The Surface Operations Data Analysis and Adaptation tool: Innovations and applications

Brinton, Chris; Lindsey, J. B.; Graham, Michael

doi:10.1109/dasc.2010.5655520

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Though not apparent at this scale, the ASR-9 data drops out near the runway threshold, and ASDE-X provides the track data as the aircraft lands on runway 17C and taxis to Terminal A via the perimeter taxiway. 10,11 where they are combined with data from other sources, refined and managed. During the import process, SODAA computes a wide array of derived elements, filling a complex relational database with more than 300 raw and derived data elements.…”

Section: A Data Sources and Analysis Toolsmentioning

confidence: 99%

Analysis of DFW Perimeter Taxiway Operations

Engelland

Ruszkowski

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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This study examines operations of the perimeter taxiway system at Dallas/Fort Worth International Airport (DFW) to characterize and understand the impact of the perimeter taxiway system and to provide operational decision makers with guidance on use of this new airport resource. DFW's perimeter taxiway entered service in December 2008 and is representative of perimeter or end-around taxiways currently in use at several other airports worldwide. This perimeter taxiway analysis is a collaborative effort between NASA and various DFW stakeholders including the FAA, air carriers and the airport operator. The initial investigation has focused on quantifying perimeter taxiway usage and assessing effects on taxi times at both the local and global levels. Local-level results show taxi times via the perimeter taxiway to be about forty-five seconds longer on average, but with significantly less variability. Global-level results show average perimeter taxiway times to be a little more than one minute longer with variability that is comparable to that for other taxi paths.

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Section: A Data Sources and Analysis Toolsmentioning

confidence: 99%

Analysis of DFW Perimeter Taxiway Operations

Engelland

Ruszkowski

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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“…Simulation scenarios were generated based on data collected at DFW for the Surface Operations Data Analysis and Adaptation (SODAA) Tool (Brinton, Lindsey, & Graham, 2010). These SODAA data aggregate actual flight information such as callsign, aircraft type, scheduled departure/arrival times, departure/arrival airports, assigned gate, etc., and links them to positional data collected for each flight.…”

Section: Softwarementioning

confidence: 99%

“…The simulation software automatically controlled the movement of aircraft outside the participants' area of control based on pre-defined scripts and controlled the movements of aircraft inside the participants' area of control based on participant-issued instructions. Simulation scenarios were generated based on data collected at DFW for the Surface Operations Data Analysis and Adaptation (SODAA) Tool (Brinton, Lindsey, & Graham, 2010). These SODAA data aggregate actual flight information such as callsign, aircraft type, scheduled departure/arrival times, departure/arrival airports, assigned gate, etc., and links them to positional data collected for each flight.…”

Section: Softwarementioning

confidence: 99%

A Comparison of Visualization and Command-Based Decision Aiding in a Simulated Aircraft Departure Sequencing Task

Riddle¹,

Kirlik²,

Talleur³

et al. 2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Three versions of a prototype ground controller interface were tested for their effect on aircraft departure sequencing in a simulation of Dallas/Forth-Worth International Airport. Two versions featured automated decision aids and a third served as a baseline with no decision aid. The two decision aids performed fundamentally different functions, with a Temporal Constraint Visualization (TCV) providing a visualization of spatiotemporal constraints on the departure sequence and a Timeline display providing release sequences derived by an optimization algorithm. Results indicate that participants in the TCV condition had more efficient departure sequences than the Baseline and Timeline conditions. No significant differences were found between conditions for timeliness of departures or handling of arrival aircraft. These results indicate that, through the use of appropriate decision aids, task performance in complex dynamic environments can be improved with humans retaining full decision making control. Additional research is warranted to investigate situation awareness and failure mode performance using these decision aid types.

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“…Steering a commercial jetliner through a turn is a complex perceptualmotor operation, one for which ACT-R did not contain adequate motor capabilities. Based on data from the Surface Operations Data Analysis and Adaptation (SODAA) tool (Brinton, Lindsey, & Graham, 2010), we had access to the turn trajectories of multiple commercial jetliners, and were able to fit those data using a series of motor adjustments based on the speed of the plane and the approximate distance to the hypothetical point where the turn is expected to be completed. The expected heading of the plane can then be calculated as a function of the tangent line at different points on this curve and the model then adjusts the yoke accordingly to match the new heading value.…”

Section: Steeringmentioning

confidence: 99%

An ACT-R Model of Commercial Jetliner Taxiing

Zemla

Ustun

Byrne

et al. 2011

Proceedings of the Human Factors and Ergonomics Society Annual

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The Next Generation Air Transportation System (NextGen) seeks to reduce gridlock at airports by, among other things, creating a more efficient surface taxi management system. Addressing this situation creates a difficult evaluation problem; how can new scheduling methods be tested? Present methods generally involve either expensive human-in-the-loop experiments or computer simulations that do not adequately represent the human component of system performance. We have developed an ACT-R model of commercial jetliner taxiing with the ultimate goal of aiding in both of these efforts. The X-Plane commercial flight simulation package provides an environment in which the model can act. That environment is populated with aircraft driven by recordings taken of real aircraft at Dallas-Fort Worth airport, which contain the actual positions of all aircraft on the taxi surface for a given time slice. This also provides us with a rich source of data for model validation, as the model can "replace" one actual aircraft, allowing comparisons between model-generated and pilot-generated trajectories.

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The Surface Operations Data Analysis and Adaptation tool: Innovations and applications

Cited by 3 publications

References 7 publications

Analysis of DFW Perimeter Taxiway Operations

Analysis of DFW Perimeter Taxiway Operations

A Comparison of Visualization and Command-Based Decision Aiding in a Simulated Aircraft Departure Sequencing Task

An ACT-R Model of Commercial Jetliner Taxiing

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