A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decisionsupport tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automation's ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

Air traffic management in the New York (NY) metropolitan area presents significant challenges including excess demand, chronic delays, and inefficient routes. At NASA, a new research effort has been initiated to explore Next Generation Air Transportation System (NextGen) Trajectory Based Operations (TBO) solutions to address lingering problems in the NY metroplex. One of the larger problems in NY is departure delays at LaGuardia airport (LGA). Constant traffic demand and physical limitations in the number of taxiways and runways cause LGA to often end up with excessive departure queues that can persist throughout the day. At the Airspace Operations Laboratory (AOL) located at NASA Ames Research Center, a TBO solution for "Departure-Sensitive Arrival Spacing" (DSAS) was developed. DSAS allows for maximum departure throughput without adversely impacting the arrival traffic during the peak demand period. The concept uses Terminal Sequencing and Spacing (TSS) operations to manage the actual runway threshold times for arrivals. An interface enhancement to the traffic manager's timeline was also added, providing the ability to manually adjust inter-arrival spacing to build precise gaps for two or even three departures between arrivals. With this set of capabilities, inter-arrival spacing could be controlled for optimal departure throughput. The concept was prototyped in a human-in-the-loop (HITL) simulation environment to determine operational requirements such as coordination procedures, timing and magnitude of TSS schedule adjustments, and display features for the tower, Terminal Radar Approach Control (TRACON), and Traffic Management Unit (TMU). A HITL simulation was conducted in August, 2014, to evaluate the concept in terms of feasibility, impact on controller workload, and potential benefits. Three conditions were compared: (1) a baseline condition using new RNAV/RNP procedures (no TSS); (2) the new procedures + TSS; and (3) new procedures + TSS + DSAS schedule adjustments. Results showed that with a maximum arrival demand (40-41 arrivals per hour), departure throughput could be increased from 38 / hour (baseline condition), to 44 / hour (TSS condition), to 47 / hour (TSS + DSAS). The results suggest that DSAS operations have the potential to increase departure throughput at LGA by up to 9 a/c per hour with little or no impact on arrivals during peak traffic demand period.

In air traffic control, task demand and workload have important implications for the safety and efficiency of air traffic, and remain dominant considerations. Within air traffic control, task demand is dynamic. However, research on demand transitions and subsequent controller perception and performance is limited. This research uses an air traffic control simulation to investigate the effect of task demand transitions, and the direction of those transitions, on workload and fatigue and one efficiency performance measure. Results indicate that a change in task demand appears to affect both workload and fatigue ratings, although not necessarily performance. In addition, participants' workload and fatigue ratings in equivalent task demand periods appear to change depending on the demand period preceding the time of the current ratings. Further research is needed to enhance understanding of demand transition and workload history effects on operator experience and performance, in both air traffic control and other safety-critical domains.

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