Marvelocity Pdf __link__ May 2026

\subsection{Limitations} \begin{itemize} \item \textbf{Data sparsity in polar regions}: AIS coverage is lower, leading to higher uncertainties. \item \textbf{Propeller efficiency assumption}: We treat $\eta_p$ as a constant; future work will embed a learnable efficiency model. \item \textbf{Real‑time constraints}: While inference is sub‑millisecond, integrating high‑resolution forecasts (e.g., ECMWF) adds latency; edge‑computing strategies are under investigation. \end{itemize}

\subsection{Fuel‑Efficiency Gains} A six‑month field trial (January–June 2025) on a fleet of 150 container ships employed MarVelocity to compute \emph{optimal speed profiles} under real‑time weather forecasts. Compared with the baseline speed‑keeping policy, the fleet realized an average fuel reduction of **4.8 \%** (≈ 1.9 million kg CO\textsubscript{2} avoided).

\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. marvelocity pdf

\section{Results} \label{sec:results} \subsection{Prediction Accuracy} Table~\ref{tab:accuracy} summarizes error metrics on the held‑out test fleet (150 vessels, 1.1 M observations).

\bigskip \noindent\textbf{Keywords:} maritime speed prediction, AIS data, hydrodynamic resistance, machine learning, fuel efficiency, autonomous vessels Their primary limitation is the assumption of steady,

\section{Methodology} \label{sec:method} \subsection{Data Acquisition} \begin{itemize} \item \textbf{AIS}: 2.3 M messages (2018–2023) from the Global Fishing Watch and MarineTraffic APIs. \item \textbf{Oceanographic Reanalysis}: ERA5 \cite{Hersbach2020} providing 10‑m wind vectors, significant wave height, and surface currents at 0.25° resolution. \item \textbf{Ship Catalog}: Technical specifications (length overall, beam, draft, block coefficient, engine power) extracted from the Lloyd’s Register database. \end{itemize} All timestamps are aligned to UTC and interpolated to a 10‑minute cadence.

\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). ship‑agnostic evaluation pipeline

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}