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Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport

Tony Diana

Transportation Research Part E: Logistics and Transportation Review, 2018, vol. 119, issue C, 149-164

Abstract: This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ordinary least-squared and ridge models performed better than other ensemble learning models. However, the gradient boosting model provided the lowest root mean squared errors in the post-sample. No algorithm fits data better in all cases. This paper recommends selecting the model that provides the best balance between bias and variance.

Keywords: Machine learning; Supervised models; Predictive analytics; Penalized regression; Taxi-out operations (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:transe:v:119:y:2018:i:c:p:149-164