\documentclass[11pt]{article} \usepackage[english]{babel} \usepackage{a4wide} \usepackage[utf8]{inputenc} \usepackage{natbib} \usepackage{algorithm} \usepackage{graphicx} \usepackage[noend]{algpseudocode} \usepackage{enumitem} \usepackage{underscore} \setlist{itemsep=0.1em} \usepackage{hyperref} \usepackage{xcolor} \definecolor{dark-red}{rgb}{0.4,0.15,0.15} \definecolor{dark-blue}{rgb}{0.15,0.15,0.4} \definecolor{medium-blue}{rgb}{0,0,0.5} \hypersetup{ colorlinks, linkcolor={dark-blue}, citecolor={dark-blue}, urlcolor={medium-blue} } \setcounter{tocdepth}{2} \bibliographystyle{agsm} \begin{document} \title{Long Line Document} \author{Jonathan Mackenzie} \maketitle \section{Research Proposal} I propose to apply stream data mining techniques to the problem of automated incident detection (AID) in arterial road networks. In particular, I will explore the use of clustering and classification techniques to stream data in order to detect anomalous traffic behaviour (that would indicate and incident on the road). The data is collected by loop detectors beneath the road of signalised traffic intersection in the Adelaide metropolitan area. Initially I will test on historical data and eventually develop a prototype system for detecting and reporting incidents on real-world live data. \end{document}