Causal analysis framework of flight service

Flight departure delay is a key and difficult problem that the civil aviation industry is unanimously concerned about. Aiming at the problem of unclear reasons for flight delays, the BLCNS-LV-IDA causality analysis framework is proposed. In the discipline of flight ground support business, causal analysis of flight departure delays is done on both a qualitative and quantitative level from the standpoint of causal inference. Firstly, a causal network model is built with the departure delay time as the goal variable using the BLCNS local causal structure learning algorithm, which combines feature selection with greedy search for maximal ancestral graph (GSMAG). Secondly, the causal effect of each edge in each equivalent network is evaluated based on the LV-IDA algorithm according to the obtained causal network. The experimental results show that the BLCNS causality discovery method has certain advantages in dealing with large sample data sets of multiple variables. In a data set with 50 nodes, compared with the baseline algorithm, the F₁ value of BLCNS increased by −0.303, 0.008, and 0.132, respectively, with sample sizes of 1000, 5000, and 10000. It presents a clear upward trend. In addition, the running time of BLCNS has been shortened by 16.29%. The causal effect between nodes clarifies the specific strength of each node's impact on flight delays. It provides guidance for the fine management of flight guarantees and reducing flight delays.