Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

Weifan Gu, Baohua Guo (), Zhezhe Zhang and He Lu
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Weifan Gu: School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Baohua Guo: School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Zhezhe Zhang: School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China
He Lu: School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China

Sustainability, 2024, vol. 16, issue 10, 1-17

Abstract: With the rapid development of China’s aviation industry, the accurate prediction of civil aviation passenger volume is crucial to the sustainable development of the industry. However, the current prediction of civil aviation passenger traffic has not yet reached the ideal accuracy, so it is particularly important to improve the accuracy of prediction. This paper explores and compares the effectiveness of the backpropagation (BP) neural network model and the SARIMA model in predicting civil aviation passenger traffic. Firstly, this study utilizes data from 2006 to 2019, applies these two models separately to forecast civil aviation passenger traffic in 2019, and combines the two models to forecast the same period. Through comparing the mean relative error (MRE), mean square error (MSE), and root mean square error (RMSE), the prediction accuracies of the two single models and the combined model are evaluated, and the best prediction method is determined. Subsequently, using the data from 2006 to 2019, the optimal method is applied to forecast the civil aviation passenger traffic from 2020 to 2023. Finally, this paper compares the epidemic’s impact on civil aviation passenger traffic with the actual data. This paper improves the prediction accuracy of civil aviation passenger volume, and the research results have practical significance for understanding and evaluating the impact of the epidemic on the aviation industry.

Keywords: BP neural network; SARIMA; combined model; civil aviation passenger traffic; epidemic (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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