This study's goal was to model airspace
Dynamic Density and complexity (and hence
controller workload) using traffic characteristic
metrics. The focus was on metrics that could
eventually enable Traffic Flow Management (TFM)
personnel to strategically prevent overloads using
triggers other than predicted sector traffic count.
Potential metrics from past studies were
assessed in terms of how well they could be predicted
at time horizons required for TFM decision support
(up to 120 minutes), and their face validity. Also,
proportional odds logistic regression determined the
metrics' usefulness for predicting subjective
complexity ratings collected in an FAA-NASA study.
Based on these analyses, a subset of 12 metrics
was chosen (from the original 41). Further multiple
regression analyses were conducted with this reduced
model, to determine which metrics provided unique
contributions to the prediction of subjective
complexity, and to see the extent to which the same
complexity factors related to subjective workload in
different airspaces.
Structured interviews with a sample of eight
Traffic Management Coordinators were used to
cross-check the quantitative findings.
Specific aircraft proximity, density, and
airspace structure metrics were found potentially
useful for real-time TFM decision support. Many of
the useful metrics were normalized and smoothed
measures from an algorithm developed by
Wyndemere, Inc. Also, it was found that different
metrics related to subjective complexity in different
centers, but the differences were small enough that a
generalized set of complexity metrics might be
applicable to multiple airspaces, at least in the near
term. Future work could determine the viability of
airspace-adapted complexity algorithms.
The fact that multiple types of metrics are useful
suggests that a multidimensional visual
representation of predicted workload might be useful
in TFM, as opposed to combining all relevant factors
into a single metric.
air traffic control, complexity,
air traffic complexity, controller workload, dynamic density, traffic flow management (TFM), TFM decision support
