Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints

2026-05-30

AI Summary

This study addresses the challenge of predicting passenger queues at heterogeneous airport facilities—specifically boarding gates and security checkpoints—where dynamic demand fluctuations and facility heterogeneity complicate accurate forecasting. To this end, we propose the first Transformer-based joint prediction framework that integrates multi-source time-series data, including historical queue lengths, waiting times, and check-in island throughput. The model employs a shared Transformer encoder to explicitly capture dynamic inter-facility dependencies and utilizes dual-task MLP heads to simultaneously predict queue states for both facility types. Experimental results demonstrate that the proposed approach accurately forecasts queue lengths and waiting times up to two hours ahead, offering actionable insights for real-time airport operations management and resource allocation.

Abstract

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.

Problem

• passenger queue forecasting
• airport terminal
• departure gates
• security checkpoints
• congestion management

Innovation

• Transformer-based forecasting
• passenger queue prediction
• inter-facility correlation
• airport terminal operations
• temporal dependency modeling