Accurate passenger queue forecasting in airport terminals is essential for efficient departure operations, as it enables proactive congestion management. However, time-varying passenger demand and heterogeneous facility usage across multiple departure facilities make forecasting challenging. In this work, we propose a passenger queue forecasting framework that learns historical passenger flow patterns from operational data. The proposed model employs a Transformer-based architecture to capture temporal dependencies and inter-facility correlations using past queue length and waiting time at departure gates and security checkpoints, together with passenger throughput at check-in islands. The learned representations are mapped to two facility-specific MLP heads to predict queue length and waiting time at departure gates and security checkpoints. Experimental results demonstrate accurate forecasts up to two hours ahead. The proposed approach offers practical real-time decision support for proactive queue management and staff reallocation in airport terminal operations.

翻译：准确的机场航站楼旅客排队预测对于高效离港运营至关重要，因其能够支持主动式拥堵管理。然而，旅客需求的时变特性以及多个离港设施间异构设施的使用模式使得预测极具挑战性。本文提出一种旅客排队预测框架，通过运营数据学习历史客流模式。该模型采用基于Transformer的架构，利用登机口与安检处的历史排队长度、等待时间以及值机岛旅客吞吐量，捕捉时间依赖关系及设施间关联性。学习得到的表征被映射至两个设施专用MLP头部，分别预测登机口与安检处的排队长度和等待时间。实验结果表明，该模型可实现长达两小时的前瞻性精准预测。所提方法为机场航站楼运营中主动式排队管理与人员重分配提供了实用的实时决策支持。