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.