Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

Lee, Hanbong; Coupe, William J.; Jung, Yoon

doi:10.2514/6.2019-2933

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…As traditional machine learning techniques continue to advance, particularly with regard to their ability to mine nonlinear relationships in data, they are seeing increasing application in studies [11][12][13][14][15][16][17][18] focused on predicting the taxi time of aircraft. In general, expert-defined feature sets [16,18] that may affect an aircraft's taxi time can be categorized into flight properties, airport operational information, traffic conditions, and weather conditions (the details of the influencing factors can be found in Appendix A).…”

Section: Taxi Time Predictionmentioning

confidence: 99%

“…This is achieved by investing the impact of different factors, drawing conclusions on the most important factor for accurately modeling taxi time [5][6][7][8][9][10]. Another kind of data-driven approach is traditional machine learning, which is achieved by constructing feature sets that may affect aircraft taxi time and utilizing extensive historical operational data to establish taxi time prediction models [11][12][13][14][15][16][17][18]. However, these methods have some drawbacks:…”

Section: Introductionmentioning

confidence: 99%

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Multi-Task Dynamic Spatio-Temporal Graph Attention Network: A Variable Taxi Time Prediction Model for Airport Surface Operation

Yang,

Mao

et al. 2024

Aerospace

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Variable taxi time prediction is the core of the Airport Collaborative Decision Making (A-CDM) system. An accurate taxi time prediction contributes to enhancing airport operational efficiency, safety and predictability. The deep dynamic spatio-temporal correlation inherent in airport traffic data is critical for taxi time prediction. However, existing machine learning (deep learning) methods have been unable to thoroughly exploit these correlations. To address this issue, we propose a deep learning-based model called the multi-task dynamic spatio-temporal graph attention network (MT-DSTGAN). Our model also predicts future entire airport traffic flow and taxiing segment traffic flow as auxiliary tasks, with the goal of enhancing the accuracy of aircrafts’ taxi time prediction. The proposed MT-DSTGAN model is implemented and assessed through a case study of Beijing Capital International Airport with a real-world dataset. The advantage of the proposed model, which shows better performance in various evaluation metrics, is demonstrated in a comparative study with other baseline works. In summary, the proposed MT-DSTGAN exhibits promising capabilities in perceiving the dynamic changes in the taxiing process of aircraft and demonstrates the ability to capture complex spatio-temporal correlations in airport traffic data.

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