deletions | additions       diff --git a/Introduction Road Safety.tex b/Introduction Road Safety.tex  index 0e768d9..8c5fda5 100644  --- a/Introduction Road Safety.tex  +++ b/Introduction Road Safety.tex 
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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 \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 \cite{Saunier_2010}  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}. \cite{Jackson_2013}.  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. This high-resolution data permits the measurement of precisely defined instantaneous surrogate safety measures identifying collision probability. One such measure is time-to-collision (TTC) which measures the time remaining at any given instant to some collision point in the future defined by a collision course with another road user. This measure is useful as it provides the remaining time road users have to react to and avoid potential collisions. Higher TTCs are generally considered safer, though the precise link has yet to be validated. However, this measure relies on motion prediction hypotheses to identify collision courses. The traditional approach is to use constant velocity projection \cite{Amundsen_1977} \cite{Laureshyn_2010} (situations in which road users fail to correct their course), which is the motion prediction method most frequently used, without a justification. This approach does not natively provide a collision course probability, and it will not be suitable in situations where observed trajectories do no include constant velocity displacements: for example, turning lanes in an intersection and movements in a roundabout.