Predicting taxi-out time at congested airports with optimization-based support vector regression methods
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 predictin...
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sg-ntu-dr.10356-850192023-03-04T17:14:17Z Predicting taxi-out time at congested airports with optimization-based support vector regression methods Lian, Guan Zhang, Yaping Desai, Jitamitra Xing, Zhiwei Luo, Xiao School of Mechanical and Aerospace Engineering Support Vector Regression Generalized Linear Model 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. Published version 2018-07-17T04:43:19Z 2019-12-06T15:55:39Z 2018-07-17T04:43:19Z 2019-12-06T15:55:39Z 2018 Journal Article 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-. 1024-123X https://hdl.handle.net/10356/85019 http://hdl.handle.net/10220/45093 10.1155/2018/7509508 en Mathematical Problems in Engineering © 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. 11 p. application/pdf |
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Support Vector Regression Generalized Linear Model Lian, Guan Zhang, Yaping Desai, Jitamitra Xing, Zhiwei Luo, Xiao Predicting taxi-out time at congested airports with optimization-based support vector regression methods |
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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. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Lian, Guan Zhang, Yaping Desai, Jitamitra Xing, Zhiwei Luo, Xiao |
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Article |
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Lian, Guan Zhang, Yaping Desai, Jitamitra Xing, Zhiwei Luo, Xiao |
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Lian, Guan |
title |
Predicting taxi-out time at congested airports with optimization-based support vector regression methods |
title_short |
Predicting taxi-out time at congested airports with optimization-based support vector regression methods |
title_full |
Predicting taxi-out time at congested airports with optimization-based support vector regression methods |
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Predicting taxi-out time at congested airports with optimization-based support vector regression methods |
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Predicting taxi-out time at congested airports with optimization-based support vector regression methods |
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predicting taxi-out time at congested airports with optimization-based support vector regression methods |
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2018 |
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https://hdl.handle.net/10356/85019 http://hdl.handle.net/10220/45093 |
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1759858417047961600 |