\title{MarVelocity:\\A Data‑Driven Metric for Predicting Maritime Vessel Speed} \author{ \textbf{Alexandra T. Liu}$^{1}$, \textbf{Rahul K. Menon}$^{2}$, \textbf{Elena G. Petrova}$^{3}$\\[2mm] $^{1}$Department of Naval Architecture, Massachusetts Institute of Technology, Cambridge, MA, USA\\ $^{2}$Marine Systems Research Group, Indian Institute of Technology, Bombay, India\\ $^{3}$Institute of Ocean Engineering, Technical University of Munich, Munich, Germany\\[2mm] \texttt{atl@mit.edu, rkm@iitb.ac.in, elena.petrova@tum.de} } \date{\today}
\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} marvelocity pdf
\subsection{Hybrid Strategies} Hybrid schemes—e.g., residual learning on top of HM \cite{Zhang2023}—have shown promise but often require vessel‑specific fine‑tuning. MarVelocity differentiates itself by learning a **universal correction** that transfers across ship types. Such models neglect the complex
Copy the code into a file named marvelocity.tex , run pdflatex (or your favourite LaTeX engine) and you will obtain a nicely formatted PDF that you can submit to a conference or journal. \documentclass[letterpaper,10pt]{article} \usepackage[margin=1in]{geometry} \usepackage{times} \usepackage{graphicx} \usepackage{amsmath,amssymb} \usepackage{hyperref} \usepackage{booktabs} \usepackage{multirow} \usepackage{siunitx} \usepackage{float} \usepackage{enumitem} \usepackage[backend=biber,style=ieee]{biblatex} \addbibresource{marvelocity.bib} nonlinear interaction among wind
\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.