A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling
Classical approaches for modeling aircraft taxi speed assume constant speed or use a turning rate function to approximate taxi timings for taxiing aircraft. However, those approaches cannot predict the Spatio-temporal component of aircraft-taxi trajectory due to a lack of consideration of the comple...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/152909 |
| _version_ | 1826115920448192512 |
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| author | Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer Duong, Vu N. |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer Duong, Vu N. |
| author_sort | Pham, Duc-Thinh |
| collection | NTU |
| description | Classical approaches for modeling aircraft taxi speed assume constant speed or use a turning rate function to approximate taxi timings for taxiing aircraft. However, those approaches cannot predict the Spatio-temporal component of aircraft-taxi trajectory due to a lack of consideration of the complexity and stochasticity of airport-airside movements and interactions. This research adopts the Generative Adversarial Imitation Learning (GAIL) algorithm for aircraft taxi-speed modeling while considering multiple operational factors including surrounding traffic on the ground and target take-off time. The proposed model can learn and reproduce the ground movement patterns in a real-world dataset under different circumstances. In addition, the characteristics of the taxi-speed model are also analyzed, especially focusing on handling conflict scenarios with surrounding traffic. Finally, the travel time of the aircraft from starting to target positions is compared with baseline models and actual taxiing data. The proposed model outperforms all the baseline models with a significant margin. In terms of spatial completion (SC), it achieves up to 97.1% for arrivals and 88.3% for departures. The results also show significantly high performance for temporal completion. The model achieves a stable performance with low Root Mean Square Error (RMSE) (16.8 seconds for arrivals, 32.4 seconds for departures) and Mean Absolute Percentage Error (MAPE) (4.4% for arrivals and 7.6% for departures). Our model's errors are 72% lower for arrivals and 48% lower for departures when compared to other baseline models. |
| first_indexed | 2024-10-01T04:03:00Z |
| format | Journal Article |
| id | ntu-10356/152909 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:03:00Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1529092021-12-11T20:10:26Z A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer Duong, Vu N. School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Engineering::Aeronautical engineering::Flight simulation Generative Adversarial Imitation Learning Aircraft Ground Movement Classical approaches for modeling aircraft taxi speed assume constant speed or use a turning rate function to approximate taxi timings for taxiing aircraft. However, those approaches cannot predict the Spatio-temporal component of aircraft-taxi trajectory due to a lack of consideration of the complexity and stochasticity of airport-airside movements and interactions. This research adopts the Generative Adversarial Imitation Learning (GAIL) algorithm for aircraft taxi-speed modeling while considering multiple operational factors including surrounding traffic on the ground and target take-off time. The proposed model can learn and reproduce the ground movement patterns in a real-world dataset under different circumstances. In addition, the characteristics of the taxi-speed model are also analyzed, especially focusing on handling conflict scenarios with surrounding traffic. Finally, the travel time of the aircraft from starting to target positions is compared with baseline models and actual taxiing data. The proposed model outperforms all the baseline models with a significant margin. In terms of spatial completion (SC), it achieves up to 97.1% for arrivals and 88.3% for departures. The results also show significantly high performance for temporal completion. The model achieves a stable performance with low Root Mean Square Error (RMSE) (16.8 seconds for arrivals, 32.4 seconds for departures) and Mean Absolute Percentage Error (MAPE) (4.4% for arrivals and 7.6% for departures). Our model's errors are 72% lower for arrivals and 48% lower for departures when compared to other baseline models. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and the Civil Aviation Authority of Singapore. 2021-12-09T08:52:30Z 2021-12-09T08:52:30Z 2021 Journal Article Pham, D., Tran, T., Alam, S. & Duong, V. N. (2021). A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling. IEEE Transactions On Intelligent Transportation Systems. https://dx.doi.org/10.1109/TITS.2021.3119073 1524-9050 https://hdl.handle.net/10356/152909 10.1109/TITS.2021.3119073 en IEEE Transactions on Intelligent Transportation Systems © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TITS.2021.3119073. application/pdf |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Engineering::Aeronautical engineering::Flight simulation Generative Adversarial Imitation Learning Aircraft Ground Movement Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer Duong, Vu N. A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling |
| title | A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling |
| title_full | A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling |
| title_fullStr | A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling |
| title_full_unstemmed | A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling |
| title_short | A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling |
| title_sort | generative adversarial imitation learning approach for realistic aircraft taxi speed modelling |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Engineering::Aeronautical engineering::Flight simulation Generative Adversarial Imitation Learning Aircraft Ground Movement |
| url | https://hdl.handle.net/10356/152909 |
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