1998 · 165 citations · 6 references
EngineeringComputational ComplexityAir Transport SystemComplexityOperations ResearchAirspace ComplexityIntelligent Traffic ManagementDynamic DensityData ScienceTraffic PredictionSystems EngineeringAviation ManagementCombinatorial OptimizationTraffic SimulationTransportation EngineeringAir Traffic ControlPredictive AnalyticsForecastingAir Traffic ManagementPredicted Dynamic DensityAerospace EngineeringBusinessTraffic Management
Growing air traffic and demand for user‑preferred routes will increase NAS workload, prompting the need to understand how airspace configurations affect controller workload—a relationship termed Airspace Complexity for which dynamic density, dependent on aircraft number and geometry, is a useful metric. The study aims to evaluate the predictive accuracy of dynamic density into the future using the trajectory generation feature of CTAS. Dynamic density is projected over the planning horizon by applying CTAS trajectory prediction to Dallas/Fort Worth traffic data, enabling computation of actual and forecasted density. Results demonstrate that dynamic density can be predicted up to 20 minutes ahead with acceptable error margins.
Predicted growth in air traffic and the desire for more user preferred routes in the National System (NAS) will impose additional demand on air traffic control and management systems. This demand can be met by alternate airspace configurations, modified traffic patterns, and staff reassignment. There is a need to understand the effect of changing airspace configurations and traffic patterns on the workload of air traffic controllers. This complex relation is referred to as Airspace Complexity. Research on dynamic density indicates that it is a good measure of airspace complexity. Dynamic density is a function of the number of aircraft and their changing geometries in a given airspace. In order to use dynamic density as a planning tool, it is necessary to project its behavior over the planning horizon. The objective of this work is to study how well dynamic density can be predicted into the future using the trajectory generation feature of the Center-TRACON Automation System (CTAS). This paper describes the application of trajectory prediction to computation of actual and predicted dynamic density using traffic data from Dallas/Fort Worth airspace. Results show that dynamic density can be predicted up to 20 minutes in advance and errors in predictions can be further
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DYNAMIC DENSITY: AN AIR TRAFFIC MANAGEMENT METRIC
Irene V. Laudeman, Stephen Shelden, R. Branstrom et al. · NASA Technical Reports Server (NASA) · 1998 · 194 citations