# An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality
###### Abstract
The delay propagation process mainly refers to the study of delay propagation from the perspective of a dynamic process [3; 14; 16; 17; 18]. In terms of simulating the delay propagation process, [PERSON] et al. [19] established an agent-based data-driven model to simulate the delay propagation process. [PERSON] et al. [20] constructed a mathematical delay propagation model and a Bayesian network-based arrival delay model according to the relationship between flights to study the relationship between arrival delays. They consider arrival delays as a source of other delays. Among related flights, the spread of arrival delays can exacerbate departure delays at busy hub airports. Delays can be reduced by the availability of scheduled flight schedules, thereby reducing delay propagation. Severe weather can cause huge delays. [PERSON] et al. [21] used a data-driven model to reproduce the delay propagation dynamics in the US airport network. The model focuses on the day of a major storm in the United States. Therefore, it is concluded that bad weather may cause network system congestion, which can be addressed by considering different interventions. After that, [PERSON] et al. defined a metric capable of quantifying the level of network congestion, and found that even under normal operating conditions, there is a nonnegligible risk of system instability. At the same time, they also argue that the connectivity between passengers and crew is the most relevant internal factor for delay propagation [22]. [PERSON] et al. [8] analyzed delay propagation from different perspectives, and established two different types of new delay propagation models using the epidemic propagation process. These two types of models are flight-based propagation models and airport-based propagation models. On this basis, they validated the model using European historical track data. [PERSON] et al. [23] used two different agent-based models (a first-come-first-served model and an ATFM-based time-planning-first-served model) to simulate delay propagation and assess the impact of network disruptions in the US and European airport networks, and found that a first-come-first-served model resulted in greater delay propagation. [PERSON] et al. [24] used a Bayesian network to build an airline network delay propagation model. The model considers multiple connectivity sourcesfor airlines, including aircraft, crew connections, and passenger connections, which can identify weak links in the flight network based on past flight data. [PERSON] et al. [25] built a heterogeneous network model according to the different connections of departing flights. Departing flights are divided into different clusters according to the connections between them, and the evolution of these clusters in multistage scheduling shows a propagation mechanism. [PERSON] et al. [26] proposed an airport sector network delay model suitable for flight delay estimation in the air transportation network. The model was applied to the 21 busiest airports in China, and by comparing the real delay data with the data estimated by the network delay model, it can be found that the model could well simulate the situation that the airport or airspace may become a delay in the air transportation system [26]. Unlike traditional studies on flight delays, [PERSON] et al. [1]. study the trend of flight delay transmission in a region and show the relationship between delays occurring at multiple airports. The delay propagation mechanism mainly includes the generation form and propagation mode of delay [3; 16; 17]. In terms of the delay propagation mechanism, [PERSON] et al. [27] and [PERSON] et al. [13] reallocated existing idle resources to those flights most prone to delay transmission, reducing the impact of the next phase of delays on the airport without changing the personnel and cost of the original plan. [PERSON] et al. [10] construct an analytical queuing and network decomposition model to study the complex phenomenon of delay propagation in large airport networks.