Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice

ESTIMATION AND REPRESENTATION OF AIR TRAFFIC FLOWS INDICES IN TERMINAL CONTROL AREAS

У статті проаналізовано дослідження щодо організації потоків повітряного руху та пропускної здатності. Розглянуто питання щодо сукупності показників повітряного руху, кількісної оцінки потоків повітряного руху, прикладних методик та відповідного програмного забезпечення. Запропоновано принципи детальної оцінки та аналізу статистичних даних щодо потоків повітряного руху в термінальних диспетчерських районах.В статье проанализированы исследования в области организации потоков воздушного движения. Рассмотрены вопросы в отношении совокупности показателей воздушного движения, количественной оценки потоков воздушного движения, прикладных методик и соответствующего программного обеспечения. Предложены принципы детальной оценки и анализа статистических данных по потокам воздушного движения в терминальных диспетчерских районах.The article deals with the analysis of the research conducted in the field of air traffic flow and capacity management. Set of air traffic indices, air traffic flows quantitative estimation, applied techniques and software have been reviewed. Principles of detailed estimation and further analysis of air traffic flows statistics in Terminal controlareas have been proposed

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of automation were proposed for integrated flight planning and re-routing functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is provided. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, airspace and airport operations

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Machine learning for aircraft trajectory prediction: a solution for pre-tactical air traffic flow management

Pla de Doctorats Industrials de la Generalitat de Catalunya(English) The goal of air traffic flow and capacity management (ATFCM) is to ensure that airport and airspace capacity meet traffic demand while optimising traffic flows to avoid exceeding the available capacity when it cannot be further increased. In Europe, ATFCM is handled by EUROCONTROL, in its role of Network Manager (NM), and comprises three phases: strategic, pre-tactical, and tactical. This thesis is focused on the pre-tactical phase, which covers the six days prior to the day of operations. During the pre-tactical phase, few or no flight plans (FPLs) have been filed by airspace users (AUs) and the only flight information available to the NM are the so-called flight intentions (FIs), consisting mainly of flight schedules. Trajectory information becomes available only when the AUs send their FPLs. This information is required to ensure a correct allocation of resources in coordination with air navigation service providers (ANSPs). To forecast FPLs before they are filed by the AUs, the NM relies on the PREDICT tool, which generates traffic forecasts for the whole European Civil Aviation Conference (ECAC) area according to the trajectories chosen by the same or similar flights in the recent past, without taking advantage of the information on AU choices encoded in historical data. The goal of the present PhD thesis is to develop a solution for pre-tactical traffic forecast that improves the predictive performance of the PREDICT tool while being able to cope with the entire set of flights in the ECAC network in a computationally efficient manner. To this end, trajectory forecasting approaches based on machine learning models trained on historical data have been explored, evaluating their predictive performance. In the application of machine learning techniques to trajectory prediction, three fundamental methodological choices have to be made: (i) approach to trajectory clustering, which is used to group similar trajectories in order to simplify the trajectory prediction problem; (ii) model formulation; and (iii) model training approach. The contribution of this PhD thesis to the state of the-art lies in the first two areas. First, we have developed a novel route clustering technique based on the area comprised between two routes that reduces the required computational time and increases the scalability with respect to other clustering techniques described in the literature. Second, we have developed, tested and evaluated two new modelling approaches for route prediction. The first approach consists in building and training an independent machine learning model for each origin destination (OD) pair in the network, taking as inputs different variables available from FIs plus other variables related to weather and to the number of regulations. This approach improves the performance of the PREDICT model, but it also has an important limitation: it does not consider changes in the airspace structure, thus being unable to predict routes not available in the training data and sometimes predicting routes that are not compatible with the airspace structure. The second approach is an airline-based approach, which consists in building and training a model for each airline. The limitations of the first model are overcome by considering as input variables not only the variables available from the FIs and the weather, but also airspace restrictions and route characteristics (e.g., route cost, length, etc.). The airline-based approach yields a significant improvement with respect to PREDICT and to the OD pair-based model, achieving a route prediction accuracy of 0.896 (versus PREDICT’s accuracy of 0.828), while being able to deal with the full ECAC network within reasonable computational time. These promising results encourage us to be optimistic about the future implementation of the proposed system.(Català) L’objectiu de la gestió de demanda i capacitat de trànsit aeri (ATFCM per les sigles en anglès) és garantir que la capacitat aeroportuària i de l’espai aeri satisfacin la demanda de trànsit mentre s’optimitzen els fluxos per evitar excedir la capacitat disponible quan aquesta no es pot augmentar més. A Europa, l’ATFCM està a càrrec d’EUROCONTROL, i consta de tres fases: estratègica, pre-tàctica i tàctica. Aquesta tesi se centra en la pre-tàctica, que inclou els sis dies previs al dia d’operacions. Durant la fase pre-tàctica, els de l'espai aeri han presentat pocs o cap pla de vol i l’única informació sobre els vols disponible són els anomenats intencions de vol (principalment els horaris). La informació de la trajectòria només està disponible quan els usuaris envien els seus pla. Aquesta informació és necessària per assegurar una assignació correcta de recursos en coordinació amb els proveïdors de serveis de. Per predir els plans abans que siguin presentats, EUROCONTROL es recolza en l'eina PREDICT, que genera prediccions de trànsit d'acord amb les trajectòries escollides per vols similars el passat recent, sense aprofitar la informació sobre les decisions en dades històriques. L'objectiu de la present tesi doctoral és millorar l'exercici predictiu de l'eina PREDICT mitjançant el desenvolupament d'una eina que pugui gestionar tots els vols a Europa de manera eficient. Per fer-ho, s’han explorat diferents enfocaments de predicció de trajectòries basats en models d’aprenentatge automàtic entrenats amb dades històriques, avaluant l’exercici de la predicció. A l’hora d’aplicar les tècniques d’aprenentatge automàtic per a la predicció de trajectòries, s’han identificat tres eleccions metodològiques fonamentals: (i) el clustering de trajectòries, que s’utilitza per agrupar trajectòries similars per simplificar el problema de predicció de trajectòries; (ii) la formulació del model d’aprenentatge automàtic; i (iii) l’aproximació seguida per entrenar el model. La contribució d’aquesta tesi doctoral a l’estat de l’art es troba a les dues primeres àrees. Primer, hem desenvolupat una nova tècnica de clustering de rutes, basada en l’àrea compresa entre dues rutes, que redueix el temps computacional requerit i augmenta l’escalabilitat respecte a altres tècniques de clustering descrites a la literatura. En segon lloc, hem desenvolupat, provat i avaluat dos nous enfocaments de modelatge per a la predicció de rutes. El primer enfocament consisteix a construir i entrenar un model d’aprenentatge automàtic independent per a cada parell de d'aeroports a la xarxa, prenent com a entrades diferents variables disponibles de les intencions més altres variables relacionades amb el clima i el nombre de regulacions. Aquest enfocament millora el rendiment del model PREDICT, però també té una limitació important: no considera canvis en l’estructura de l’espai aeri, per la qual cosa no podeu predir rutes que no estan disponibles a les dades d’entrenament i, de vegades, podeu predir rutes que no són compatibles amb l’estructura de l’espai aeri. El segon enfocament, basat en les aerolínies, consisteix a construir i entrenar un model independent per a cada aerolínia. Les limitacions del primer model se superen en considerar com a variables d’entrada no només les variables disponibles dels intencions i el clima, sinó també les restriccions de l’espai aeri i les característiques de la ruta (p. ex., cost de la ruta, longitud, etc.). L’enfocament basat en aerolínies produeix una millora significativa respecte a PREDICT i al model basat en parells d'aeroports, aconseguint una precisió de predicció de ruta del 0,896 (comparant amb la precisió de PREDICT del 0,828), alhora que el problema pot escalar a tota l'àrea al complet amb un temps de computació raonable.(Español) El objetivo de la gestión de demanda y capacidad de tráfico (ATFCM por sus siglas en inglés) es garantizar que la capacidad aeroportuaria y del espacio aéreo satisfagan la demanda de tráfico mientras se optimizan los flujos para evitar exceder la capacidad disponible cuando esta no se puede aumentar más. En Europa, el ATFCM está a cargo de EUROCONTROL y consta de tres fases: estratégica, pre-táctica y táctica. Esta tesis se centra en la pre-táctica, que abarca los seis días previos al día de operaciones. Durante la fase pre-táctica, los usuarios del espacio aéreo han presentado pocos o ningún plan de vuelo y la única información sobre los vuelos disponible para EUROCONTROL son las llamados Intenciones de vuelo, que consisten principalmente en los horarios. La trayectoria está disponible sólo cuando los usuarios envían sus planes. Esta información es necesaria para asegurar una correcta asignación de recursos en coordinación con los provedores de servicios de navegación aérea de los distintos estados. Para predecir los FPLs antes de que sean presentados, EUROCONTROL se apoya en la herramienta PREDICT, que genera predicciones de tráfico de acuerdo las trayectorias elegidas por vuelos similares en el pasado reciente, sin aprovechar la información sobre las decisiones en datos históricos. El objetivo de la presente tesis doctoral es mejorar el desempeño predictivo de la herramienta PREDICT mediante el desarrollo de una herramienta que pueda gestionar todos los vuelos en Europa de una forma eficiente. Para ello, se han explorado diferentes enfoques de predicción de trayectorias basados en modelos de aprendizaje automático. A la hora de aplicar las técnicas de aprendizaje automático para predicción de trayectorias, se han identificado tres elecciones metodológicas fundamentales: (i) el clustering de trayectorias, que se utiliza para agrupar trayectorias similares a fin de simplificar el problema de predicción de trayectorias; (ii) la formulación del modelo de aprendizaje automático; y (iii) la aproximación seguida para entrenar el modelo. La contribución de esta tesis doctoral al estado del arte se encuentra en las dos primeras áreas. Primero, hemos desarrollado una novedosa técnica de clustering de rutas, basada en el área comprendida entre dos rutas, que reduce el tiempo computacional requerido y aumenta la escalabilidad con respecto a otras técnicas de clustering en la literatura. En segundo lugar, hemos desarrollado, probado y evaluado dos nuevos enfoques de modelado para la predicción de rutas. El primer enfoque consiste en construir y entrenar un modelo de aprendizaje automático independiente para cada par de aeropuertos en la red, tomando como entradas diferentes variables disponibles de las intenciones de vuelo más otras variables relacionadas con la meteorología y el número de regulaciones. Este enfoque mejora el rendimiento del modelo PREDICT, pero también tiene una limitación importante: no considera cambios en la estructura del espacio aéreo, por lo que no xvii puede predecir rutas que no están disponibles en los datos de entrenamiento y, a veces, puede predecir rutas que no son compatibles con el estructura del espacio aéreo. El segundo enfoque, basado en las aerolíneas, consiste en construir y entrenar un modelo independiente para cada aerolínea. Las limitaciones del primer modelo se superan al considerar como variables de entrada no solo las variables disponibles de las FIs y la meteorología, sino también las restricciones del espacio aéreo y las características de la ruta (p. ej., coste de la ruta, longitud, etc.). El enfoque basado en aerolíneas produce una mejora significativa con respecto a PREDICT y al modelo basado en pares de aeropuertos, logrando una precisión de predicción de ruta de 0,896 (frente a la precisión de PREDICT de 0,828), a la vez que puede lidiar con toda la red en un tiempo de computación razonable. Estos prometedores resultados nos animan a ser optimistas sobre una futura implementación del sistema propuesto.Ciència i tecnologies aeroespacial

Upravljanje putanjama vazduhoplova u kontroli letenja na pre-taktičkom i taktičkom nivou

Global air traffic demand is continuously increasing, and it is predicted to be tripled by 2050. The need for increasing air traffic capacity motivates a shift of ATM towards Trajectory Based Operations (TBOs). This implies the possibility to design efficient congestion-free aircraft trajectories more in advance (pre-tactical, strategic level) reducing controller’s workload on tactical level. As consequence, controllers will be able to manage more flights. Current flow management practices in air traffic management (ATM) system shows that under the present system settings there are only timid demand management actions taken prior to the day of operation such as: slot allocation and strategic flow rerouting. But the choice of air route for a particular flight is seen as a commercial decision to be taken by airlines, given air traffic control constraints. This thesis investigates the potential of robust trajectory planning (considered as an additional demand management action) at pre-tactical level as a mean to alleviate the en-route congestion in airspace. Robust trajectory planning (RTP) involves generation of congestion-free trajectories with minimum operating cost taking into account uncertainty of trajectory prediction and unforeseen event. Although planned cost could be higher than of conventional models, adding robustness to schedules might reduce cost of disruptions and hopefully lead to reductions in operating cost. The most of existing trajectory planning models consider finding of conflict-free trajectories without taking into account uncertainty of trajectory prediction. It is shown in the thesis that in the case of traffic disturbances, it is better to have a robust solution otherwise newly generated congestion problems would be hard and costly to solve. This thesis introduces a novel approach for route generation (3D trajectory) based on homotopic feature of continuous functions. It is shown that this approach is capable of generating a large number of route shapes with a reasonable number of decision variables. Those shapes are then coupled with time dimension in order to create trajectories (4D)...Globalna potražnja za vazdušnim saobraćajem u stalnom je porastu i prognozira se da će broj letova biti utrostručen do 2050 godine. Potreba za povećanjem kapaciteta sistema vazdušnog saobraćaja motivisala je promene u sistemu upravljanja saobraćajnim tokovima u kome će u budućnosti centralnu ulogu imati putanje vazduhoplova tzv. “trajectory-based” koncept. Takav sistem omogućiće planiranje putanja vazduhoplova koje ne stvaraju zagušenja u sistemu na pre-taktičkom nivou i time smanjiti radno opterećenje kontrolora na taktičkom nivou. Kao posledica, kontrolor će moći da upravlja više letova nego u današnjem sistemu. Današnja praksa upravljanja saobraćajnim tokovima pokazuje da se mali broj upravljačkih akcija primenjuje pre dana obavljanja letova npr.: alokacija slotova poletanja i strateško upravljanje saobraćajnim tokovima. Međutim izbor putanje kojom će se odviti let posmatra se kao komercijalna odluka aviokompanije (uz poštovanje postavljenih ograničenja od strane kontrole letenja) i stoga je ostavljen na izbor avio-kompaniji. Većina, do danas razvijenih, modela upravljanja putanjama vazduhoplova ima za cilj generisanje bez-konfliktnih putanja, ne uzimajući u obzir neizvesnost u poziciji vazduhoplova. U ovoj doktorskoj disertaciji ispitivano je planiranje robustnih putanja vazduhoplova (RTP) na pre-taktičkom nivou kao sredstvo ublažavanja zagušenja u vazdušnom prostoru . Robustno upravljanje putanjama vazduhoplova podrazumeva izbor putanja vazduhoplova sa minimalnim operativnim troškovima koje ne izazivaju zagušenja u vazdušnom prostoru u uslovima neizvesnosti buduđe pozicije vazduhoplova i nepredviđenih događaja. Iako predviđeni (planirani) operativni troškovi robustnih putanja mogu u startu biti veći od operativnih troškova bez-konfliktnih putanja, robusnost može uticati na smanjenje troškove poremećaja putanja jer ne zahteva dodatnu promenu putanja vazduhplova radi izbegavanja konfliktnih situacija na taktičkom nivou. To na kraju može dovesti i do smanjenja stvarnih operativnih troškova. U tezi je pokazano, da je u slučaju poremećaja saobraćaja bolje imati robustno rešenje (putanje), jer novo-nastali problem zagušenosti vazdušnog prostora je teško i skupo rešiti..

Mental Workload in the Explanation of Automation Effects on ATC Performance

Automation has been introduced more and more into the role of air traffic control (ATC). As with many other areas of human activity, automation has the objective of reducing the complexity of the task so that performance is optimised and safer. However, automation can also have negative effects on cognitive processing and the performance of the controllers. In this paper, we present the progress made at AUTOPACE, a European project in which research is carried out to discover what these negative effects are and to propose measures to mitigate them. The fundamental proposal of the project is to analyse, predict, and mitigate these negative effects by assessing the complexity of ATC in relation to the mental workload experienced by the controller. Hence, a highly complex situation will be one with a high mental workload and a low complex situation will be one in which the mental workload is low

Investigation into Air Traffic Complexity as a Driver of a Controller‘s Workload

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