deletions | additions       diff --git a/Methodolofy Measurement Definitions.tex b/Methodolofy Measurement Definitions.tex  index a4061a6..3eb1d38 100644  --- a/Methodolofy Measurement Definitions.tex  +++ b/Methodolofy Measurement Definitions.tex 
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One solution is to perform a triage of user pairs based on physical access and proximity. A network topology coupled with a driving distance horizon is proposed. This is not a perfect solution, however, as physical access isn't necessarily a binary option. In our median example, it is still physically possible, although less likely, for vehicles to cross-over into an opposing lane and cause a collision, although this is something that could be modeled.  \subsubsection{Motion Prediction}  \label{motion-prediction}  While vehicle trajectories offer a rich set of observed behavioural data, they do not provide much collision data; this is by design of the proactive road safety approach: predicting collisions should be performed without observing them directly. In order to study collisions, they need to be extrapolated from traffic events with potential for collision. This potential is modeled by predicting future positions of vehicles using motion prediction at every instant in time and examining i) situations of particular risk (i.e. threshold) or ii) evolution of the risk. Several motion prediction models are proposed for study: