Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85019
Title: Predicting taxi-out time at congested airports with optimization-based support vector regression methods
Authors: Lian, Guan
Zhang, Yaping
Desai, Jitamitra
Xing, Zhiwei
Luo, Xiao
Keywords: Support Vector Regression
Generalized Linear Model
Issue Date: 2018
Source: Lian, G., Zhang, Y., Desai, J., Xing, Z., & Luo, X. (2018). Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods. Mathematical Problems in Engineering, 2018, 7509508-.
Series/Report no.: Mathematical Problems in Engineering
Abstract: Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR) approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO) and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK) is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA) optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.
URI: https://hdl.handle.net/10356/85019
http://hdl.handle.net/10220/45093
ISSN: 1024-123X
DOI: 10.1155/2018/7509508
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2018 Guan Lian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

SCOPUSTM   
Citations 20

12
Updated on Mar 21, 2024

Web of ScienceTM
Citations 20

7
Updated on Oct 25, 2023

Page view(s)

356
Updated on Mar 28, 2024

Download(s) 50

130
Updated on Mar 28, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.