Machine learning techniques for taxi-out time prediction with a macroscopic network topology
File(s)Taxi prediction DASC2018.pdf (1021.42 KB)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Accurate prediction of taxi-out time is essential for enhancing airport performance and flight efficiency. In this paper, we apply machine learning techniques to predict the taxi- out time of departure aircraft at Shanghai Pudong International Airport. The exploration of historical data reveals several relevant influencing factors of taxi-out time as well as their correlations. We formulate an extensive system of predictors for our machine learning approach, based on a macroscopic network topology from an aggregate view. The predictors can be divided into 4 categories; namely surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs) and slot resource demand indices (SRDIs). Three machine learning methods: linear regression (LR), support vector machines (SVM) and random forest (RF) are formulated using one-day and one-month training samples, and applied to new test dataset to validate the prediction performance. Computational results show that the training RF model using one-month sample significantly outperform other models in terms of prediction accuracy. The proposed methodology can bring significant benefits to analyzing airport ground movement performance and support the activities of airport decision making.
Date Issued
2018-12-10
Date Acceptance
2018-05-24
Publisher
IEEE
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source Database
manual-entry
Identifier
https://ieeexplore.ieee.org/document/8569664
Source
37th AIAA/IEEE Digital Avionics Systems Conference (DASC)
Subjects
Science & Technology
Technology
Engineering, Aerospace
Engineering
air transport
taxi-out time
machine learning
prediction
macroscopic network topology
MODEL
air transport
taxi-out time
machine learning
prediction
macroscopic network topology
Publication Status
Published
Start Date
2018-09-23
Finish Date
2018-09-27
Country
London, UK
Date Publish Online
2018-12-10