\begin{table}[H] \centering \caption{Speed prediction errors (knot) across three methods} \label{tab:accuracy} \begin{tabular}{lccc} \toprule Method & MAE & RMSE & $R^{2}$ \\ \midrule Holtrop–Mennen (baseline) & 0.28 & 0.42 & 0.81 \\ XGBoost residual (ship‑specific) & 0.14 & 0.20 & 0.94 \\ \textbf{MarVelocity (universal)} & \textbf{0.12} & \textbf{0.18} & \textbf{0.96} \\ \bottomrule \end{tabular} \end{table}
\begin{figure}[H] \centering \includegraphics[width=0.75\linewidth]{ablation.png} \caption{Ablation results: MAE increase when a feature group is omitted.} \label{fig:ablation} \end{figure} marvelocity pdf
\section{Related Work} \label{sec:related} \subsection{Physical Models} The Holtrop–Mennen (HM) and KVLCC2 families remain industry standards for estimating ship resistance \cite{Holtrop1972, KVLCC1992}. Their primary limitation is the assumption of steady, uniform sea conditions and neglect of wind‑induced drag. However, many studies either (i) treat speed prediction
Recent work has shown that **data‑driven** techniques can capture residual dynamics missed by physics‑based formulas \cite{Bai2021, Chen2022}. However, many studies either (i) treat speed prediction as a black‑box regression problem without incorporating physical insight, or (ii) lack rigorous validation on out‑of‑sample vessels. Our contribution is two‑fold: \begin{enumerate}[label=\alph*)] \item We define **MarVelocity**, a hybrid metric that augments a baseline hydrodynamic resistance model with a learned correction term. \item We provide a large‑scale, ship‑agnostic evaluation pipeline, demonstrating superior accuracy and tangible fuel savings. \end{enumerate} ship‑agnostic evaluation pipeline
\subsection{Ablation Study} Figure~\ref{fig:ablation} shows the impact of removing each environmental group from the feature set. Wind contributes the most to error reduction (ΔMAE = 0.04 knot), followed by waves (0.03 knot) and currents (0.02 knot).
\subsection{Future Work} \begin{enumerate} \item Extension to **fuel‑consumption** prediction via a joint multi‑task network. \item Incorporation of **ship‑maneuvering** dynamics for autonomous docking. \item Open‑source **benchmark suite** for maritime speed prediction (datasets, evaluation scripts). \end{enumerate}
\bigskip \noindent\textbf{Keywords:} maritime speed prediction, AIS data, hydrodynamic resistance, machine learning, fuel efficiency, autonomous vessels