deletions | additions       diff --git a/Introduction Road Safety.tex b/Introduction Road Safety.tex  index 71d3472..0e768d9 100644  --- a/Introduction Road Safety.tex  +++ b/Introduction Road Safety.tex 
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\subsection{Road Safety}  One such behavioural study application is in proactive road safety diagnosis. This has been a long-standing goal in the field of transportation safety. Traditional statistical methods applied to accident data require long observation periods, are expensive to organize and come at a price to society: one must wait for (enough) accidents to occur. Beginning in the 1960s, attempts were made to predict collision rates based on observations without a collision rather than historical accident records \cite{Perkins_1968}: these methods are now termed surrogate safety methods. The traffic conflict technique was one of the earliest methods proposed which entailed the observation of quasi-collision events: situations in which road users were exposed to some recognizable risk (probability) of collision, e.g. a “near-miss”. However, several problems limited their adoption: the manual data collection method is costly and may not be reliable, and  the definition and objective measurement of these events are difficult. difficult \cite{Chin_1997}.  Today, with technological improvements in computing power, data storage, sensor technologies, and advances in artificial intelligence, these issues are quickly being addressed. This research presents the application of a video-based automated trajectory analysis solution which combines the latest advances in high-resolution traffic data acquisition and machine learning methods to model and predict collision potential from relatively short, but extremely rich traffic data. This data is typically obtained from ordinary video data via computer vision from a camera situated at 10 m or more above the roadway \cite{Saunier_2010}. This trajectory data consists of position and velocity measurements of road users captured 15 to 30 times per second to a relatively high degree of accuracy. This amounts to several million individual instantaneous measurements over the period of one day at a typical site.