| Project/Area Number |
19K21092
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| Project/Area Number (Other) |
18H05925 (2018)
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund (2019)
Single-year Grants (2018) |
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| Review Section |
0303:Civil engineering, social systems engineering, safety engineering, disaster prevention engineering, and related fields
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| Research Institution | Japan Aerospace EXploration Agency |
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Principal Investigator |
Andreeva-Mori Adriana 国立研究開発法人宇宙航空研究開発機構, 航空技術部門, 研究開発員 (30747499)
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| Project Period (FY) |
2018-08-24 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | ground holding / air traffic management / synergistic algorithm / airborne delay / capacity loss / traffic pattern / machine learning / ground delay / traffic classification / airspace congestion |
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| Outline of Research at the Start |
The purpose of this research is to develop a novel synergistic ground holding algorithm based on real-time air traffic pattern classification and off-line buffer optimization.Our synergistic approach introduces optimal buffers varying with traffic pattern to achieve both practicality and optimality.
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| Outline of Final Research Achievements |
To address the issue of increased air traffic and congestions at hub airports, this research developed a novel synergistic ground holding algorithm based on real-time air traffic pattern classification and off-line buffer optimization. When the expected airborne holding time is expected to exceed a certain constant buffer value, this excess waiting is set as ground holding,i.e. aircraft are kept on the ground before departure, experiencing ground holding. In our research, we considered various real-world uncertainties to determine the optimal buffer applied by the ground holding program. We then built a simulated database and developed a machine-learning-based traffic pattern classifier which, based on traffic features, predicts the optimal ground holding control parameters and potential savings within mean absolute percentage error of 17.96% of the potential optimal ones.
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| Academic Significance and Societal Importance of the Research Achievements |
A concept of a traffic pattern classifier applied to optimal ground holding was proposed.The combination of static and dynamic optimization approaches allowed near-optimal solutions easily implemented in real-world. The potential of machine learning for air traffic management was also demonstrated.
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