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量的混沌特性，选择了便于地面航空用户使用的ADS-B数据作为交通流量的基本数据，并基于该数据对空中交通流量时间序列进行了高维预测。建立了非迭代的PSR-ELM算法。然后将提出的算法的预测结果与需要迭代的SVR算法的预测结果进行比较。此外，以COVID-19疫情爆发前后的空域运行数据为实验场景，并比较分析了不同混沌程度的时间序列的预测效果。实验结果表明，PSR-ELM算法取得了快速，准确的结果，当交通流状态稀疏时，降低了混乱程度，提高了预测效果。这项研究的发现为空中交通流量理论提供了参考。