Sliding window change point detection based dynamic network model inference framework for airport ground service process

Abstract

With the rapid development of civil aviation, the number of airport flights is continually increasing and the low efficiency of flight ground services has become one of the main reasons for flight delays. Obtaining a precise description of the flight ground service network is of great importance to the efficient and safe operation of airports. In this study, we constructed a dynamic network model inference framework based on sliding window change point detection. First, a change point detection algorithm was proposed to divide all time points into two parts, namely, a set of possible change points and a set of completely impossible change points, which greatly reduces the true change point search space. Then, two novel change point elimination judgment rules were designed and combined with the structure learning algorithm to achieve iterative optimization within the possible change point sets, obtain the final true change points, and realize precise dynamic network inference. Based on the experimental results, our proposed method has a higher accuracy and computational efficiency than the state-of-the-art methods, and it can be easily adapted to large-scale datasets. Finally, the proposed model was used in an airport flight ground service analysis. Each day was divided into four stages and the potential dependence relationships between different nodes in each stage were accurately determined. Thus, the model has great practical significance for the accurate scheduling and efficient operation of airport ground services.