A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic

TL;DR

This study develops a two-stage machine learning framework using gradient boosting to analyze vessel speed in the Arctic, integrating environmental data to improve understanding of navigational conditions.

Contribution

The paper introduces a novel two-stage gradient boosted decision tree approach with random effects for modeling vessel speed, accounting for zero and positive speeds separately in Arctic conditions.

Findings

Model achieved AUC = 0.85 for positive SOG classification.

Explained 77% of variance in vessel speed with the conditional model.

Distance to coast and bathymetric depth are key determinants of vessel speed.

Abstract

Understanding how environmental and operational conditions influence vessel speed is crucial for characterizing navigational conditions in the Arctic. We analyzed Automatic Identification System (AIS) data from 2010-2019 to examine vessel speed over ground (SOG). Over half of the AIS records showed zero SOG, and treating zero and positive SOG as a single continuous process can obscure important patterns. We therefore applied a two-stage machine learning framework, first modeling the probability of SOG greater than zero and then modeling SOG conditional on being positive. AIS observations were integrated with sea ice concentration, course over ground, wind, bathymetric depth, distance to coast, vessel group, and navigational status. Gradient boosted decision trees with random effects captured nonlinear environmental responses while accounting for repeated observations. The positive SOG classifier achieved strong discrimination (AUC = 0.85), while the conditional speed model explained approximately 77 percent of out-of-fold variance. SHAP values quantified covariate effects by decomposing model predictions into additive contributions from individual variables. Distance to coast and bathymetric depth were dominant determinants of both the likelihood and magnitude of vessel speed, while changes in course, vessel group, and navigational status introduced secondary variation. Wind and sea ice effects were modest. Together, these results empirically characterize Arctic vessel operating regimes relevant to speed management and corridor-level assessment.