### 基于分形插值的空中交通流量短期预测

1. 中国民航大学 空管学院, 天津 300300
• 收稿日期:2021-03-29 修回日期:2021-05-14 出版日期:2022-09-15 发布日期:2021-07-20
• 通讯作者: 王飞,E-mail:feiwang@cauc.edu.cn E-mail:feiwang@cauc.edu.cn
• 基金资助:
国家自然科学基金(U1833103);中央高校基本科研业务费(3122019129)

### Short-term prediction of air traffic flow based on fractal interpolation

WANG Fei, HAN Xiangyu

1. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
• Received:2021-03-29 Revised:2021-05-14 Online:2022-09-15 Published:2021-07-20
• Supported by:
National Natural Science Foundation of China (U1833103); the Fundamental Research Funds for the Central Universities (3122019129)

Abstract: To implement optimization and management of air traffic flow accurately, a short-term flow prediction method is proposed based on fractal interpolation. Firstly, the Hurst index is calculated by the Rescaled Range Analysis (R/S) method to identify the fractal features of flow time series. Secondly, the fractal interpolation model is used to establish the iterated function system for each similar day, and the iterative function system of all similar days is weighted to form an iterated function system in the statistical sense. Starting from any given point, the stable attractor curve is obtained through multiple iterations, and then the flow prediction values are obtained. Finally, the actual operation data of 35 days are collected for example analysis. The results show that the Hurst index of 60 min scale flow time series is 0.333 6, so that the time series has fractal characteristics. The equilibrium coefficient and the average absolute relative error of prediction results is 0.957 4 and 0.086 7, respectively. The flow time series with the statistical scale of 30 min and 15 min also have fractal characteristics, and the equilibrium coefficients are 0.925 9 and 0.875 7, respectively. There is no significant difference between the prediction results of the similar days close to the prediction day and the similar days of the same day in different weeks. Compared with the traditional model, the method proposed has better adaptability for fractal time series prediction. The fractal interpolation model is found to be feasible and effective for the short-term prediction of air traffic flow, and the prediction accuracy decreases with the decrease of statistical scale.