Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-23T22:51:17.065Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling and Simulating Airport Surface Operations with Gate Conflicts

Published online by Cambridge University Press:  05 November 2018

S. Zelinski Open the ORCID record for S. Zelinski [Opens in a new window]  and
R. Windhorst
S. Zelinski*
Affiliation:
Aviation Systems DivisionNASA Ames Research CenterMoffett FieldCalifornia, USA
R. Windhorst*
Affiliation:
Aviation Systems DivisionNASA Ames Research CenterMoffett FieldCalifornia, USA
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The Surface Operations Simulator and Scheduler (SOSS) is a fast-time simulation of the airport surface used to rapidly develop and test new surface scheduling concepts. Gate conflicts present a challenge for surface scheduling. A late departure pushback or early arrival sharing the same gate can cause a gate conflict, which if left unmanaged, can lead to surface gridlock. Surface scheduling concepts that meter departures at their gates can increase the likelihood of gate conflicts. In real operations, hardstand areas are used to temporality park aircraft out of the way to avoid gate conflicts. New SOSS models and functionality for hardstand operations were developed to simulate gate conflict management approaches using hardstands to temporarily park either the arrival or departure out of the way of the other. Four gate conflict management approaches were simulated with surface scheduling and their effects on surface operations were compared. The four gate conflict management approaches each allowed a unique subset of resolution actions including early departure pushback, sending the departure to the hardstand, and sending the arrival to the hardstand. The gate conflict management approaches allowing arrivals to be sent to the hardstand were found to be most successful in resolving the gate conflicts and maintaining scheduler performance measured by takeoff time predictability.

Keywords

airport modelling and simulationairport surface operationssurface scheduling
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1259 , January 2019 , pp. 1 - 19
Copyright
© Royal Aeronautical Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Branch Chief, Aerospace High Density Operations Branch, MS 210-6, AIAA Senior Member.

†

Computer Scientist, Aerospace High Density Operations Branch, MS 210-6, AIAA Senior Member.

References

1. Windhorst, R.D., Montoya, J.V., Zhu, Z., Gridnev, S., Griffin, K.J., Saraf, A. and Stroiney, S. Validation of Simulations of Airport Surface Traffic with the Surface Operations Simulator and Scheduler, AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 12–14 August 2013, Los Angeles, California.Google Scholar
2. Wood, Z., Kistler, M., Rathinam, S. and Jung, Y. A Simulator for Modeling Aircraft Surface Operations at Airports, AIAA Modeling and Simulation Technologies Conference, 10–13 August 2009, Chicago, Illinois.Google Scholar
3. Chung, W., Chadchad, G. and Hochstetler, R. An Integrated Gate Turnaround Management Concept Leveraging Bif Data/Analytics for NAS Performance Improvements, AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 13–17 June 2016, Washington, DC.Google Scholar
4. Windhorst, R. Towards a Fast-time Simulation Analysis of Benefits of the Spot and Runway Departure Advisor, AIAA Guidance, Navigation, and Control Conference, 13–16 August 2012, Minneapolis, Minnesota.Google Scholar
5. Griffin, K.J., Saraf, A., Yu, P., Stoiney, S.R., Levy, B.S., Solveling, G., Clarke, J. and Windhorst, R.D. Benefits Assessment of a Surface Traffic Management Concept at a Capacity-Constrained Airport, AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 17–19 September 2012, Indianapolis, Indiana.Google Scholar
6. Montoya, J., Windhorst, R., Zhu, Z., Gridnev, S., Griffin, K.J., Saraf, A. and Stroiney, S. Analysis of Airport Surface Schedulers Using Fast-time Simulation, AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 12–14 August 2013, Los Angeles, California.Google Scholar
7. Saraf, A., Griffin, K., Stroiney, K., Felipe, V. and Windhorst, R. Recommendations for NextGen Airport Surface Traffic Scheduling Algorithms: A Fast-time Simulation-based Perspective, AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 12–14 August 2013, Los Angeles, California.Google Scholar
8. Zelinski, S. and Windhorst, R. Departure Queue Prediction for Strategic and Tactical Surface Scheduler Integration, 35th Digital Aviation Systems Conference (DASC), 25–29 September 2016, Sacramento, California.Google Scholar
9. Zelinski, S. and Windhorst, R. Assessing Tactical Scheduling Options for Time-Based Surface Metering, 36th Digital Aviation Systems Conference (DASC), 17–21 September 2017, St. Petersburg, Florida.Google Scholar
10. Zhu, Z., Okuniek, N., Gerdes, I., Schier, S., Lee, H. and Jung, Y. Performance Evaluation of the Approaches and Algorithms Using Hamburg Airport Operations, 35th Digital Aviation Systems Conference (DASC), 25–29 September 2016, Sacramento, California.Google Scholar
11. Eun, Y., Jeon, D., Lee, H., Zhu, Z., Jung, Y.C., Jeong, M., Kim, H., Oh, E., Hong, S. and Lee, J. Operational Characteristics Identification and Simulation Model Validation for Incheon International Airport, AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 13–17 June 2016, Washington, DC.Google Scholar
12. Zelinski, S. and Coppenbarger, R. A Concept for Integrated Arrival/Departure/Surface (IADS) Traffic Management for the Metroplex, Airspace Technology Demonstration 2 (ATD-2) ConOps Synopsis, NASA Ames Research Center, August 2015 (unpublished).Google Scholar