# An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality
###### Abstract  
The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a \(t\)-test and propagation delay analysis. Taking China's civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports. a 20221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200221200212  
due to the rapid development of the civil aviation industry and the rapid increase in the number of flights, many of the methods previously proposed are no longer applicable [4]. For example, flight slot optimization methods at the strategic level and flight scheduling methods at the tactical level can reduce flight delays to a certain extent, but these methods may not work when the number of flights increases to a certain extent [5; 6]. Therefore, in the development process of the civil aviation industry, flight delay has always been a difficult problem that needs to be challenged. The reasons for initial flight delays are attributed to five categories: air carrier delays, extreme weather delays, the national aviation system, aircraft late arrival, and safety [7]. Since airports are connected by air routes and have connectivity in resources, initial flight delays may cause flight delays at the destination airport, and delays may spread to other associated airports, spreading throughout the airport network from the time that the initial delay is caused. According to different research objects, the research on delay propagation is divided into the perspective of aircraft and the perspective of airports [8]. From the perspective of aircraft, the main study is how delays are propagated between different segments when a single aircraft is flying multiple segments. These studies provide decision support for airline fleet arrangements [9]. From the airport's point of view, the main study object is how delays are propagated in the airport network [1; 10; 11; 12; 13]. For example, flight delays at an airport will affect related airports and which airports are key nodes in the airport network. Research on these issues will help air traffic management to reduce congestion from the perspective of the entire airport network, while improving operational efficiency and reducing flight delays. This paper focuses on the study of flight delay propagation mechanisms from the perspective of airports. The main goal is to find the causal relationship between airports and the magnitude of the impact, so as to control the level of delay from the perspective of the overall airport network. At present, researchers mainly analyze airport delay propagation from the aspects of simulating the delay propagation process, airport delay propagation mechanism, and delay propagation causality [13; 14; 15].