deletions | additions       diff --git a/Introduction Road Safety.tex b/Introduction Road Safety.tex  index 7219a12..90e4503 100644  --- a/Introduction Road Safety.tex  +++ b/Introduction Road Safety.tex 
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This high-resolution data permits the measurement of precisely defined instantaneous surrogate safety measures identifying collision risk. 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 defined. However, this measure relies on motion prediction hypotheses to identify collision courses. The traditional approach is to use constant velocity projection (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.  More advanced collision course modelling efforts are underway, including motion patterns which represent naturalistic (expected) driving behaviour learnt from the same data set. This procedure provides several potential collision points and their probability as a function of both the characteristics of the specific site and the behaviour of the road users. The motion patterns, or the distribution of trajectories at a site and their probabilities, may be described discretely over time and space \cite{St_Aubin_2014} or with prototype trajectories[CITE]. trajectories [CITE].  The motion and collision predictions are computationally  intensive as they explore, for each pair of road users, at each point in time, all future positions in time and space (typically subject to a time horizon). Furthermore, interaction complexity and exposure increases exponentially with increases in traffic flow, as the number of potential interactions is proportionnal to simultaneous road users in a scene. For example, over  the square course  of the number one day, a typical intersection can experience between 100 thousands and 100 millions  of road users simultaneously going through these instantaneous interactions, depending on  the intersection. intersection complexity.  Over This data-driven methodology is applied to a large video dataset collected at more than 20 roundabouts in Quebéc to study road user behaviour and their safety. Camera views record data at more than 40 roundabout weaving zones, an area within the roundabout delimited by an entry and  the course next following exit. Each camera records 12 to 16 h  of one video on a given work  day, which constitutes  a typical intersection can experience between 100 thousands and 100 millions dataset  of these instantaneous interactions, depending on over 600 hours of video data. Applying  the intersection complexity. proposed method to this large dataset yields considerable amounts of indicators, from individual road user measurements, e.g. speed, to individual interaction measurements, e.g. TTC, to aggregated indicators per road user or interaction, to aggregated indicators per site over time and space.  This data-driven methodology is applied to a large video dataset collected at more than 20 roundabouts in Quebec to study road user behaviour and their safety. More than 40 camera views define roundabout sub-regions delimited by an entry and the following exit, constituting a weaving zone with the vehicles already within the roundabout. Each camera recorded 12 to 16 h of video on a given day, which constitutes a dataset with 600 h of video data. Applying the proposed method to this large dataset yields considerable amounts of indicators, from individual road user measurements, e.g. speed, to individual interaction measurements, e.g. TTC, to aggregated indicators per road user or interaction, to aggregated indicators per site over time and space. Analyzing such big data is a challenge of a magnitude that has never been undertaken before in driver behaviour and road safety research. It holds the key to understanding the processes that lead road user users  to collide, and  to design and validate safety indicators that do not requireto wait for  accidents to occur. The approach will be demonstrated on this video dataset to identify roundabout characteristics that influence road \subsection{Motivation}  \subsubsection{Size of data (hours, GB, framerate, resolution)}  Technology availability (cameras along MTQ network), data type, high-resolution, microsopic data.   Needs of traffic safety anayslis. safety.