Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)

Research on Improved Grey Prediction Method of Airport Passenger Throughput

Authors
Jia-juan Chen, Da-chuan Ding, Chuan-tao Wang
Corresponding Author
Da-chuan Ding
Available Online July 2019.
DOI
https://doi.org/10.2991/masta-19.2019.16How to use a DOI?
Keywords
Improved grey prediction, Passenger throughput, Airport
Abstract

Airport passenger throughput prediction is of great significance to the operation and development of the airport. Based on the grey prediction method, this paper studies the future passenger throughput of the Capital International Airport and the Beijing New Airport. In this paper, the traditional grey prediction model is improved and the passenger throughput of the two airports from 2019 to 2025 is predicted and analyzed. The prediction results show that the improved grey model can improve the accuracy and reduce the prediction error.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
Series
Advances in Intelligent Systems Research
Publication Date
July 2019
ISBN
978-94-6252-761-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/masta-19.2019.16How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jia-juan Chen
AU  - Da-chuan Ding
AU  - Chuan-tao Wang
PY  - 2019/07
DA  - 2019/07
TI  - Research on Improved Grey Prediction Method of Airport Passenger Throughput
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 96
EP  - 100
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.16
DO  - https://doi.org/10.2991/masta-19.2019.16
ID  - Chen2019/07
ER  -