2018 Fiscal Year Annual Research Report
Synergistic ground holding algorithm based on real-time air traffic pattern classification and off-line buffer optimization
| Project/Area Number |
18H05925
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| Allocation Type | Single-year Grants |
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| Research Institution | Japan Aerospace EXploration Agency |
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Principal Investigator |
アンドレエバ森 アドリアナ 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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| Keywords | ground holding / airborne delay / ground delay / machine learning / capacity loss |
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| Outline of Annual Research Achievements |
A dual-component ground holding (GH) algorithm based on real-time air traffic classification and offline ground holding program parameter optimization is developed. The basic design principles governing the ground holding algorithm are developed. Numerical simulations are developed to quantitatively evaluate this new concept. GH program performance is evaluated based on airborne delay, ground delay, and lost throughput costs. Preliminary results show that the developed machine-learning-based traffic pattern classifier can propose ground holding control parameters which would result in 88% of the potential optimal savings.Besides, departure time delays are modeled and will be implemented to increase the fidelity of the developed algorithm.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Numerical simulation to quantitatively evaluate each ground holding program parameters has been developed and used to create the database needed as input for the machine learning algorithm. Suppor vector machine has been applied as a preliminary machine learning algorithm candidate.Results has shown the potential of the proposed dual-component algorithm and also identified areas where work is needed, especially to increase fidelity. In particular, departure time errors have been modeled, and further improvements are going to be made in the second year.
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| Strategy for Future Research Activity |
Research development will continue in two major directions 1) increase the fidelity by implementing more detailed and accurate flight time error models and departure time error models, and 2) investigate other machine larning techniques to increase the prediction accuracy, as well as air traffic controller's acceptance.
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