Taxi-out time prediction is one of the key elements required for successful implementation of Airport Collaborative Decision Making (A-CDM) system. In this paper, we discuss an application of a Machine Learning technique called Random Forest algorithm to predict taxi-out time. Using information retrieved from Incheon International airport’s A-CDM and METAR databases, the model is trained and tested. This paper presents the process of selecting explanatory variables (i.e., features) using feature importance and demonstrates the prediction accuracy of the resulting model.
Taxi-Out Time Prediction at a Busy Airport using Random Forest Algorithm
2021-10-03
1719660 byte
Conference paper
Electronic Resource
English
Airport Taxi-Out Prediction Using Approximate Dynamic Programming
Transportation Research Record | 2008
|Engineering Index Backfile | 1952