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 prediction 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的出发运营至关重要，因为它能够实现主动的拥堵管理。然而，随时间变化的旅客需求以及多个出发设施中设施使用的异质性使得预测充满挑战。本文提出了一种旅客排队预测框架，该框架从运营数据中学习历史客流模式。所提出的模型采用了基于Transformer的架构，利用出发厅和安检口的历史排队长度与等待时间以及值机岛的旅客吞吐量，来捕捉时间依赖性和设施间的关联性。学习到的表征被映射到两个设施专用的预测头，以预测出发厅和安检口的排队长度与等待时间。实验结果表明，该模型能够实现对长达两小时后的准确预测。所提出的方法为机场航站楼运营中主动排队管理和人员重新调配提供了实用的实时决策支持。