Variable taxi-out time prediction based on machine learning with interpretable attributes
This paper presents a machine learning-based approach for predicting the taxi-out time, with the departure process decomposed into two components: the time taken to travel from the gate to the departure queue, and the time spent in the departure queue. Gradient-Boosted Decision Tree (GBDT) models ar...
Main Authors: | , , , |
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Other Authors: | |
Format: | Journal Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/178975 |