Pre-arrival OCC decision support for aircraft turnaround coordination: a human-in-the-loop architecture and scenario-based evaluation
- 1Institut für Flugführung, Technische Universität Braunschweig , ,
Abstract
Aircraft turnaround coordination depends on maintenance, catering, passenger-assistance, gate, and bottleneck information that may become available before arrival but is often handled through fragmented workflows. This paper presents a human-in-the-loop Operations Control Center (OCC) decision-support architecture for pre-arrival aircraft turnaround coordination. The architecture integrates component-aware maintenance risk indicators, service-area inventory readiness, process-class bottleneck forecasting, passenger-assistance readiness logic, and a transparent rule-based fusion layer that converts heterogeneous signals into readiness scores, alert levels, and operator-facing action queues. The evaluation is deliberately framed as controlled synthetic scenario analysis rather than airline deployment validation. Existing module-level holdout results are combined with a deterministic scenario sweep of 1,000 synthetic inbound cases across six operational regimes, monotonicity and sanity checks, and ablation and baseline comparisons. The scenario sweep produced mean readiness 47.4, median readiness 50.6, 370 Critical cases, 284 Priority cases, and 188 Routine cases. Ablations show that removing maintenance influence increased mean readiness by 13.7 points, while removing bottleneck influence increased mean readiness by 10.0 points, indicating that the fusion layer materially changes prioritization under controlled stress conditions. Two maintenance-related monotonicity checks did not strictly pass because small local readiness increases occurred at low malfunction levels before urgency thresholds changed; these failures are treated as limitations of the current model-fusion interaction rather than hidden. The work contributes a reproducible OCC decision-support architecture and scenario-evaluation package, while explicitly preserving the limitations that no airline operational data, OCC user study, deployment, certification, or measured operational savings are claimed.
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BibTeX
@article{purohit2026prearrival,
title = {{Pre-arrival OCC decision support for aircraft turnaround coordination: a human-in-the-loop architecture and scenario-based evaluation}},
author = {Parth Yogeshbhai Purohit and Thomas Feuerle and Peter Hecker},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1977034},
url = {https://europepmc.org/article/PMC/PMC13724608},
note = {CC BY},
}
RIS
TY - JOUR
TI - Pre-arrival OCC decision support for aircraft turnaround coordination: a human-in-the-loop architecture and scenario-based evaluation
AU - Parth Yogeshbhai Purohit
AU - Thomas Feuerle
AU - Peter Hecker
PY - 2026
DA - 2026/10/19
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1977034
UR - https://europepmc.org/article/PMC/PMC13724608
AB - Aircraft turnaround coordination depends on maintenance, catering, passenger-assistance, gate, and bottleneck information that may become available before arrival but is often handled through fragmented workflows. This paper presents a human-in-the-loop Operations Control Center (OCC) decision-support architecture for pre-arrival aircraft turnaround coordination. The architecture integrates component-aware maintenance risk indicators, service-area inventory readiness, process-class bottleneck forecasting, passenger-assistance readiness logic, and a transparent rule-based fusion layer that converts heterogeneous signals into readiness scores, alert levels, and operator-facing action queues. The evaluation is deliberately framed as controlled synthetic scenario analysis rather than airline deployment validation. Existing module-level holdout results are combined with a deterministic scenario sweep of 1,000 synthetic inbound cases across six operational regimes, monotonicity and sanity checks, and ablation and baseline comparisons. The scenario sweep produced mean readiness 47.4, median readiness 50.6, 370 Critical cases, 284 Priority cases, and 188 Routine cases. Ablations show that removing maintenance influence increased mean readiness by 13.7 points, while removing bottleneck influence increased mean readiness by 10.0 points, indicating that the fusion layer materially changes prioritization under controlled stress conditions. Two maintenance-related monotonicity checks did not strictly pass because small local readiness increases occurred at low malfunction levels before urgency thresholds changed; these failures are treated as limitations of the current model-fusion interaction rather than hidden. The work contributes a reproducible OCC decision-support architecture and scenario-evaluation package, while explicitly preserving the limitations that no airline operational data, OCC user study, deployment, certification, or measured operational savings are claimed.
SN - 2624-8212
ER -