The National Aeronautics and Space Administration (NASA) is developing automation for managing flight traffic on the airport surface to reduce taxi times and increase traffic throughput, without compromising safety. The scheduler is the part of the automation that calculates the advisories that assist the controller with clearing, holding, and sequencing flights. The Surface Operations Simulator and Scheduler (SOSS) is a fast-time airport surface operations simulator that connects to schedulers. SOSS is used to develop and test schedulers to determine if they can produce benefits. To show that schedulers developed with SOSS are credible, a validation of SOSS was performed to demonstrate that it is an accurate model of real operations. Surveillance and Federal Aviation Administration (FAA) operational performance data recorded from real operations at Charlotte Douglas International Airport were used to build a SOSS traffic scenario. The traffic scenario was run through SOSS to create simulated flight tracks. The flight tracks were analyzed to generate simulated taxi time and runway throughput metrics. Actual taxi time and runway throughput metrics were generated from the surveillance and FAA operational performance data. The simulated and actual metrics were compared. After the initial simulation, the average difference between simulated and actual taxi times on a flight by flight basis was not zero. A model tuning was performed by running the SOSS simulation multiple times while varying SOSS parameters to drive the average difference between the simulated and actual taxi times to zero. The SOSS parameters used were the pushback duration times and the taxi and ramp target speeds. Results show that the average difference between the simulated and actual taxi times was driven to zero. In addition, the standard deviations of the simulated taxi times and the actual taxi times were almost the same. However, the standard deviation of the flight by flight taxi time differences was large. This is because SOSS cannot simulate on an individual flight basis the exact actions taken by each flight in reality, which is an issue for all simulators. Despite this issue, SOSS was found to be a statistically accurate simulation of real airport operations, and schedulers developed and tested using SOSS have potential for producing benefits in real airport traffic management automation systems. NomenclatureASQP = Airline Service Quality Performance ASDE-X = Airport Surface Detection Equipment, Model X 1 Aerospace Engineer, Aerospace High Density Operations Branch, Mail Stop 210-15, Senior member. 2 ATCT = Air Traffic Control Tower CAI = Common Algorithm Interface CLT = airport code for Charlotte Douglas International Airport FAA = Federal Aviation Administration NASA = National Aeronautics and Space Administration SOSS = Surface Operations Simulator and Scheduler I. IntroductionASA is researching and developing automation that provides advisories to ramp, ground, and local controllers, who manage air traffic on the airport surface. 1 ...

Using a fast-time simulation tool Surface Operations Simulator and Scheduler, three gate pushback metering concepts are tested on a mock-up at Charlotte Douglas International Airport. The three concepts include (1) a mixed-integer-linear program to minimize aircraft delay, (2) a heuristic scheduler which uses aggregate aircraft counts to meter traffic, and (3) a first-come-first-served method that releases aircraft as soon as they are ready. A realistic scenario is created using surveillance data. By perturbing departure pushback times and arrival on-times, approximately 500 new scenarios are created from the original scenario. Additionally, pushback duration uncertainty and the inclusion of a miles-in-trail restriction are tested as independent variables. Results show that the calibration of certain model parameters on the mixed-integer-linear-program and heuristic scheduler strongly affect aircraft taxi times and delays. Also, the mixed-integer-linear-program is more robust to pushback duration uncertainties when there is a miles-in-trail restriction present.

Inefficient surface traffic management may lead to congested taxiways, long departure queues, and excess delay in the air transportation system. To address this problem, NASA researchers have developed optimization algorithms and a concept of operations for an airport surface traffic management tool called the Spot and Runway Departure Advisor (SARDA). Past SARDA research efforts have been focused on the Dallas/Fort Worth International airport. This paper describes the development of SARDA-like schedulers for managing the traffic at an operationally dissimilar airport-Charlotte Douglas International airport, and presents the results of a fast-time simulation-based benefits assessment. Fasttime simulations were conducted to test the benefits of optimized scheduling over a baseline model of current-day operations. In the fast-time simulations, it was observed that optimization schedulers reduced movement area delays by up to 3.1 minutes per departure on average, as compared to the baseline simulation. The movement area delay savings translated to shorter movement area taxi-out times and an average reduction in fuel burn and emissions of approximately 24% per departure. The overall trend observed in the total delay (gate delay + ramp delay + movement area delay) comparison indicated the optimization schedulers were not able to reduce total delay, and runway throughput comparisons suggested the optimization schedulers had little to no effect on throughput.2 NASA researchers are developing surface optimization algorithms and a concept of operations for an airport surface management tool called the Spot and Runway Departure Advisor (SARDA). 2 SARDA integrates two decoupled schedulers-the Spot Release Planner (SRP) and Runway Scheduler (RS). [2][3][4][5] Until recently, NASA's SARDA algorithm designs and experiments have been focused solely on Dallas/Fort Worth International airport (DFW). Some aspects of the SARDA schedulers are designed to take advantage of certain optimization opportunities or degrees of freedom that are specific to DFW airport. For example, runway scheduling at DFW is made easier by large areas of pavement or "pads" near the runway threshold, which are used to build up to three separate departure queues. Thus, departure sequencing decisions can be delayed until the very end of taxi-out.Research is currently underway to assess whether or not SARDA can be effective at other capacity-constrained airports where dissimilar airport geometries and operational characteristics exist. The benefits assessment described in this paper is focused on Charlotte/Douglas International airport (CLT). CLT has the potential to benefit from improved surface management since it experiences significant departure delays. The runway geometry at CLT is different than the geometry at DFW, and CLT features various types of dependent runway operations (e.g., mixeduse). Lastly, CLT is dominated by a single carrier (US Airways), which runs a single ramp tower for the entire passenger terminal.For the CLT assessment, fast-time simu...

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The application of NASA's Airspace Concept Evaluation System (ACES) Terminal Model Enhancement (TME), a fast-time airport surface simulation, to evaluate surface optimization techniques is discussed in this paper. This work supports a research effort to develop a better understanding of surface operation constraints and develop surface optimization techniques to increase capacity and reduce delay. There were five strategic optimization components integrated with ACES-TME: Taxiway Planner, Runway Planner, Gate Assigner, Runway Assigner, and Configuration Planner. The optimization components were evaluated using a Detroit Metropolitan Wayne County (DTW) Airport surface model and a 2006 traffic demand set. The collective impact of the Taxiway Planner and Runway Assigner provided the most benefit in terms of reducing taxi time and taxi delay.