ABSTRACT

Air traffic complexity is thought to include those aspects of the static airspace and dynamic traffic pattern that contribute to an air traffic controller’s workload, and is an important concept in evaluating ATM productivity, benchmarking cost effectiveness, and assessing the impact of new tools. Traditionally, standardized complexity indicators have relied on geometric aspects of the traffic flow, and/or observable controller behavior. In many cases, the best indicator has been the mere count of aircraft in an area of airspace. It is increasingly recognized, however, that such types of indicators do not always capture the richness of what makes some airspace more challenging (and ultimately capacity-limited). As part of its COmplexity and CApacity analysis (COCA) project, EUROCONTROL has therefore undertaken to construct a model of traffic complexity that better incorporates the cognitive aspects of air traffic control.

COCA is in the process of developing a model of cognitive complexity that promises to be unique in at least two respects. First, it is being built on an Information Processing model and sets out to elicit and refine complexity factors that relate directly to the perceptual and attentional aspects underlying cognitive complexity. Second, it is investigating the use of non-linear regression techniques to refine a generic complexity index for use across various types of airspace. Together it is hoped that this approach can strengthen current models of cognitive complexity, and benefit such modeling in air traffic control, as well as other complex human-machine systems.