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• Methodology identifies surrogate suitability at different road geometries. • Original review of SCE identified by kinematic triggers in NDS. • Identifying collision type manifestation within geometry and geometry subtypes. • Several important future works have been identified for surrogate indicators. Advancements in data collection and processing methods have produced large databases containing high quality vehicular data. Despite this, conventional vehicle-vehicle collisions remain difficult to identify due to their rarity. Therefore, there is a need to identify potential collisions given the introduction of these new data collection methods. Surrogate indicators are a popular methods utilised to identify such events, however, the type of surrogate that can be used depends heavily on the type of data collection method. Though most surrogate indicators are used at different road geometries, there is evidence to suggest that some surrogate indicators may perform better than others at a given geometry. This review provides two key contributions to the body of literature. Firstly, a review of kinematic surrogates is put forward, along with a discussion on the whether these surrogates can be contextualised at different road geometries. Secondly, an extensive analysis and discussion of observer-based and video processed surrogate indicators, the collision types they aim to identify and the geometries they have been used at previously were analysed and advantages and disadvantages of the surrogates have been presented for future use. To do this, intersections, highways and roundabouts were selected and divided into geometry subtypes (i.e. three-legged and four-legged intersection) and segments (i.e. approaches to intersections and internal to the intersection) based on the likelihood of crash types and pre-crash manoeuvres occurring in that segment. Due to the lack of research around the use of kinematic triggers at road geometries, it is difficult to advocate for the use of any given trigger over another at a given geometry. Furthermore, it was found that kinematic triggers cannot accurately identify conflicts from naturalistic driving data and require the use of advanced statistical techniques such as machine learning to increase accuracy. A brief analysis of threshold identification techniques was also performed. Several future works have been put forward including the introduction of surrogates which capture conflict severity and the role of surrogate indicators in connected and automated vehicle environments. [ABSTRACT FROM AUTHOR]