Scipediacontent: Scipediacontent moved page Draft Content 180907080 to Lee et al 2015f

2021-01-28T22:32:11Z

Scipediacontent moved page Draft Content 180907080 to Lee et al 2015f

Scipediacontent: Created page with " == Abstract == urate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumption..."

2021-01-28T22:32:06Z

Created page with " == Abstract == urate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumption..."

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== Abstract ==

urate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.2514/6.2015-2272 http://dx.doi.org/10.2514/6.2015-2272]

* [http://www.aviationsystemsdivision.arc.nasa.gov/publications/2015/AIAA-2015-2272.pdf http://www.aviationsystemsdivision.arc.nasa.gov/publications/2015/AIAA-2015-2272.pdf]

* [http://arc.aiaa.org/doi/pdf/10.2514/6.2015-2272 http://arc.aiaa.org/doi/pdf/10.2514/6.2015-2272],
: [http://dx.doi.org/10.2514/6.2015-2272 http://dx.doi.org/10.2514/6.2015-2272]

* [https://www.aviationsystemsdivision.arc.nasa.gov/publications/2015/AIAA-2015-2272.pdf https://www.aviationsystemsdivision.arc.nasa.gov/publications/2015/AIAA-2015-2272.pdf],
: [https://www.aviationsystems.arc.nasa.gov/publications/2015/AIAA-2015-2272.pdf https://www.aviationsystems.arc.nasa.gov/publications/2015/AIAA-2015-2272.pdf],
: [https://arc.aiaa.org/doi/pdf/10.2514/6.2015-2272 https://arc.aiaa.org/doi/pdf/10.2514/6.2015-2272],
: [https://ntrs.nasa.gov/search.jsp?R=20160004946 https://ntrs.nasa.gov/search.jsp?R=20160004946],
: [https://repository.exst.jaxa.jp/dspace/handle/a-is/561581 https://repository.exst.jaxa.jp/dspace/handle/a-is/561581],
: [https://academic.microsoft.com/#/detail/2323439506 https://academic.microsoft.com/#/detail/2323439506]

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Lee et al 2015f - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 180907080 to Lee et al 2015f

Scipediacontent: Created page with " == Abstract == urate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumption..."