Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput

2026-08-03

AI Summary

This study addresses the challenge of hourly passenger flow forecasting for airport security staff scheduling by proposing a method that does not require explicit passenger–flight matching. To overcome the limitation that flight schedules typically provide only departure times, the authors innovatively employ a truncated Poisson kernel to map scheduled flights into an interpretable arrival intensity signal at security checkpoints. This signal is then integrated with historical throughput data, scheduled activities, and temporal features within a Temporal Fusion Transformer-based time series forecasting model. Experimental results demonstrate that the proposed approach achieves a weighted mean absolute percentage error (WMAPE) of 9.33% in direct six-hour-ahead predictions, outperforming RNN (12.16%) and LSTM (11.37%) baselines, with particularly strong performance during peak hours. Moreover, recursive forecasts over 24–96 hours maintain stable errors ranging from 10.60% to 11.04%.

Abstract

Checkpoint staffing requires accurate forecasts of when screening demand will occur, yet flight schedules record departure times rather than passenger arrival times at security checkpoints. This study develops a framework that converts known flight schedules into temporally aligned signals for forecasting hourly checkpoint throughput. Using 2023-2024 Transportation Security Administration throughput data and Cirium Diio flight schedules for Hartsfield-Jackson Atlanta International Airport, domestic and international seat capacity was distributed across pre-departure hours using truncated Poisson kernels. A Temporal Fusion Transformer then combined these schedule-derived arrival-intensity signals with historical throughput, scheduled activity, and temporal variables. Models were trained chronologically, with July-December 2024 reserved for testing, and evaluated against recurrent neural network and long short-term memory models across five random seeds. For direct six-hour forecasts, the proposed model achieved a weighted mean absolute percentage error of 9.33%, compared with 12.16% for the recurrent neural network and 11.37% for long short-term memory, while also producing the lowest errors during peak periods. With six-hour recursive updates, errors remained between 10.60% and 11.04% across 24-96 hour horizons, although longer horizons contained fewer valid forecast origins. By transforming scheduled departures into interpretable pre-departure screening-load signals without requiring passenger-flight matching, the framework supports advance staffing, lane-opening, and multiday checkpoint planning. Because observed throughput reflects realized processing rather than unconstrained arrivals, the forecasts should be interpreted together with local staffing, capacity, queue, and wait-time information.

Problem

• checkpoint throughput forecasting
• flight schedule
• passenger arrival prediction
• airport security
• temporal forecasting

Innovation

• Temporal Fusion Transformer
• schedule-informed forecasting
• truncated Poisson kernel
• checkpoint throughput prediction
• pre-departure arrival intensity