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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Aircraft taxi time prediction: Feature importance and their implications
Author(s): Wang, Xinwei
Brownlee, Alexander
Woodward, John
Weiszer, Michal
Mahfouf, Mahdi
Chen, Jun
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Keywords: air traffic management
feature importance
machine learning
taxi time
Citation: Wang X, Brownlee A, Woodward J, Weiszer M, Mahfouf M & Chen J (2020) Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies.
Abstract: Taxiing remains a major bottleneck at many airports. Recently, several approaches to allocating efficient routes for taxiing aircraft have been proposed. The routing algorithms underpinning these approaches rely on accurate prediction of the time taken to traverse each segment of the taxiways. Many features impact on taxi time, including the route taken, aircraft category, operational mode of the airport, traffic congestion information, and local weather conditions. Working with real-world data for several international airports, we compare multiple prediction models and investigate the impact of these features, drawing conclusions on the most important features for accurately modelling taxi times. We show that high accuracy can be achieved with a small subset of the features consisting of those generally important across all airports (departure/arrival, distance, total turns, average speed and numbers of recent aircraft), and a small number of features specific to particular target airports. Moving from all features to this small subset results in less than a 1 percentage-point drop in movements correctly predicted within 1, 3 and 5 minutes.
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Notes: Output Status: Forthcoming

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