Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning
Abstract Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-09-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00333-3 |
| _version_ | 1797451524138336256 |
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| author | Haoran Gao Yubing Xie Changjiang Yuan Xin He Tiantian Niu |
| author_facet | Haoran Gao Yubing Xie Changjiang Yuan Xin He Tiantian Niu |
| author_sort | Haoran Gao |
| collection | DOAJ |
| description | Abstract Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure. |
| first_indexed | 2024-03-09T14:55:53Z |
| format | Article |
| id | doaj.art-2b4e838d8edf49beaa043f55207d6cae |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-09T14:55:53Z |
| publishDate | 2023-09-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2b4e838d8edf49beaa043f55207d6cae2023-11-26T14:12:02ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-09-0116111410.1007/s44196-023-00333-3Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine LearningHaoran Gao0Yubing Xie1Changjiang Yuan2Xin He3Tiantian Niu4Institute Office, Civil Aviation Flight, University of ChinaKaizhou District Transportation Construction Development CenterAir Traffic Management College, Civil Aviation Flight University of ChinaAir Traffic Management College, Civil Aviation Flight University of ChinaAir Traffic Management College, Civil Aviation Flight University of ChinaAbstract Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.https://doi.org/10.1007/s44196-023-00333-3Arrival runway occupancy timeQuick access recorder dataGenetic algorithm–particle swarm optimization hybrid algorithmShapley additive explanation modelMachine learning |
| spellingShingle | Haoran Gao Yubing Xie Changjiang Yuan Xin He Tiantian Niu Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning International Journal of Computational Intelligence Systems Arrival runway occupancy time Quick access recorder data Genetic algorithm–particle swarm optimization hybrid algorithm Shapley additive explanation model Machine learning |
| title | Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning |
| title_full | Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning |
| title_fullStr | Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning |
| title_full_unstemmed | Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning |
| title_short | Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning |
| title_sort | prediction of aircraft arrival runway occupancy time based on machine learning |
| topic | Arrival runway occupancy time Quick access recorder data Genetic algorithm–particle swarm optimization hybrid algorithm Shapley additive explanation model Machine learning |
| url | https://doi.org/10.1007/s44196-023-00333-3 |
| work_keys_str_mv | AT haorangao predictionofaircraftarrivalrunwayoccupancytimebasedonmachinelearning AT yubingxie predictionofaircraftarrivalrunwayoccupancytimebasedonmachinelearning AT changjiangyuan predictionofaircraftarrivalrunwayoccupancytimebasedonmachinelearning AT xinhe predictionofaircraftarrivalrunwayoccupancytimebasedonmachinelearning AT tiantianniu predictionofaircraftarrivalrunwayoccupancytimebasedonmachinelearning |