Characterizing flight delay profiles with a tensor factorization framework

Abstract 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 understanding 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 preliminary 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.