Using Models to Explore Air Traffic Controller Workload

Abstract

When a new system, concept, or tool is proposed in the aviation domain, one concern is the impact that it will have on operator workload. As an experience, workload is difficult to measure in a way that will allow comparison of proposed systems with those already in existence. Chatterji and Sridhar (2001) suggested a method by which airspace parameters can be translated into workload ratings, using a neural network. This approach was employed, and modified to accept input from a non-real time airspace simulation model. The following sections describe the method and pilot test comparing a future airspace concept with a current day baseline in terms of workload levels. The results presented describe a preliminary analysis from using these models with the data from two non-real time model runs.

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