在空中交通管理和民航机场运营过程中,从过去的运营中获得的经验对于设计合理的策略至关重要.因此,本文使用大量时空飞行数据来识别相似的航空交通和延误模式,这对于更好地了解航空系统和制订管理决策至关重要.但是,由于数据集隐含了复杂的依赖关系以及高维时空之间的交互作用,因此挖掘数据中的重要特征和模式非常具有挑战性.本文提出了高维历史飞行数据的张量因子分解框架.我们通过潜在类别分析模型,并使用2014—2017年中国224个机场的空中交通运行数据证明了该框架的有效性.研究发现每个维度可以清晰地代表不同的运行模式.为了证明这些模式的有效性,我们创建了一个估计模型,该模型可实现对航班延误程度的初步判断.上述结果可以根据历史情景获得的经验,帮助机场运营商和空中交通管理人员更好地了解空中交通和延误模式.“,”In air traffic and airport management, experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario. Therefore, this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns, which become critical for gaining a better under-standing of the aviation system and relevant decision-making. However, as the datasets imply complex dependence and higher-order interactions between space and time, retrieving significant features and patterns can be very challenging. In this paper, we propose a probabilistic framework for high-dimensional historical flight data. We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017. We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations. To prove the effectiveness of these patterns, we then create an estimation model that provides prelimi-nary judgment on the airport delay level. The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.