Mr. Hansen Long Profile

Hansen Long

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 22 December 2022 Paper

Freight traffic of civil aviation volume forecast based on hybrid ARIMA-LR model

Bin Chen, Jiacheng Liu, Zhouying Ruan, Ming Yue, Hansen Long, Weiping Yao

Proceedings Volume 12460, 124602O (2022) https://doi.org/10.1117/12.2657975

KEYWORDS: Data modeling, Autoregressive models, Performance modeling, Signal attenuation, Integrated modeling, Manufacturing, Time series analysis

Read Abstract +

Freight traffic of civil aviation has developed rapidly because of its advantages of fast transportation speed and high safety. The fluctuation of freight traffic of civil aviation has brought many challenges to air traffic scheduling. If the freight traffic of civil aviation volume can be accurately predicted, the difficulty of air traffic scheduling will be reduced and the transportation efficiency of air cargo will be improved. The current prediction model can’t properly respond to the impact of emergencies. And it is not sensitive to the trend variations caused by policies, epidemics and other factors. In this paper, based on the autoregressive integrated moving average model (ARIMA) and linear regression model (LR), a hybrid ARIMA-LR model is proposed by using an improved Bayesian combined model. Through the prediction of the actual freight traffic of civil aviation volume, it is found that the hybrid ARIMA-LR model can not only better adapt to the changes caused by emergencies, but also have higher overall prediction accuracy than the ARIMA model and LR model. The three indicators of mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE) of the hybrid ARIMA-LR model are 1.06,29.02,0.03 lower than that of the ARIMA model; compared with the LR model, it is reduced by 3.00,92.00,0.06.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years