deletions | additions       diff --git a/Methodology Complimentary Data.tex b/Methodology Complimentary Data.tex  index 0875b30..abd71a8 100644  --- a/Methodology Complimentary Data.tex  +++ b/Methodology Complimentary Data.tex 
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\begin{equation} \label{eqn:coordinate-transform} (x,y)\to(S,\gamma). \end{equation}  where a point located at ($x$,$y$) in cartesian space is snapped to the nearest position on the nearest alignment, and is represented by the curvilinear distance $S$ along this alignment from its begining and the offset \( \gammma \), orthogonal to this alignment, measuring the distance between the original point and it's snapped position. These coordinates are useful for measuring following behaviour, lane changes, and lane deflection.  Many approaches exist to trajectory clustering, some are produced manually, some are learned automatically. Manual trajectory clustering is labour intensive and potentially a source of bias, but allows for tight control of scenery description and analysis oversight. Larned clustering is systematic but naive as this form of clustering can only make use of trajectory data to infer position. The methodology is primarily based in manual trajectory clustering (called alignments), although a hybrid approach, which refines spatial positioning of manually defined alignments through traditional clustering approaches, is proposed for future work.