Under ideal weather conditions, each en route sector in an air traffic management (ATM) system has a certain maximum operational traffic density that its controller team can safely handle with nominal traffic flow. We call this the design capacity of the sector. Bad weather and altered flow often reduce sector capacity by increasing controller workload. We refer to sector capacity that is reduced by such conditions as dynamic capacity. When operational conditions cause workload to exceed the capability of a sector's controllers, air traffic managers can respond either by reducing demand or by increasing design capacity. Reducing demand can increase aircraft operating costs and impose delays. Increasing design capacity is usually accomplished by assigning more control resources to the airspace. This increases the cost of ATM. To ensure full utilization of the dynamic capacity and efficient use of the workforce, it is important to accurately characterize the capacity of each sector. Airspace designers often estimate sector capacity using microscopic workload simulations that model each task imposed by each aircraft. However, the complexities of those detailed models limit their real-time operational use, particularly in situations in which sector volumes or flow directions must adapt to changing conditions. To represent design capacity operationally in the United States, traffic flow managers define an acceptable peak traffic count for each sector based on practical experience. These subjective thresholds-while usable in decision-making-do not always reflect the complexity and geometry of the sectors, nor the direction of the traffic flow. We have developed a general macroscopic workload model to quantify the workload impact of traffic density, sector geometry, flow direction, and air-to-air conflict rates. This model provides an objective basis for estimating design capacity. Unlike simulation models, this analytical approach easily extrapolates to new conditions and allows parameter validation by fitting to observed sector traffic counts. The model quantifies coordination and conflict workload as well as observed relationships between sector volume and controller efficiency. The model can support real-time prediction of changes in design capacity when traffic is diverted from nominal routes. It can be used to estimate residual airspace capacity when weather partially blocks a sector. Its ability to identify dominant manual workload factors can also help define the benefits and effectiveness of alternative concepts for automating labor-intensive tasks.