Predictability in Airport Surface Operation Management

The performance of airport surface operations has usually been assessed with respect to delay, capacity and efficiency. Although predictability as a performance measure is recognized by stakeholders as an important goal, predictability metrics have not been defined for airport surface operations. This paper aims to fill that gap by using data from NASA’s Spot and Runway Departure Advisor (SARDA) human-in-the-loop simulations in 2012 to study airport operations predictability. Using the simulation data, we measure and compare predictability on the airfield with and without SARDA from three perspectives: controllers’ perspective, flight operator’s perspective and traffic management perspective. The controller survey results indicate the perception that SARDA reduces controller’s workload surges and has the potential to better handle off-nominal situations. By studying taxi-out time in both baseline and advisory runs, it is found that SARDA reduces variability in total taxi-out time and eliminates uncertainty in taxi-out time sooner into the taxi-out process. Moreover, SARDA enables more accurate predictions of wheels-off time through use of a linear regression model. There is no evidence indicating that SARDA causes more deviation from First-Scheduled-First-Served as compared to the non-SARDA case. Instead, SARDA improves First-In-First-Out performance in the queue area.