When roadworks are performed, one of the impacts is delays in the traffic. Partly because of reduced speed limits past the work zones but mainly because of the facility's limited quality of service. This could result in significant delays for the road users with lost work and spare time as a consequence. The lost time in congestions could have been used more efficiently. It is therefore of interest to estimate these delays for different types of roadworks to calculate its macro economical impact. The estimations may be a factor in different types of planning, for example in roadwork planning, in work zone scheduling or in life cycle assessments. The estimations can be conducted using different methods, such as analytical, queuing theory or macrosimulation. However, to include the details of specific work zones an analysis method of higher detail is required. First to include varying geometric details, secondly due to the higher level of detail in the representation of the traffic. Therefore may microsimulation be a suitable analysis method. The purpose of this master thesis was to investigate microsimulations suitability for work zone analysis, by using the software AIMSUN. AIMSUN were different work zone types modeled, first of highways. Later was a 2-lane road modeled for which the traffic control was a fixed signal, then controlled by a simpler extension developed in C++ that simulated a flagger control. Using a developed helper program, the work zone models could be simulated for a large set of daily flow levels and heavy vehicle ratios. Together with corresponding results for normal conditions, it was possible to calculate the delays. The results were relationships between the total delays, for various heavy vehicle ratios. A concern with the results was deviations of the delays for different daily flows and heavy vehicle ratios. Even if the deviations will not have any greater impact on the total cost, they may be unwanted in decision models. Processing of the result may therefore be necessary. This master thesis has showed that microsimulation may be a suitable method to analyze work zone impacts. Using the high level of detail, work zones special character can be captured. Possible future work is to improve the reliability of results by performing validations of results with real work zone data to compare speeds, headways and queue lengths. Additional work may include to consider the diversion delays. / När vägarbeten utförs uppkommer fördröjningar i trafiken. Delvis på grund av reducerade hastigheter förbi arbetsområdet men i huvudsak på grund av minskad framkomlighet. Det kan resultera i betydande fördröjningar för trafikanterna med förlorad arbets- och fritid som följd.  Den förlorade tiden i köer kunde då ha använts effektivare. Det är då av intresse att kunna uppskatta fördröjningarna vid olika typer av vägarbeten för att beräkna dess samhällsekonomiska påverkan. Uppskattningarna kan då vara en faktor vid beslut inom olika typer av planering, till exempel vid vägarbetsplanering, schemaläggning eller livscykelanalyser. För att göra dessa uppskattningar kan olika metoder användas, till exempel analytiskt, med köteori eller makrosimulering. Men för att kunna inkludera detaljerna vid olika vägarbeten krävs en analysmetod med högre detaljnivå. Dels för att få med geometriska detaljer, dels detaljnivån i representation av trafiken. Då kan mikrosimulering vara en lämplig metod. Syftet med det här examensarbetat var att undersöka mikrosimulerings användbarhet vid vägarbetesanalyser, det genom att använda programvaran AIMSUN. I AIMSUN modellerades olika typer av vägarbeten, först för motorvägar. Sedan modellerades en 2-fältsväg där trafikkontrollen dels sköttes av en skyttelsignal, sedan utvecklades ett enklare tillägg i C++ som simulerade en flaggvakt som istället skötte trafikkontrollen. Med hjälp av ett utvecklat program kunde modellerna simuleras för en stor mängd olika dygnsflöden och andel tung trafik. Tillsammans med motsvarande grundscenario kunde fördröjningarna beräknas. Resultaten blev samband mellan totala fördröjningen under dagen beroende på dygnsflödet, det för olika andelar tung trafik. Ett bekymmer med resultaten var variationer i fördröjningarna mellan olika dygnsflödesnivåer och andelar tung trafik. Även om variationerna inte ger så stort utslag på totala kostnaden, så kan variationerna vara icke önskvärda i till exempel beslutsmodeller. En viss bearbetning av resultaten kan då vara nödvändig. Det här examensarbetet har visat att mikrosimulering kan vara en lämplig metod för att analysera vägarbetens påverkan. Med dess höga detaljnivå har vägarbetenas olika karaktär kunnat återskapas. Möjligt framtida arbete är att öka pålitligheten i resultaten genom validation med trafikdata från verkliga vägarbeten med jämförelser mellan hastigheter, avstånd och kölängder. Ytterligare arbete kan vara att uppskatta fördröjningar av att fordon väljer omvägar för att undvika vägarbetenas köer.

We study the problem of joint congestion control, routing and MAC layer scheduling in multi-hop wireless mesh networks, where the nodes in the network are subjected to energy expenditure rate constraints. As wireless scenario does not allow all the links to be active all the time, only a subset of given links can be active simultaneously. We model the inter-link interference using the link contention graph. All the nodes in the network are power-constrained and we model this constraint using energy expenditure rate matrix. Then we formulate the problem as a network utility maximization (NUM) problem. We notice that this is a convex optimization problem with affine constraints. We apply duality theory and decompose the problem into two sub-problems namely, network layer congestion control and routing problem, and MAC layer scheduling problem. The source adjusts its rate based on the cost of the least cost path to the destination where the cost of the path includes not only the prices of the links in it but also the prices associated with the nodes on the path. The MAC layer scheduling of the links is carried out based on the prices of the links. The optimal scheduler selects that set of non-interfering links, for which the sum of link prices is maximum. We study the effects of energy expenditure rate constraints of the nodes on the maximum possible network utility. It turns out that the dominant of the two constraints namely, the link capacity constraint and the node energy expenditure rate constraint affects the network utility most. Also we notice the fact that the energy expenditure rate constraints do not affect the nature of optimal link scheduling problem. Following this fact, we study the problem of distributed link scheduling. Optimal scheduling requires selecting independent set of maximum aggregate price, but this problem is known to be NP-hard. We first show that as long as scheduling policy selects the set of non-interfering links, it can not go unboundedly away from the optimal solution of network utility maximization problem. Then we proceed and evaluate a simple greedy scheduling algorithm. Analytical bounds on performance are provided and simulations indicate that the greedy heuristic performs well in practice.

Η τεχνολογία της αναγνώρισης και παρακολούθησης αεροσκαφών βρίσκει ποικίλες εφαρμογές σε όλους τους τομείς της αεροναυσιπλοΐας, πολιτικούς και στρατιωτικούς, από τον έλεγχο και τη ρύθμιση της εναέριας κυκλοφορίας σε πολιτικά αεροδρόμια έως το χειρισμό και την καθοδήγηση αντιαεροπορικών όπλων για στρατιωτικούς σκοπούς (π.χ. το σύστημα TAS του αντιαεροπορικού συστήματος MIM-23B Hawk). Έως σήμερα, γνωστές μέθοδοι υλοποίησης αποτελούν οι ραδιοεντοπιστές (radar), οι υπέρυθρες και οι θερμικές κάμερες, τα οποία είναι εγκατεστημένα σε επίγειους σταθμούς, σε κινούμενες μονάδες και σε αεροσκάφη. Το σύστημα που δημιουργήθηκε και θα παρουσιαστεί στην παρούσα διπλωματική εργασία αποτελεί μια εναλλακτική μέθοδο υλοποίησης της αναγνώρισης και της παρακολούθησης ιπτάμενων αντικειμένων, που εκμεταλλεύεται το οπτικό φάσμα με τη χρήση μιας οπτικής κάμερας ενσωματωμένης σε ένα σερβοκινητήρα. Σε σημεία όπου είναι δύσκολο να εφαρμοσθεί κάποια άλλη τεχνολογία ή σε σημεία που δεν καλύπτονται από άλλες συσκευές ανίχνευσης (π.χ. radar), η συσκευή μας προσφέρει όμοιες υπηρεσίες και συμπληρώνει πιθανά χάσματα ακάλυπτων περιοχών. Συγκεκριμένα, μέσω του λογισμικού που έχει αναπτυχθεί, όταν κάποιος στόχος (αεροσκάφος) εισέλθει στο οπτικό πεδίο της κάμερας, ανιχνεύεται και αναγνωρίζεται. Στη συνέχεια ο σερβοκινητήρας παρακολουθεί τον στόχο τροφοδοτούμενος με δεδομένα της θέσης και της ταχύτητάς του, ενώ βρίσκεται σε συνεχή επικοινωνία με την κάμερα. Όλα τα παραπάνω έχουν αναπτυχθεί ώστε να λειτουργούν σε συνθήκες πραγματικού χρόνου. Παρά την απουσία μιας θεωρητικής παρουσίασης ή μιας ολοκληρωμένης λύσης οπτικής αναγνώρισης και παρακολούθησης αεροσκαφών, η αναζήτηση και μελέτη της διεθνούς βιβλιογραφίας μας έδωσε το θεωρητικό υπόβαθρο για την κατανόηση του προβλήματος και ταυτόχρονα τη δυνατότητα να συνδυάσουμε τεχνικές και μεθόδους για την επίτευξη του στόχου μας. Για την επιτυχή αναγνώριση και παρακολούθηση των στόχων δημιουργήθηκαν διάφορα μοντέλα προσομοίωσης για τον έλεγχο της συμπεριφοράς μεμονομένων χαρακτηριστικών. Συγκεκριμένα, στο υποσύστημα της αναγνώρισης του στόχου μοντελοποιήθηκε αρχικά μια μέθοδος εξαγωγής της θέσης βασισμένη στο χρώμα του στόχου σε περιβάλλον Matlab/Simulink. Στη συνέχεια η ίδια μέθοδος μεταφέρθηκε σε περιβάλλον LabVIEW για να εμπλουτισθεί με διάφορες άλλες μεθόδους βασισμένες σε ένα σύνολο από χαρακτηριστικά που θα αναλυθούν στη συνέχεια. Το τελικό μοντέλο αποτελεί συνδυασμό των μεθόδων του αθροίσματος απολύτων διαφορών, της οπτικής ροής, της εξαγωγής χρωματικών και σχηματικών χαρακτηριστικών, της κανονικοποιημένης εττεροσυσχέτισης και άλλων λογικών μεθόδων και βελτιστοποιήσεων τους. Για την επίτευξη μιας επιτυχυμένης παρακολούθησης ενός “κλειδωμένου” στόχου, δοκιμάστηκαν και έγιναν πολλές προσομοιώσεις με διαφορετικούς τύπους ελεγκτών. Συγκεκριμένα η δυναμική του μοντέλου που δημιουργήθηκε, εξαρτάται από ένα συνδυασμό ελεγκτών θέσεως, ταχύτητας και άλλων παραμέτρων. Αυτά εξασφαλίζουν ένα ευσταθές και γραμμικοποιημένο σύστημα παρακολούθησης, ικανό να παρακολουθήσει οποιοδήποτε στόχο με τη προϋπόθεση ότι τα χαρακτηριστικά του στόχου καθώς και η κατάστασή του (θέση, ταχύτητα κτλ.), ικανοποιούν τις απαιτήσεις του αλγορίθμου αναγνώρισης και είναι μέσα στις εργοστασιακές δυνατότητες του συστήματος. Το μοντέλο αυτό αναπτύχθηκε και υλοποιήθηκε σε περιβάλλον LabVIEW, όπως και οι μετρήσεις και προσομοιώσεις που έγιναν πάνω σε αυτό. Όλες οι παραπάνω μέθοδοι συνεργάζονται και είναι ικανοί να δώσουν ακριβή αποτελέσματα θέσης πραγματικών στόχων κατά τη διάρκεια της ημέρας ακόμα και κάτω από δύσκολες συνθήκες (όπως συννεφιά, χαμηλή φωτεινότητα, παρεμβολή αντικειμένων) σε πραγματικό χρόνο. Η ακραία μεταβολή των περιβαλλοντικών συνθηκών θα επηρρέαζε οποιοδήποτε οπτικό σύστημα, συνεπώς και το παρόν. Περιγραφή των παραγόντων που επηρρεάζουν το σύστημά μας θα γίνει στη συνέχεια. / The technology of aircraft recognition and tracking applies in various applications in all areas of air navigation, civil and military, from air traffic control and regulation at civilian airports to anti-aircraft weapon handling and guidance for military purposes (e.g the TAS system of MIM-23B Hawk anti-aircraft system). To date, known methods of implementation are the radar, infrared and thermal cameras, which are installed at ground stations, in moving plants and aircrafts. The system that was created and is presented in this thesis is an alternative implementation of identifying and tracking flying objects, which operates in the optical spectrum using an optical camera built into a servomotor (pan-tilt unit – PTU). In regions where is difficult for one technology to be applied or in areas that are not covered by other detection devices (e.g. radar), our device offers similar services and complements potential gaps that arise by uncovered areas. Specifically, through the software we developed, when a target (aircraft) enters the field of view of our camera, it is detected and identified. Then the PTU, fed with data of target position and velocity, tracks the aircraft while keeps in constant communication with the camera. All the above have been developed to operate in real time. Despite the lack of a theoretical presentation or a complete solution of optical aircraft recognition and tracking, search and study of literature has given us the theoretical background for understanding the problem and making it possible to combine techniques and methods to achieve our goal. For the successful identification and monitoring of the targets, various simulation models were created to control the behavior of isolated features. Specifically, for the target recognition subsystem a method for extraction of the position based on the color of the target was initially modeled in Matlab/Simulink environment. Then the same method was implemented in LabVIEW to be enriched with several other methods based on a set of features that will be discussed below. The final model is a combination of the sum of absolute differences between two images, the extraction of color and shape profiles, the normalized cross-correlation and other logical methods and their optimizations. In order a successful tracking of a “locked” target to be achieved, there have been many tests and carried out many simulations with different types of controllers. Specifically, the dynamic of the model which was created, depends on a combination of position/velocity controllers and other parameters. These provide a stable and linearized tracking system, capable to follow any target under the condition that the characteristics of the target and its current status (position, speed, etc.) meet the requirements of the recognition algorithm and is within the capabilities of the system. The model was developed and implemented in the LabVIEW environment, as well as measurements and simulations were carried out in it. All these methods work and are able to give accurate results of the position of real targets during the day, even under difficult circumstances (such as clouds, decreased sky brightness etc) in real time. The extreme variation of environmental conditions would affect any optical system and hence could affect ours as well. Description of the factors that affect our system will be presented.

The theme of this student thesis is {\clq}qAnalysis of the machinery as a component of the accident system in the area of traffic operations of self-propelled motor vehicles``. It deals with problems of traffic and main causes of deaths and injuries in road accidents, and proposes a possibility of their elimination. The solution is based on the fact that most of the road accidents are caused by drivers themselves who don´t pay enough attention by driving or their behaviour behind the wheel isn´t sufficient. The proposed solution is supposed to monitor the traffic in real time and in co-operation with specially-designed program installed in the electronic control unit of the vehicle it´s able to notify the driver of dangers around him. The system is also able to dynamically control the traffic in order to prevent traffic-jams, accidents on pedestrian crossings, etc. As a result of system implementation the author estimates rapid decrease in fatal road accidents, serious injuries and physical damages.

This thesis addresses the problems of collision avoidance and coalition formation of multiple UAVs in high density traffic environments, proposes simple and efficient algorithms as solutions, and discusses their applications in multiple UAV missions. First, the problem of collision avoidance among UAVs is considered and deconfliction algorithms are proposed. The efficacy of the proposed algorithms is tested using simulations involving random flights in high density traffic. Further, the proposed collision avoidance algorithms are implemented using realistic six degree of freedom UAV models. The studies in this thesis show that implementation of the proposed collision avoidance algorithms leads to a safer and efficient operational airspace occupied by multiple UAVs. Next, coalition formation in a search and prosecute mission involving a large number of UAVs and targets is considered. This problem is shown to be NP-hard and a sub-optimal but polynomial time coalition formation strategy is proposed. Simulations are carried out to show that this coalition formation algorithm works well. The coalition formation algorithm is then extended to handle situations where the UAVs have limited communication ranges. Finally, this thesis considers some multiple UAV missions that require the application of collision avoidance and coalition formation techniques. The problem of multiple UAV rendezvous is tackled by using (i) a consensus among the UAVs to attain rendezvous and (ii) the collision avoidance algorithm previously developed for safety. The thesis also considers a search and prosecute mission where the UAVs also have to avoid collisions among one another. In summary, the main contributions of this thesis include (a) novel collision avoidance algorithms, which are conceptually simple and easy to implement, for resolving path conflicts – both planar and three dimensional – in a high density traffic airspace with UAVs in free flight and (b) efficient coalition formation algorithms for search and prosecute task with large number of UAVs and targets where UAVs have limited communication ranges and targets are maneuvering. Simulations to evaluate the performance of algorithms based on these concepts to carry out realistic tasks by UAV swarms are also given.

Many sequential decision making problems under uncertainty arising in engineering, science and economics are often modelled as Markov Decision Processes (MDPs). In the setting of MDPs, the goal is to and a state dependent optimal sequence of actions that minimizes a certain long-term performance criterion. The standard dynamic programming approach to solve an MDP for the optimal decisions requires a complete model of the MDP and is computationally feasible only for small state-action MDPs. Reinforcement learning (RL) methods, on the other hand, are model-free simulation based approaches for solving MDPs. In many real world applications, one is often faced with MDPs that have large state-action spaces whose model is unknown, however, whose outcomes can be simulated. In order to solve such (large) MDPs, one either resorts to the technique of function approximation in conjunction with RL methods or develops application specific RL methods. A solution based on RL methods with function approximation comes with the associated problem of choosing the right features for approximation and a solution based on application specific RL methods primarily relies on utilizing the problem structure. In this thesis, we investigate the problem of choosing the right features for RL methods based on function approximation as well as develop novel RL algorithms that adaptively obtain best features for approximation. Subsequently, we also develop problem specie RL methods for applications arising in the areas of wireless sensor networks and road traffic control. In the first part of the thesis, we consider the problem of finding the best features for value function approximation in reinforcement learning for the long-run discounted cost objective. We quantify the error in the approximation for any given feature and the approximation parameter by the mean square Bellman error (MSBE) objective and develop an online algorithm to optimize MSBE. Subsequently, we propose the first online actor-critic scheme with adaptive bases to find a locally optimal (control) policy for an MDP under the weighted discounted cost objective. The actor performs gradient search in the space of policy parameters using simultaneous perturbation stochastic approximation (SPSA) gradient estimates. This gradient computation however requires estimates of the value function of the policy. The value function is approximated using a linear architecture and its estimate is obtained from the critic. The error in approximation of the value function, however, results in sub-optimal policies. Thus, we obtain the best features by performing a gradient descent on the Grassmannian of features to minimize a MSBE objective. We provide a proof of convergence of our control algorithm to a locally optimal policy and show numerical results illustrating the performance of our algorithm. In our next work, we develop an online actor-critic control algorithm with adaptive feature tuning for MDPs under the long-run average cost objective. In this setting, a gradient search in the policy parameters is performed using policy gradient estimates to improve the performance of the actor. The computation of the aforementioned gradient however requires estimates of the differential value function of the policy. In order to obtain good estimates of the differential value function, the critic adaptively tunes the features to obtain the best representation of the value function using gradient search in the Grassmannian of features. We prove that our actor-critic algorithm converges to a locally optimal policy. Experiments on two different MDP settings show performance improvements resulting from our feature adaptation scheme. In the second part of the thesis, we develop problem specific RL solution methods for the two aforementioned applications. In both the applications, the size of the state-action space in the formulated MDPs is large. However, by utilizing the problem structure we develop scalable RL algorithms. In the wireless sensor networks application, we develop RL algorithms to find optimal energy management policies (EMPs) for energy harvesting (EH) sensor nodes. First, we consider the case of a single EH sensor node and formulate the problem of finding an optimal EMP in the discounted cost MDP setting. We then propose two RL algorithms to maximize network performance. Through simulations, our algorithms are seen to outperform the algorithms in the literature. Our RL algorithms for the single EH sensor node do not scale when there are multiple sensor nodes. In our second work, we consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the -greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through numerical experiments, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method. In the context of road traffic control, optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users. This problem is hard to solve when simultaneously considering all the junctions in the road network. So, we propose a decentralized multi-agent reinforcement learning (MARL) algorithm for solving this problem by considering each junction in the road network as a separate agent (controller) to obtain dynamic TSC policies. We propose two approaches to minimize the average delay. In the first approach, each agent decides the signal duration of its phases in a round-robin (RR) manner using the multi-agent Q-learning algorithm. We show through simulations over VISSIM (microscopic traffic simulator) that our round-robin MARL algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm over two real road networks. In the second approach, instead of optimizing green light duration, each agent optimizes the order of the phase sequence. We then employ our MARL algorithms by suitably changing the state-action space and cost structure of the MDP. We show through simulations over VISSIM that our non-round robin MARL algorithms perform significantly better than the FST, SAT and the round-robin MARL algorithms based on the first approach. However, on the other hand, our round-robin MARL algorithms are more practically viable as they conform with the psychology of road users.

Les visualisations de données permettent de transmettre de l’information aux utilisateurs. Pour explorer et comprendre les données, les utilisateurs sont amenés à interagir avec ces visualisations.Toutefois, l’interaction avec les visualisations modifie le visuel. Pour éviter des changements brusques et garder l’utilisateur focalisé sur les objets graphiques d’intérêt, des transitions visuelles sont nécessaires pour accompagner les modifications de la visualisation. Ces transitions visuelles peuvent être codées sous la forme d’animations, ou de techniques qui permettent de faire des correspondances, ou des liens avec des données représentées sur plusieurs affichages. Le premier objectif de cette thèse était d’étudier les bénéfices et les propriétés des animations pour l’exploration et la compréhension de grandes quantités de données multidimensionnelles. Nous avons établi en conséquence une taxonomie des transitions animées en visualisation d’information basée sur les tâches des utilisateurs. Cette taxonomie a permis de constater qu’il n’existe pas de contrôle utilisateur sur la direction des objets durant l’animation. Nous avons donc proposé des interactions pour le contrôle de la direction des objets graphiques lors d’une transition animée. D’autre part, nous avons étudié une technique de transition animée mettant en jeu une rotation 3D entre visualisations. Nous avons identifié les avantages qu’elle pouvait apporter et en avons proposé une amélioration.Le second objectif était d’étudier les transitions visuelles dans le domaine du Contrôle du Trafic Aérien. En effet, les contrôleurs utilisent de nombreuses visualisations qui comportent des informations étalées et dupliquées sur plusieurs affichages: l’écran Radar, le tableau de strips, des listes spécifiques d’avions (départ, arrivées) etc. Ainsi dans leur activité, les Contrôleurs Aériens réalisent des transitions visuelles en recherchant et en reliant de l’information à travers les différents affichages. Nous avons étudié comment les animations pouvaient être utilisées dans le domaine du contrôle aérien en implémentant un prototype d’image radar regroupant trois visualisations usuelles pour instrumenter l’activité de supervision du trafic aérien. / Data visualizations allow information to be transmitted to users. In order to explore and understand the data, it is often necessary for users to manipulate the display of this data. When manipulating the visualization, visual transitions are necessary to avoid abrupt changes in this visualization, and to allow the user to focus on the graphical object of interest. These visual transitions can be coded as an animation, or techniques that link the data across several displays. The first aim of this thesis was to examine the benefits and properties of animated transitions used to explore and understand large quantities of multidimensional data. In order to do so, we created a taxonomy of existing animated transitions. This taxonomy allowed us to identify that no animated transition currently exists that allows the user to control the direction of objects during the transition. We therefore proposed an animated transition that allows the user to have this control during the animation. In addition, we studied an animated transition technique that uses 3D rotation to transition between visualizations. We identified the advantages of this technique and propose an improvement to the current design. The second objective was to study the visual transitions used in the Air Traffic Control domain. Air Traffic Controllers use a number of visualizations to view vast information which is duplicated in several places: the Radar screen, the strip board, airplane lists (departures/arrivals) etc. Air traffic controllers perform visual transitions as they search between these different displays of information. We studied the way animations can be used in the Air Traffic Control domain by implementing a radar image prototype which combines three visualizations typically used by Air Traffic Controllers.

Air traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.

Space debris growth and an influx in space traffic will create a need for increased space traffic management. Due to orbital population density and likely future growth, the implementation of a slot architecture to Sun-synchronous orbit is considered in order to mitigate conjunctions among active satellites. This paper furthers work done in Sun-synchronous orbit slot architecture design and focuses on two main aspects. First, an in-depth relative motion analysis of satellites with respect to their assigned slots is presented. Then, a method for developing a slot architecture from a specific set of user defined inputs is derived.

Purpose - This bachelor thesis compares high-speed rail (HSR) transport with air transport. The investigation considers physical fundamentals, energy consumption, environmental impact, infrastructure and investment, market situations, passenger's selection criteria to choose transportation options, and overall economics. --- Methodology - The thesis combines an investigation of physical principles with a literature review. --- Findings - Steel wheels on steel rails show by far less rolling resistance to support the train's weight than drag due to lift (induced drag) to support the aircraft's weight. This leads to less energy consumption. HSR trains use electricity from an overhead line. Hence, the environmental impact of HSR also depends much on how the electricity is produced. Airplanes only need an air traffic control environment to connect airports. In contrast, HSR needs infrastructure to connect stations. The amount of necessary infrastructure depends on the geological conditions. For example, crossing mountains means high investment. Longer passages over water are infeasible for HSR. High-speed rail is superior to air transport when connecting megacities because the trains have higher transport capacity, offer higher service frequencies and mission reliability, shorter total travel time, shorter access time to stations, shorter unproductive waiting time in stations and potentially lower travel costs. HSR is a strong competitor to airline services and has replaced some short range flights. A comparison of HSR in different world regions shows differences in the market situation and in passenger's selection criteria for transportation options. --- Research limitations - The potential of high-speed rail was investigated mainly on busy routes with high service frequencies. A comprehensive network comparison between high-speed trains and airplanes was not done and could lead to somewhat different results. --- Practical implications - The report tries to contribute arguments to the discussion about alternatives to air travel. --- Social implications - With more knowledge people can make an educated choice between transport options, can vote with their feet, and can take a firm position in the public discussion. --- Originality/value - A general comparison of HSR and air transport from physical fundamentals to economics seemed to be missing.