DYNAMIC DENSITY AND COMPLEXITY METRICS FOR REALTIME TRAFFIC FLOW MANAGEMENT

TLDR

The study aims to model airspace dynamic density and complexity using traffic metrics to enable TFM personnel to prevent overloads beyond sector traffic counts. Researchers evaluated past metrics for predictability up to 120 min, applied proportional‑odds logistic regression and multiple regression to assess their predictive value for subjective complexity and workload across airspaces. The analyses identified a subset of 12 metrics from an original set of 41.

Abstract

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.