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\section{Introduction}  Affordable computing and flexible and inexpensive sensor technology are transforming the current practice and methods for traffic data collection, monitoring and analysis: big data is changing how we interact with our environment and how we  approach problem solving tasks in the field of transportation. This should come to no surprise as urban mobility complexity and pervasiveness in daily life lends itself naturally to large data sets. amounts of data.  In this context, the use of mobile and/or fixed video sensors for traffic monitoring and data collection is becoming a common practice not only for freeways but also for urban streets. This opens up possibilities for more dynamic traffic load balancing and congestion easing of road networks and in return provides researchers with participatory network usage data collection. This new situation in which traffic data is being collected intensively demands more intelligent and advanced methods for traffic data analysis; it is then not surprising that computer vision techniques have gained popularity given the potential of transforming the existing CCTV surveillance infrastructure into a highly detailed traffic data collection tool to identify and study traffic behaviours. 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 (years of crash data): 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 {\textquotedblleft}near-miss{\textquotedblright}. 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}.