlich erleichtern. Zuerst wurden die Veränderungen der Arbeitsbelastung und -die Tätigkeiten der Lotsen unter Verwendung einer Varianzanalyse untersucht. Um eine valide Zuordnung zu den Komplexitätsfaktoren sicherzustellen, war es ebenfalls notwendig, die Veränderungen dieser Faktoren und Tätigkeiten unter wechselnden Randbedingungen zu analysieren. Die Analysen zeigen hierbei unterschiedliche Resultate bezüglich der jeweiligen Komplexitätsfaktoren. So beeinflussen die verschiedenen Komplexitätsfaktoren die Handlungsabläufe der Lotsen und die wahrgenommene Arbeitsbelastung, jedoch in Abhängigkeit von den ausgewählten Sektoren und den betrieblichen Randbedingungen. Unter Berücksichtigung dieser erarbeiteten Abhängigkeiten der Arbeitsbelastung des Lotsen können nun die Auswirkungen von Veränderungen im Luftraum zuverlässig bestimmt werden. Gerade in Bezug auf Veränderungen der gegenwärtigen Luftraumstruktur oder die Einführung neuer Prozeduren oder Arbeitsabläufe können die entwickelten Komplexitätsfaktoren bereits frühzeitig Aufschluss darüber geben, welche Konsequenzen solche Veränderungen auf die Arbeitsbelastung der Lotsen nach sich ziehen können und Entscheidungsprozesse unterstützen. Weiterhin sind die entwickelten Komplexitätsfaktoren als Grundlage für die Validierung neuer Konzepte und Technologien, gegebenenfalls unter Verwendung von entwickelten Simulationsszenarien, nutzbar. Darüber hinaus können die Komplexitätsfaktoren für die Gegenüberstellung von verschiedenen Luftraumsektoren genutzt werden und zur Abwägung bzw. Optimierung von Entwürfen eines Luftraumdesigns dienen.

Significant research efforts have been devoted to large-scale dynamical systems, with the aim of understanding their complicated behaviors and managing their responses in real-time. One pivotal technological obstacle in this process is the existence of uncertainty. Although many of these large-scale dynamical systems function well in the design stage, they may easily fail when operating in realistic environment, where environmental uncertainties modulate system dynamics and complicate real-time predication and management tasks. This dissertation aims to develop systematic methodologies to evaluate the performance of large-scale dynamical systems under uncertainty, as a step toward real-time decision support. Two uncertainty evaluation approaches are pursued: the analytical approach and the effective simulation approach. The analytical approach abstracts the dynamics of original stochastic systems, and develops tractable analysis (e.g., jump-linear analysis) for the approximated systems. Despite the potential bias introduced in the approximation process, the analytical approach provides rich insights valuable for evaluating and managing the performance of large-scale dynamical systems under uncertainty. When a system’s complexity and scale are beyond tractable analysis, the effective simulation approach becomes very useful. The effective simulation approach aims to use a few smartly selected simulations to quickly evaluate a complex system’s statistical performance. This approach was originally developed to evaluate a single uncertain variable. This dissertation extends the approach to be scalable and effective for evaluating large-scale systems under a large-number of uncertain variables. While a large portion of this dissertation focuses on the development of generic methods and theoretical analysis that are applicable to broad large-scale dynamical systems, many results are illustrated through a representative large-scale system application on strategic air traffic management application, which is concerned with designing robust management plans subject to a wide range of weather possibilities at 2-15 hours look-ahead time.