An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model

Xintai Zhang; Yanwen Xie; Yaping Zhang; Zhiwei Xing; Xiao Luo; Qian Luo

doi:10.1007/978-981-15-0644-4_75

An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model

Xintai Zhang
, Yanwen Xie
, Yaping Zhang^*
, Zhiwei Xing
, Xiao Luo
, Qian Luo

^*Corresponding author for this work

School of Transportation Science and Engineering, Harbin Institute of Technology
Civil Aviation University of China
Second Institute of Civil Aviation Administration of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper is mainly devoted to an prediction method for airport operational situation which is one of the most important parts of the airport operation system. In order to provide theoretical support for high-level airport management, field operation management, air traffic control and airlines, and improve the service capacity of the airport, this paper makes a prediction study of the airport operation situation. Hidden Markov (HMM) prediction model is established based on the analysis of airport operation system. Baum-Welch and Viterbi algorithms are used to solve the prediction results. The model is validated and applied in a domestic hub airport. The results show that the prediction accuracy of HMM is 60 and 20% higher than that of Autoregressive Moving Average Model and Grey Markov model, respectively. It can also improve the situation value of airport operation situation, i.e. airport service capability. This method is more suitable for the analysis of airport operation.

Original language	English
Title of host publication	Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018
Editors	Wuhong Wang, Xiaobei Jiang, Xiaobei Jiang, Martin Baumann
Publisher	Springer
Pages	959-975
Number of pages	17
ISBN (Print)	9789811506437
DOIs	https://doi.org/10.1007/978-981-15-0644-4_75
State	Published - 2020
Externally published	Yes
Event	9th International Conference on Green Intelligent Transportation Systems and Safety, 2018 - Guilin, China Duration: 1 Jul 2018 → 3 Jul 2018

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	617
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	9th International Conference on Green Intelligent Transportation Systems and Safety, 2018
Country/Territory	China
City	Guilin
Period	1/07/18 → 3/07/18

Keywords

Airport traffic
Hidden markov model
Hierarchical division
Operational situation forecast

Access to Document

10.1007/978-981-15-0644-4_75

Cite this

Zhang, X., Xie, Y., Zhang, Y., Xing, Z., Luo, X., & Luo, Q. (2020). An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model. In W. Wang, X. Jiang, X. Jiang, & M. Baumann (Eds.), Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018 (pp. 959-975). (Lecture Notes in Electrical Engineering; Vol. 617). Springer. https://doi.org/10.1007/978-981-15-0644-4_75

Zhang, Xintai ; Xie, Yanwen ; Zhang, Yaping et al. / An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model. Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018. editor / Wuhong Wang ; Xiaobei Jiang ; Xiaobei Jiang ; Martin Baumann. Springer, 2020. pp. 959-975 (Lecture Notes in Electrical Engineering).

@inproceedings{30128554a7ee4b8a8422f9a743af4b43,

title = "An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model",

abstract = "This paper is mainly devoted to an prediction method for airport operational situation which is one of the most important parts of the airport operation system. In order to provide theoretical support for high-level airport management, field operation management, air traffic control and airlines, and improve the service capacity of the airport, this paper makes a prediction study of the airport operation situation. Hidden Markov (HMM) prediction model is established based on the analysis of airport operation system. Baum-Welch and Viterbi algorithms are used to solve the prediction results. The model is validated and applied in a domestic hub airport. The results show that the prediction accuracy of HMM is 60 and 20\% higher than that of Autoregressive Moving Average Model and Grey Markov model, respectively. It can also improve the situation value of airport operation situation, i.e. airport service capability. This method is more suitable for the analysis of airport operation.",

keywords = "Airport traffic, Hidden markov model, Hierarchical division, Operational situation forecast",

author = "Xintai Zhang and Yanwen Xie and Yaping Zhang and Zhiwei Xing and Xiao Luo and Qian Luo",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018 ; Conference date: 01-07-2018 Through 03-07-2018",

year = "2020",

doi = "10.1007/978-981-15-0644-4\_75",

language = "英语",

isbn = "9789811506437",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "959--975",

editor = "Wuhong Wang and Xiaobei Jiang and Xiaobei Jiang and Martin Baumann",

booktitle = "Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018",

address = "德国",

}

Zhang, X, Xie, Y, Zhang, Y, Xing, Z, Luo, X & Luo, Q 2020, An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model. in W Wang, X Jiang, X Jiang & M Baumann (eds), Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018. Lecture Notes in Electrical Engineering, vol. 617, Springer, pp. 959-975, 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018, Guilin, China, 1/07/18. https://doi.org/10.1007/978-981-15-0644-4_75

An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model. / Zhang, Xintai; Xie, Yanwen; Zhang, Yaping et al.
Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018. ed. / Wuhong Wang; Xiaobei Jiang; Xiaobei Jiang; Martin Baumann. Springer, 2020. p. 959-975 (Lecture Notes in Electrical Engineering; Vol. 617).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model

AU - Zhang, Xintai

AU - Xie, Yanwen

AU - Zhang, Yaping

AU - Xing, Zhiwei

AU - Luo, Xiao

AU - Luo, Qian

PY - 2020

Y1 - 2020

N2 - This paper is mainly devoted to an prediction method for airport operational situation which is one of the most important parts of the airport operation system. In order to provide theoretical support for high-level airport management, field operation management, air traffic control and airlines, and improve the service capacity of the airport, this paper makes a prediction study of the airport operation situation. Hidden Markov (HMM) prediction model is established based on the analysis of airport operation system. Baum-Welch and Viterbi algorithms are used to solve the prediction results. The model is validated and applied in a domestic hub airport. The results show that the prediction accuracy of HMM is 60 and 20% higher than that of Autoregressive Moving Average Model and Grey Markov model, respectively. It can also improve the situation value of airport operation situation, i.e. airport service capability. This method is more suitable for the analysis of airport operation.

AB - This paper is mainly devoted to an prediction method for airport operational situation which is one of the most important parts of the airport operation system. In order to provide theoretical support for high-level airport management, field operation management, air traffic control and airlines, and improve the service capacity of the airport, this paper makes a prediction study of the airport operation situation. Hidden Markov (HMM) prediction model is established based on the analysis of airport operation system. Baum-Welch and Viterbi algorithms are used to solve the prediction results. The model is validated and applied in a domestic hub airport. The results show that the prediction accuracy of HMM is 60 and 20% higher than that of Autoregressive Moving Average Model and Grey Markov model, respectively. It can also improve the situation value of airport operation situation, i.e. airport service capability. This method is more suitable for the analysis of airport operation.

KW - Airport traffic

KW - Hidden markov model

KW - Hierarchical division

KW - Operational situation forecast

UR - https://www.scopus.com/pages/publications/85083083287

U2 - 10.1007/978-981-15-0644-4_75

DO - 10.1007/978-981-15-0644-4_75

M3 - 会议稿件

AN - SCOPUS:85083083287

SN - 9789811506437

T3 - Lecture Notes in Electrical Engineering

SP - 959

EP - 975

BT - Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018

A2 - Wang, Wuhong

A2 - Jiang, Xiaobei

A2 - Baumann, Martin

PB - Springer

T2 - 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018

Y2 - 1 July 2018 through 3 July 2018

ER -

Zhang X, Xie Y, Zhang Y, Xing Z, Luo X, Luo Q. An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model. In Wang W, Jiang X, Jiang X, Baumann M, editors, Green, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018. Springer. 2020. p. 959-975. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-0644-4_75

An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this