arXiv:2606.07622v1 Announce Type: new 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
The increasing complexity of air travel and passenger volumes, coupled with advancements in AI technologies like Transformers, creates a timely opportunity for developing more sophisticated queue management systems.
Efficient airport operations are crucial for global commerce and leisure, and advancements in forecasting can significantly improve passenger experience, reduce delays, and optimize resource allocation.
This development introduces more precise, AI-driven tools for managing passenger flow, moving beyond simpler statistical models to anticipate congestion more effectively. Airport operators can now proactively adapt to dynamic passenger demands.
- · Airport Authorities
- · Aviation Technology Providers
- · Passengers
- · Airlines
- · Airports relying solely on manual or basic forecasting methods
- · Passengers experiencing frequent delays
Improved passenger flow and reduced wait times at security checkpoints and departure gates.
Enhanced operational efficiency leading to potential cost savings for airports and airlines.
Wider adoption of AI-driven predictive analytics across various transportation hubs and public services for real-time congestion management.
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Read at arXiv cs.LG