Aircraft taxi time prediction: Feature importance and their implications



Wang X, Brownlee AEI, Woodward JR, Weiszer M, Mahfouf M & Chen J (2021) Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies, 124, Art. No.: 102892.

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.

air traffic management; feature importance; machine learning; prediction; taxi time

Transportation Research Part C: Emerging Technologies: Volume 124

FundersEngineering and Physical Sciences Research Council, Engineering and Physical Sciences Research Council and Engineering and Physical Sciences Research Council
Publication date31/03/2021
Publication date online31/12/2020
Date accepted by journal20/11/2020

People (1)


Dr Sandy Brownlee
Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division