A generative adversarial imitation learning approach for realistic aircraft taxi-speed modelling

• Main Authors: Pham, Duc-Thinh, Tran, Thanh-Nam, Alam, Sameer, Duong, Vu N.
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: 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
• Online Access: https://hdl.handle.net/10356/152909
• Institution: Nanyang Technological University

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.