Marvelocity Pdf Guide

\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).

\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}

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

\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}

\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 $V_{\text{HM}}$. Liu}$^{1}$, \textbf{Rahul K

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

\section{Discussion} \label{sec:discussion} \subsection{Interpretability} Feature importance (gain) indicates that $V_{\text{HM}}$ accounts for 38 \% of the model’s predictive power, confirming that the physics‑based backbone remains dominant. The top three environmental variables are wind speed, wave height, and current speed, aligning with maritime operational experience. and current speed

\subsection{Learning the Residual} Define the residual speed: \begin{equation} \Delta V = V_{\text{SOG}} - V_{\text{HM}}, \end{equation} where $V_{\text{SOG}}$ is the measured speed over ground from AIS. We train a Gradient‑Boosted Regression Tree (XGBoost \cite{Chen2016}) to predict $\Delta V$ from the feature vector $\mathbf{x}$: \[ \mathbf{x} = \bigl[\,\underbrace{L, B, D, C_B}_{\text{design}};\, \underbrace{V_{\text{HM}}}_{\text{baseline}};\, \underbrace{U_{10}, \theta_{\text{wind}}}_{\text{wind}};\, \underbrace{H_s, \theta_{\text{wave}}}_{\text{wave}};\, \underbrace{U_c, \theta_{\text{current}}}_{\text{current}}\,\bigr]. \]