Colours play an important role in our everyday life. Yet, it is something that we might not pay too much attention to, it is just there, even if we may have our favourite colours and likewise. However, sometimes the colours have a very specific meaning and is a medium of communication. One example of this is air traffic control systems as the one used in Sweden, Denmark, Austria, Ireland, and Croatia. However, despite using the same system, all but Denmark and Sweden use different colour schemes in the human computer interface of the radar screens. A decision was taken within the common organisation, COOPANS, to change this and harmonize the colour scheme, but how will that be received by the users, the air traffic controllers? This thesis aimed at investigating how usable the controllers in the different countries, except Croatia, found the new colour scheme and how easy they thought it would be to adapt to. The question was how this was affected by the fact they are using different colour schemes today? Data was collected with questionnaires during simulations in high fidelity simulator platforms at the air traffic control centres in Malmö, Copenhagen, Vienna, and Shannon. It was found that there were some differences between the sites which could not be explained by the controlled for factors, age, gender, and experience. Among the differences found, one was that the perceived usability differed between controllers in Malmö and Copenhagen respectively. Hence, since they are using the same colours today, the differences seem to be a result of expectations and opinions about the current colour schemes rather than exactly which colour scheme that are currently used. There was also a trend that the opinions from the first impression seemed to be reinforced within the group during the simulation. The major differences however were found to be on individual level.

Air Traffic Control (ATC) is a safety-critical system which places high demands on air traffic controllers’ (ATCO) multitasking abilities. Having the requisite information for well-informed decision making is central, and as new technologies such as remote towers demand an increase in capacity, efficiency, and safety there is a need for research that informs system development. Adopting a systems perspective, Distributed Cognition is an approach for investigating system functioning, and Resilience Engineering is a way of observing safety factors in everyday work. The purpose of this study is to understand how air traffic controllers work from a distributed cognition perspective, and manage safety in everyday tasks from a resilience perspective. Six observations and six interviews were conducted in a Swedish control tower. The data was analyzed using Distributed Cognition for Teamwork (DiCoT) and Resilience markers (REM), which both focus on the transformation and propagation of information. The results of DiCoT show how cognitive processes in ATCO work are supported in models of physical layout, artefacts, information flow, social organization, and evolutionary design. The results of REM show potential for resilience enhancing behavior in several episodes of ATCO work. Moreover, the results suggest that methods such as DiCoT and REM may work well in the ATC domain, as well as complementary to each other. The results may be used for informing system development, and enable a before-and-after study as the control tower of study will be transformed into a remote tower.

As air traffic is forecasted to increase, air traffic control software subsequently needs to be more sophisticated. To efficiently push development forward, testing is important in order to determine usability. The tests need to be adapted to fit a particular purpose and carried out with methods that preserve the validity of the results. This thesis describes an implementation project carried out at the EUROCONTROL Experimental Centre, Bretigny-sur-Orge, France. The purpose of the project is to create an application that enables a user to create datasets of air traffic to be used for these tests. The application allows for manual work or bulk imports from external data sources. Furthermore it compiles scenarios as output datasets intended for prototype air traffic control software developed at Linköping University. The application design rationale and development process is described. Some time is spent on demonstrating the flexibility of the application and how its usage fits in a bigger picture.

The main aim of the study was to assess the influence of flight delays on business travellers. Studies on flight delays have been done from a number of perspectives; these include the reasons for flights delays, the costs to airlines and airports, the effect on airline scheduling and the impact on airline market share. An area that has received little, if any, attention is the impact of flight delays on business travellers, one of the most lucrative markets for airlines. This study empirically researches the direct cost of flight delays to travellers of a specific corporation. In addition, the use of mobile technology in communicating the occurrence of flight delays to business travellers, and how this could alleviate traveller frustrations, are discussed from a theoretical perspective. The study followed a quantitative methodology to determine man-hours lost and the direct costs of flight delays to travellers from a selected corporation. Two data sets were used, one provided by the corporation on flights undertaken by their corporate travellers over a predetermined period, the other by the Air Traffic and Navigation Services (ATNS) on all flights over the same period. The two sets of data were matched and analysed to determine which flights undertaken by the corporate travellers were delayed, based on actual arrival times, and if any significant relationships could be determined between flight delays and types of traveller (frequent versus infrequent) or specific time periods (time of day, day, week and month). The results indicated that frequent travellers experienced the majority of flight delays, and consequently represented the greatest cost to the corporation. The study also found significant relationships between substantial delays and the month of the year, day of the week, and the time of day flown. The identification of patterns could provide business travellers with the information to better manage their travel arrangements and optimise their travel times and costs. In calculating the direct monetary cost, the value of time lost was found not to constitute a substantial amount to the corporation, but this result must be viewed against the limitations of the study. This study serves to provide a foundation for future research into the cost of flight delays to business travellers. Future research should include larger samples (large global or multiple companies could be used) and extend the time periods for assessing delays. Future studies could also include other direct and indirect costs not covered here and the study could be replicated in different geographical areas, particularly areas with a high density of flights such as Asia, the United States of America and Europe. Copyright / Dissertation (MCom)--University of Pretoria, 2010. / Tourism Management / unrestricted

Thesis analyses present status of air traffic management in European area from historical and legislative point of view. Subjects of thesis are present projects in ATM and future variants of organization of air traffic in EU. Practical part focuses on impact of ATM on air transportation.

The control of multiple unmanned aerial vehicles (UAVs) in a swarm or cooperative team scenario has been a topic of great interest for well over a decade, growing steadily with the advancements in UAV technologies. In the academic community, a majority of the studies conducted rely on simulation to test developed control strategies, with only a few institutions known to have nurtured the infrastructure required to propel multiple UAV control studies beyond simulation and into experimental testing. With the Cal Poly UAV FLOC Project, such an infrastructure was created, paving the way for future experimentation with multiple UAV control systems. The control system architecture presented was built on concepts developed in previous work by Cal Poly faculty and graduate students. An outer-loop formation flight controller based on a virtual waypoint implementation of potential function guidance was developed for use on an embedded microcontroller. A commercially-available autopilot system, designed for fully autonomous waypoint navigation utilizing low cost hardware and open source software, was modified to include the formation flight controller and an inter-UAV communication network. A hardware-in-the-loop (HIL) simulation was set up for multiple UAV testing and was utilized to verify the functionality of the modified autopilot system. HIL simulation results demonstrated leader-follower formation convergence to 15 meters as well as formation flight with three UAVs. Several sets of flight tests were conducted, demonstrating a successful leader-follower formation, but with relative distance convergence only reaching a steady state value of approximately 35 +/- 5 meters away from the leader.

<div>The air traffic system is one of the most complex and safety-critical systems, which is expected to grow at an average rate of 0.9% a year -- from 51.8 million operational activities in 2018 to 62 million in 2039 -- within the National Airspace System. In such systems, it is important to identify degradations in system performance, especially in terms of safety and efficiency. Among the operations of various subsystems of the air traffic system, the arrival and departure operations in the terminal airspace require more attention because of its higher impact (about 75% incidents) on the entire system's safety, ranging from single aircraft incidents to multi-airport congestion incidents.</div><div><br></div><div>The first goal of this dissertation is to identify the air traffic system's degradations -- called anomalies -- in the multi-airport terminal airspace or metroplex airspace, by developing anomaly detection models that can separate anomalous flights from normal ones. Within the metroplex airspace, airport operational parameters such as runway configuration and coordination between proximal airports are a major driving factor in aircraft’s behaviors. As a substantial amount of data is continually recording such behaviors through sensing technologies and data collection capabilities, modern machine learning techniques provide powerful tools for the identification of anomalous flights in the metroplex airspace. The proposed algorithm ingests heterogeneous data, comprising the surveillance dataset, which represents an aircraft’s physical behaviors, and the airport operations dataset, which reflects operational procedures at airports. Typically, such aviation data is unlabeled, and thus the proposed algorithm is developed based on hierarchical unsupervised learning approaches for anomaly detection. This base algorithm has been extended to an anomaly monitoring algorithm that uses the developed anomaly detection models to detect anomalous flights within real-time streaming data.</div><div><br></div><div>A natural next-step after detecting anomalies is to determine the causes for these anomalies. This involves identifying the occurrence of precursors, which are triggers or conditions that precede an anomaly and have some operational correlation to the occurrence of the anomaly. A precursor detection algorithm is developed which learns the causes for the detected anomalies using supervised learning approaches. If detected, the precursor could be used to trigger actions to avoid the anomaly from ever occurring.</div><div><br></div><div>All proposed algorithms are demonstrated with real air traffic surveillance and operations datasets, comprising of departure and arrival operations at LaGuardia Airport, John F. Kennedy International Airport, and Newark Liberty International Airport, thereby detecting and predicting anomalies for all airborne operations in the terminal airspace within the New York metroplex. Critical insight regarding air traffic management is gained from visualizations and analysis of the results of these extensive tests, which show that the proposed algorithms have a potential to be used as decision-support tools that can aid pilots and air traffic controllers to mitigate anomalies from ever occurring, thus improving the safety and efficiency of metroplex airspace operations.</div>

The procedure of testing the implemented software is important and should be an essential and integrated part of the development process. In order for the testing to be meaningful it is important that the testing procedure ensures that the developed software meet certain requirements. The testing procure is often controlled by some sort of test specification. For many companies it is desirable to automate this procure. The focus of this thesis has been to automate a small subpart of the manual tests today performed related to SAAB:s air traffic management system. The automation has been achieved by studying the existing test specification which involves a lot of manual operations and to write software that mimics a few of these test cases. The thesis has resulted in a test framework which automates a small subset of the manual tests performed today. The framework has been designed to be scalable and to easily allow more test cases to be added by the personnel when time permits. The test framework has also been integrated with SAAB:s existing CI/CD workflow.

Cette thèse a pour but de modéliser et d'optimiser deux composantes du système de gestion de flux de trafic aérien : la prédictibilité et la flexibilité. Cette modélisation est équivalente à établir une relation entre la fenêtre temporelle et les taux d'arrivée des avions. Deux approches sont utilisées : l'analyse des données historiques et la modélisation mathématique. L'analyse des données historique a permis de déterminer la fenêtre temporelle raisonnable mais sans pouvoir apporter les améliorations nécessaires pour y arriver. La modélisation mathématique permet non seulement de définir de façon rigoureuse la prédictibilité et la flexibilité mais également de traiter des vols en différents scénarios de priorités. La combinaison de DC algorithme avec des méthodes de résolutions classiques comme Branch and Bound a nettement amélioré la vitesse de la convergence des solutions et donc elle peut être utilisée pour la phase tactique de gestion de flux du trafic aérien. / This thesis aims to model and optimise two components of the air traffic flow management system : predictibility and flexibility. This modelling is equivalent to establishing a relationship between the time window and the rate of arrival flights. Two approachs are used : the analysis of historical data and mathematical modeling. The analysis of historical data was used to establish the relationship between the time window and arrivla rate of flights. It provided the optimal time window but could not show how to modify the system to lead to that time window. Mathematical modeling can not only define the predictability and flexibility in the rigourous manner but also deal with different scenarios of fligths priorities. The combination of DC algorithm with classical methods like Branch and Bound has significantly improved the speed of convergence of solutions and therefore it can be used for the tactical phase of the air traffic flow management.

<div><p>Predictive modeling allows us to analyze historical patterns to forecast future events. When the data available for this analysis is imbalanced or skewed, many challenges arise. The lack of sensitivity towards the class with less data available hinders the sought-after predictive capabilities of the model. These imbalanced datasets are found across many different fields, including medical imaging, insurance claims and financial frauds. The objective of this thesis is to identify the challenges, and means to assess, the application of machine learning to transportation data that is imbalanced and using only one independent variable. </p><p>Airlines undergo a decision-making process on air route addition or deletion in order to adjust the services offered with respect to demand and cost, amongst other criteria. This process greatly affects the topology of the network, and results in a continuously evolving Air Traffic Network (ATN). Organizations like the Federal Aviation Administration (FAA) are interested in the network transformation and the influence airlines have as stakeholders. For this reason, they attempt to model the criteria used by airlines to modify routes. The goal is to be able to predict trends and dependencies observed in the network evolution, by understanding the relation between the number of passengers per flight leg as the single independent variable and the airline’s decision to keep or eliminate that route (the dependent variable). Research to date has used optimization-based methods and machine learning algorithms to model airlines’ decision-making process on air route addition and deletion, but these studies demonstrate less than a 50% accuracy. </p><p>In particular, two machine learning (ML) algorithms are examined: Sparse Gaussian Classification (SGC) and Deep Neural Networks (DNN). SGC is the extension of Gaussian Process Classification models to large datasets. These models use Gaussian Processes (GPs), which are proven to perform well in binary classification problems. DNN uses multiple layers of probabilities between the input and output layers. It is one of the most popular ML algorithms currently in use, so the results obtained using SGC were compared to the DNN model. </p><p>At a first glance, these two models appear to perform equally, giving a high accuracy output of 97.77%. However, post-processing the results using a simple Bayes classifier and using the appropriate metrics for measuring the performance of models trained with imbalanced datasets reveals otherwise. The results in both SGC and DNN provided predictions with a 1% of precision and 20% of recall with an score of 0.02 and an AUC (Area Under the Curve) of 0.38 and 0.31 respectively. The low score indicates the classifier is not performing accurately, and the AUC value confirms the inability of the models to differentiate between the classes. This is probably due to the existing interaction and competition of the airlines in the market, which is not captured by the models. Interestingly enough, the behavior of both models is very different across the range of threshold values. The SGC model captured more effectively the low confidence in these results. In order to validate the model, a stratified K-fold cross-validation model was run. </p>The future application of Gaussian Processes in model-building for decision-making will depend on a clear understanding of its limitations and the imbalanced datasets used in the process, the central purpose of this thesis. Future steps in this investigation include further analysis of the training data as well as the exploration of variable-optimization algorithms. The tuning process of the SGC model could be improved by utilizing optimal hyperparameters and inducing inputs.<br></div><div><div><br></div></div>