The Federal Aviation Administration (FAA) is projecting tremendous growth in air traffic demand and complexity with the integration of unmanned aerial systems (UAS) in the National Airspace System (NAS). To meet this demand, the FAA is working to a 2025 goal of a system-wide autonomous optimized air traffic management (ATM) system that is safe, resilient, and agile enough to adapt to the ever changing business models and operator demands in the NAS. Modeling ATM to understand the change impact of automation and human computer interfaces on safety and performance is critical to achieving the 2025 goal. Necessary to any such modeling effort are abstractions of complex automated ground systems; wherein the abstractions represent the functional specifications of those systems. To that end, this paper presents an abstract functional model of the Terminal Sequencing and Spacing (TSS) system that provides automation to support Terminal Radar Approach Control (TRACON) in sequencing arrival traffic at airports. The model employs simplified flight dynamics under visual meteorological conditions (VMC) to create a schedule for arriving flights that is free of conflict at meter fixes, merge points, and runway thresholds. The utility of the TSS model is evaluated in a study of airspace around the LaGuardia airport (LGA) to understand the change impact of Departure-Sensitive Arrival Spacing (DSAS) automation for TRACON controllers. DSAS improves departure throughput under high traffic conditions at LGA, but different deployment configurations have different workload impact on controllers. The study suggests that the TSS model scales to a level sufficient to recreate scenarios from the published human in the loop simulations (HITL) of DSAS at the LGA. It also suggests that models of the complex automation at a high-level of abstraction enables the tractability of more general system-wide model analysis.
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