首页|Reports from Nanyang Technological University Advance Knowledge in Machine Learn ing (Variable Taxi-out Time Prediction Based On Machine Learning With Interpreta ble Attributes)
Reports from Nanyang Technological University Advance Knowledge in Machine Learn ing (Variable Taxi-out Time Prediction Based On Machine Learning With Interpreta ble Attributes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Singapore, Singapore, by News Rx journalists, research stated, "This paper presents a machine learning-based a pproach 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 qu eue, and the time spent in the departure queue. Gradient-Boosted Decision Tree ( GBDT) models are trained to predict the two components using different feature s ets, and a comparison of both model shows that they can provide better predictio n accuracy compared with conventional methods, with a Root Mean Squared Error (R MSE) of 1.79 minutes and 0.92 minutes when predicting the taxiing and queuing ti mes respectively, and 78% and 96% of predictions fal ling within a<<2 minute error margin ." Financial support for this research came from Saab AB (publ).
SingaporeSingaporeAsiaCyborgsEme rging TechnologiesMachine LearningNanyang Technological University
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