Predicting Abnormal Runway Occupancy Times and Observing Related Precursors

Friso, Herrema Floris; Richard, Curran; Visser, H.G.; Treve, Vincent; Desart, Bruno

doi:10.2514/1.i010548

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…In another work Friso et al [7] did not make a generic ROT prediction model. Instead, the authors focused on the detection of abnormal AROTs and used a Decision Tree to build the "what-if" statements.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies have focused on making the model more general [1], in order to be able to be applied to many different airports or to be a component in simulation software. Other works have attempted to make the real-time prediction [7] [8] at different distances from the runway threshold. General prediction models which use data from various sources often have an advantage in terms of generalizability.…”

Section: Introductionmentioning

confidence: 99%

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Model Generalization in Arrival Runway Occupancy Time Prediction by Feature Equivalences

Nguyen¹,

Pham²,

Lilith³

et al. 2022

Preprint

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General real-time runway occupancy time prediction modelling for multiple airports is a current research gap. An attempt to generalize a real-time prediction model for Arrival Runway Occupancy Time (AROT) is presented in this paper by substituting categorical features by their numerical equivalences. Three days of data, collected from Saab Sensis' Aerobahn system at three US airports, has been used for this work. Three tree-based machine learning algorithms: Decision Tree, Random Forest and Gradient Boosting are used to assess the generalizability of the model using numerical equivalent features. We have shown that the model trained on numerical equivalent features not only have performances at least on par with models trained on categorical features but also can make predictions on unseen data from other airports.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%