Abstract
Ground handling operations at commercial airports remain one of the least predictable segments of the air
transport value chain, and baggage offload and onward transit represent a particularly volatile component of the
ramp process. This research article examines how probabilistic machine learning models, including Gaussian
process regression, Bayesian neural networks, quantile regression forests, hidden Markov models, and survival
analysis frameworks, can be applied to estimate the offload duration and downstream transit time of checked
baggage from aircraft arrival to sortation, transfer, or reclaim availability. Unlike deterministic point estimate
models, probabilistic approaches produce full predictive distributions that quantify the uncertainty inherent in
ramp operations, allowing ground handling planners, baggage system operators, and airline operations control
centers to make risk informed decisions rather than relying on single average values that routinely fail during
irregular operations.
The article synthesizes operational data characteristics, feature engineering strategies, and model architectures
suited to the short haul, high frequency nature of ramp events. It presents an illustrative simulation study
demonstrating how calibrated uncertainty intervals shrink misconnection risk and improve resource allocation for
tug drivers, belt loaders, and baggage handling system capacity. The findings indicate that probabilistic models,
when properly calibrated, can reduce short connection misconnection exposure, improve staffing precision during
irregular operations, and provide a quantitative basis for minimum connection time policy review at hub airports.
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