用Transformer预测航站楼排队情况，提前两小时预警拥堵。
Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints
- 基于Transformer建模历史客流，捕捉时间与设施间关联
- 可提前两小时准确预测登机口和安检口的排队长度与等待时间
- 适合机场运营方做实时调度与人力调配
机场航站楼中精准的乘客排队预测对高效离港运营至关重要，有助于主动管理拥堵。然而，动态变化的乘客需求及多个离港设施间使用差异使预测困难。本文提出一种基于运营数据学习历史客流模式的排队预测框架。模型采用Transformer架构，利用过去登机口与安检口的队列长度、等待时间，以及值机岛的旅客通行量，捕捉时间依赖性和设施间相关性。学习到的表示通过两个设施特异性预测头，分别预测登机口与安检口的队列长度和等待时间。实验结果表明，该方法可在两小时前实现高精度预测。所提方法为机场航站楼运营中的主动排队管理与人员调配提供了实用的实时决策支持。
原文摘要 · Abstract (English)
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.
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