Zhang Zhaoyue, Zhang An, Sun Cong, Xiang Shuaida, Guan Jichen, Huang Xuedong
School of Aeronautics, Northwestern Polytechnical University, Xi'an, 710072 China.
College of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300 China.
Mob Netw Appl. 2021;26(1):425-439. doi: 10.1007/s11036-020-01679-0. Epub 2020 Nov 6.
In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.
本文研究了空中交通流的混沌特性，选取地面航空用户易于获取的广播式自动相关监视（ADS-B）数据作为交通流的基础数据，建立了基于非迭代概率统计正则化极限学习机（PSR-ELM）算法的空中交通流时间序列高维预测模型。然后将该算法的预测结果与需要迭代的支持向量回归（SVR）算法的预测结果进行比较。此外，选取新型冠状病毒肺炎（COVID-19）疫情爆发前后的空域运行数据作为实验场景，对不同混沌程度的时间序列预测效果进行对比分析。实验结果表明，PSR-ELM算法具有快速、准确的效果，且当交通流状态稀疏时，混沌程度降低，预测效果得到改善。本研究结果为空域交通流理论提供了参考。
