Marvelocity — Pdf

\section{Conclusion} \label{sec:conclusion} We presented **MarVelocity**, a hybrid metric that blends classical hydrodynamic resistance modelling with a universal machine‑

The final **MarVelocity** prediction is: \begin{equation} V_{\text{MarV}} = V_{\text{HM}} + \hat{\Delta V}(\mathbf{x}). \end{equation}

\newpage \section{Introduction} \label{sec:intro} The global shipping industry transports over \SI{80}{\percent} of world trade by volume \cite{UNCTAD2022}. Despite advances in hull design and propulsion, a substantial fraction of fuel burn is attributable to sub‑optimal speed choices driven by inaccurate speed forecasts \cite{Mitsui2019}. Conventional approaches—e.g., the Holtrop–Mennen method \cite{Holtrop1972} or the ITTC‑1998 friction line \cite{ITTC1998}—rely on static ship parameters and simplified sea‑state corrections. Such models neglect the complex, nonlinear interaction among wind, waves, currents, and ship trim. marvelocity pdf

\subsection{Baseline Physical Model} We compute the **theoretical speed over ground** $V_{\text{HM}}$ by solving for the equilibrium of propulsive thrust $T$ and total resistance $R_{\text{HM}}$: \begin{equation} R_{\text{HM}}(V) = R_f(V) + R_r(V) + R_a(V) + R_w(V) \,, \end{equation} where $R_f$, $R_r$, $R_a$, and $R_w$ denote frictional, residual, air, and wave resistance respectively (see Holtrop–Mennen \cite{Holtrop1972} for detailed expressions). The thrust is estimated from the ship’s installed power $P$ and propeller efficiency $\eta_p$: \begin{equation} T(V) = \frac{\eta_p P}{V}. \end{equation} The root of $T(V)-R_{\text{HM}}(V)=0$ yields $V_{\text{HM}}$.

\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. Conventional approaches—e

\bigskip \noindent\textbf{Keywords:} maritime speed prediction, AIS data, hydrodynamic resistance, machine learning, fuel efficiency, autonomous vessels

\begin{document} \maketitle \thispagestyle{empty} \begin{abstract} Accurate estimation of a vessel’s speed under varying environmental and operational conditions remains a cornerstone of maritime safety, fuel‑efficiency optimisation, and autonomous navigation. We introduce **MarVelocity**, a novel composite metric that fuses physical‑based hydrodynamic modelling with machine‑learning‑derived correction terms. Using a curated dataset of \num{2.3} million AIS (Automatic Identification System) records combined with high‑resolution oceanographic reanalysis, we train Gradient‑Boosted Regression Trees (GBRT) to predict the \emph{effective speed over ground} (SOG) from a low‑dimensional set of inputs: vessel design parameters, draft, wind, wave, and current vectors. MarVelocity achieves a mean absolute error of \SI{0.12}{\knot} (≈ 3 \% relative) on held‑out test ships, outperforming traditional empirical resistance formulas by a factor of 2.3. We further demonstrate real‑time deployment on a fleet of 150 container ships, reporting a 4.8 \% reduction in fuel consumption over a six‑month trial. The metric is released as an open‑source Python package \texttt{marvelocity} (v1.2) together with reproducible notebooks. \end{abstract} The thrust is estimated from the ship’s installed

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}