Digital growth and the adaption of internet-based solutions, particularly artificial intelligence and machine learning, have dramatically changed the way design is done today. This rapid change in technology has challenged the level of automation, which influences the human-automation interactions with the available colour-design tools (academic and commercial). As colour design and selection are known to be one of the most critical steps of any art or design journey, the currently available tools use one over the other approaches, from the automation-levels spectrum, when it comes to contextual search for colour palettes, colour-extracting, and colour compatibility. On the one hand, fully automated approaches could exclude the designers’ intervention; on the other hand, fully manual approaches could be affected by human errors and weaknesses. Both approaches tend to have problems when used in colour design tools, such as restricting the designers’ freedom, overwhelming designers with information-overload and option-widget clutter that exist in the interfaces of such tools, or limiting designers by the functionalities offered by the tool based on its purpose, causing it to partially support certain parts of the designers’ colour selection process rather than the whole process. The thesis focuses on investigating the possible solutions for balancing the automated and manual methods for generating colour palettes and supporting the designers’ non-standardised colour-selection processes while tailoring the solution to intellectually stimulate and engage designers who work in different design fields, in comparison with the Adobe Explore Page–which is one of the most well-known and established colour design tools in today’s market and one of the applications that offers a contextual search feature. To fulfil the purpose of this research, a web-based application was prototyped (named Paletto), which consists of the requirements for enabling the rapid generation and exploration of colour palette variations, supporting end-users to contextually search for palettes, and allowing users to apply constraints (via a preference selection list) for a holistic palette adjustment. Afterwards, the proposed application was evaluated with 20 individuals from the target audience, using both qualitative and quantitative approaches to prove the concept according to participants’ acceptance, estimate Paletto’s effectiveness on their workflow and design process, examine their engagement and experience when completing the exploratory tasks, and gather additional insights about the design or the conceptual design and implementation of the application. Paletto generally received positive responses towards (1) the accuracy and relevance of its search results, (2) the selection feature and its adaptability and flexibility for human interventions, and (3) the system’s feedback in terms of information accessibility (e.g., search word and number of pages in the pagination). However, the palette generation feature had partially negative responses where participants showed annoyance, confusion, and thought it was complicated. At the same time, several participants appreciated the diversity of the generated palettes and the conceptual design of Paletto in general. Paletto found to effectively facilitate the colour-selection process and designers’ workloads in several areas, such as: fulfilling the end-user goals of producing quality palettes to be used in design projects; resources-efficiency (e.g., money-preserving, effort facilitation, and time-saving) for inspirational image gathering; automatic colour extraction and palette generation; providing freedom and support of decision making to explore colour combinations and variations via the iterative preferences selection; supporting colour-pattern identification in the selections; providing variation and relevant results when searching inspirational image gathering with accurate colour extractions that represent the searched images. Moreover, Paletto proved to offer greater user engagement and a better user experience in comparison with Adobe’s Explore Page. This was due to the felt involvement and the continuous interactivity offered by Paletto’s search and preference-selection features that allowed iterative palette generation and modification. In conclusion, the evaluations indicated some pain-points and gaps in the current design that were discussed in this thesis, and are accordingly recommended to be investigated in future work.

Through a history of more than 50 years, the results of mathematical models have shown that controller workload is being driven by the complexity involved in the airspace environment. Part of this complexity is prompted by the dynamical behavior of traffic patterns. From the results of models describing controller’s workload, it is observed that predictability decreases the complexity. Therefore, the general idea behind this topic is to analyze how a specific notion of predictability influences the controller’s workload. This specific notion in this research is a type of automation that aircraft benefit from. In a more specific sense, the goal of this research was to analyze how the controllers handle the air traffic in different complex situations when exposed to different automation levels. The following dilemmas are focused through this work: - Information visualization of controllers’ interaction with radar screen - Quantification of dynamics of air traffic patterns - Modeling and quantification of controllers’ workload First, in order to have a grasp of the controllers’ interaction with the air traffic patterns, the controllers’ activities on the radar screen have been visualized in chapter 2. The visualization results for different automated conditions have been analyzed. Based on such analysis the criteria for problem space has been addressed and the main research question is identified. Next in chapter 3, the airspace complexity caused by air traffic flow has been studied and a set of known complexity factors are quantified using a novel calculation approach. With a logistics perspective toward airspace complexity, to calculate each complexity factor, a mathematical formulation has been used and the effects of each corresponding factor on controllers’ workload are addressed. Then in chapter 4, a novel approach toward modeling controller’s workload is presented. After implementing the model on 18 different scenarios, a model for controller’s workload has been developed in which around 60 percent of the en-route air traffic complexity values and around 80 percent of terminal air traffic complexity values could be well-matched with the workload values. From statistical point of view, the results are very much acceptable for experiments in which human factors are involved. Cognitive load has not been considered in the workload model which is the focus of a future work. Later on in chapter 5, the results for each complexity factor as well as workload models are analyzed and discussed for each sector separately. Based on the airspace complexity results, areas where traffic situation had become complex were identified and the controller’s response to different situations are discussed. For each complexity factor as well as workload, the results for three different scenarios featuring different automation levels for two en-route and terminal sectors are compared. At last in chapter 6, the main ideas are discussed, thesis conclusions are presented and possible future work is suggested.