The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase boarding time. The passenger behavior is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers are travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and low risk of virus transmission. Our results show that the consideration of groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario.

电晕大流行显着改变了机场的飞机和旅客处理流程。在我们的贡献中，我们专注于飞机登机的时间关键过程，其中有关乘客之间物理距离的规定将显着增加登机时间。乘客行为在经过现场验证的随机元胞自动机模型中实现，该模型由一个模块扩展以评估传输风险。我们建议改进登机流程，考虑到大多数乘客是一起旅行的，应该作为一个整体登机和就座。使用遗传算法解决组间个体交互最小化的组的 NP 硬席位分配问题。然后，改进后的座位分配用于推导出相关的登机顺序，旨在缩短登机时间和降低病毒传播风险。我们的结果表明，与大流行情景中应用的标准随机登机程序相比，考虑团体将显着有助于加快登机速度（减少约 60% 的时间）和降低传播风险（减少 85%）。