The growth in European air traffic has led to delays, inefficiencies, and attendant costs. This growth is predicted to increase. The workload of the air traffic controller is the limiting factor in airspace capacity and will remain so despite the introduction of new technology and procedures. A framework is provided for modeling airspace capacity by considering the factors that affect controller workload and then using a model of controller workload, aided by the appropriate analytical techniques, to estimate airspace capacity. The context of the current European air traffic control problems is given, airspace capacity is examined, and a scheme is outlined for estimating airspace capacity using simulation modeling. The Reorganized ATC Mathematical Simulator (RAMS) controller workload model was chosen for use in the research. The output of this simulation model is used for estimating airspace capacity, and simulation experiments undertaken using RAMS are described. The analysis of how these factors affect the air traffic controller, using a generalized linear modeling framework, is undertaken. This accounts for the spatial correlation structure of the error structure. Two alternative estimation methods are used: maximum likelihood and estimated generalized least squares. The results from the analysis, together with a workload-based capacity measure, are used to provide airspace capacity estimates.