Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput
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Computer Science > Machine Learning
Title:Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput
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.
| Subjects: | Machine Learning (cs.LG); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2608.02950 [cs.LG] |
| (or arXiv:2608.02950v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02950
arXiv-issued DOI via DataCite (pending registration)
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