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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.437
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Xiaowei TANG, Mengfan YE, Shengrun ZHANG, Kurt FUELLHART
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Prediction of Taxi-in Time and Analysis of Influencing Factors for Arrival Flights at Airport with a Decentralised Terminal Layout

Authors:Xiaowei TANG, Mengfan YE, Shengrun ZHANG, Kurt FUELLHART

Abstract

Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness. This study investigates terminal layout characteristics, specifically decentralised layouts, to predict and analyse arrival flight taxi-in times. We develop a surface traffic flow calculation method considering arrival and departure flights, eliminating fixed thresholds. We introduce runway-crossing operations for decentralised airports, creating new prediction variables. We consider factors like runway, aircraft type, airline, taxi distance, and time periods. Gradient Boosting Regression Tree predicts taxi-in times, while Lasso analyses factor impact. Our approach yields highly accurate predictions for decentralised airports, with Surface traffic flow and Runway-crossing variables significantly influencing taxi-in times. This research informs airport managers in decentralised layouts, enabling tailored management strategies.

Keywords:air transportation, taxi-in time prediction, terminal layout, airport surface movement, lasso, GBRT, airport management

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