From 2010 to 2030, the number of instrument flight rules aircraft operations handled by Federal Aviation Administration en route traffic centers is predicted to increase from approximately 39 million flights to 64 million flights. The projected growth in air transportation demand is likely to result in traffic levels that exceed the abilities of the unaided air traffic controller in managing, separating, and providing services to aircraft. Consequently, the Federal Aviation Administration, and other air navigation service providers around the world, are making several efforts to improve the capacity and throughput of existing airspaces. Ultimately, the stated goal of the Federal Aviation Administration is to triple the available capacity of the National Airspace System by 2025. In an effort to satisfy air traffic demand through the increase of airspace capacity, air navigation service providers are considering the inclusion of advisory conflict-detection and resolution systems. In a human-in-the-loop framework, advisory conflict-detection and resolution decision-support tools identify potential conflicts and propose resolution commands for the air traffic controller to verify and issue to aircraft. A number of researchers and air navigation service providers hypothesize that the inclusion of combined conflict-detection and resolution tools into air traffic control systems will reduce or transform controller workload and enable the required increases in airspace capacity. In an effort to understand the potential workload implications of introducing advisory conflict-detection and resolution tools, this thesis provides a detailed study of the conflict event process and the implementation of conflict-detection and resolution algorithms. Specifically, the research presented here examines a metric of controller taskload: how many resolution commands an air traffic controller issues under the guidance of a conflict-detection and resolution decision-support tool. The goal of the research is to understand how the formulation, capabilities, and implementation of conflict-detection and resolution tools affect the controller taskload (system demands) associated with the conflict-resolution process, and implicitly the controller workload (physical and psychological demands). Furthermore this thesis seeks to establish best practices for the design of future conflict-detection and resolution systems. To generalize conclusions on the conflict-resolution taskload and best design practices of conflict-detection and resolution systems, this thesis focuses on abstracting and parameterizing the behaviors and capabilities of the advisory tools. Ideally, this abstraction of advisory decision-support tools serves as an alternative to exhaustively designing tools, implementing them in high-fidelity simulations, and analyzing their conflict-resolution taskload. Such an approach of simulating specific conflict-detection and resolution systems limits the type of conclusions that can be drawn concerning the design of more generic algorithms. In the process of understanding conflict-detection and resolution systems, evidence in the thesis reveals that the most effective approach to reducing conflict-resolution taskload is to improve conflict-detection systems. Furthermore, studies in the this thesis indicate that there is significant flexibility in the design of conflict-resolution algorithms.

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.