When Do Data-Driven Models Improve Runway-Configuration Forecasts? Multi-Horizon Operational Evidence from São Paulo/Guarulhos

Publication status

Journal of Air Transport Management, accepted for publication, 2026. DOI and final bibliographic details will be added when assigned.

Abstract

Runway-configuration forecasts can support coordination, but state persistence may inflate accuracy while changes remain undetected. We establish when data-driven models add value at São Paulo/Guarulhos International Airport, where two reciprocal configurations make the current state a strong reference. We integrate 87,540 15-minute movement, schedule, and METAR intervals from 1 January 2022 to 30 June 2024 into 33 predictors under stated availability rules. Forecasts at 15, 30, and 60 minutes are selected by expanding-window validation in 2023 and evaluated on a fixed January–June 2024 future test period, comparing majority, persistence, climatology, and a wind rule with logistic regression, random forest, and gradient boosting, plus three adapted comparators. Locked-test balanced accuracy for the selected models is 0.971, 0.959, and 0.940, against 0.961, 0.942, and 0.904 for persistence; 95% intervals for the paired gains exclude zero. Value concentrates at 60 minutes, where transition recall reaches 0.373 at precision 0.507 and 2.07 false alerts per operational day. Reliance shifts from the current state mainly toward time of day, and less toward wind, as lead time grows. The evidence concerns forecast quality; capacity, delay, workload, and safety outcomes are not measured.

Keywords: runway configuration; airport operations; air traffic management; transition warning; temporal validation; decision support.

BibTeX

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