Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers.

旅客在机场航站楼延误的破坏行为不仅影响人身安全，还降低了民航效率和旅客满意度。这项研究从以下三个方面研究了延误乘客破坏行为的因果机制：环境，管理和个人。收集并分析了2018年深圳机场的航班延误数据。确定了导致延迟旅客破坏行为的主要因素，并使用多元逻辑回归和反向传播（BP）神经网络建立了扰动预警模型。结果表明，所提出的模型和方法是可行的。与Logistic回归模型相比，BP神经网络模型在预测延误乘客的干扰方面具有优势，显示更高的预测准确性。运用BP网络权重分析方法获得各因素对延误乘客行为变化的影响权重。获得了不同因素的影响权重，为解决航班延误乘客的干扰提供了一种辅助决策方法。