The present Deliverable (D6.1) describes the identification and evaluation of vehicle related risk factors. It outlines the results of Task 6.1 of Work Package 6 (WP6) of SafetyCube, which aimed to identify and evaluate vehicle related risk factors and related road safety problems by (i) presenting a taxonomy of vehicle related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To reach this objective, Task 6.1 has initially exploited current knowledge (e.g. existing studies) and existing accident data (macroscopic and in-depth) in order to quantify scenarios (defined in Work Package 8) related to the vehicle element. This information will help further on in WP6 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. The identification of a comprehensive taxonomy of vehicle-related risks has been a challenge by itself. Most of the studied risk factors are related to the human behaviour and it is often difficult to dissociate the driver from their vehicle in the literature. Nevertheless, a specific taxonomy has been identified, based on expertise and some well-known issues. Because every vehicle type has its own characteristics (size, weight, agility …), different uses, and moves on different types of infrastructure (roadway, sidewalk, path …), the first level of this taxonomy has been established from various types of road users, i.e. vehicles, pedestrians and cyclists. The second level has been based primarily on each of these road user groups, while still trying to have some common main characteristics. To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP6 has applied this methodology to vehicle risk factors. This uniformed approach facilitated systematic searching of the scientific literature, and consistent evaluation of the evidence, for each risk factor whatever the observed point of view (human, infrastructure or vehicle). The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main database used in the WP6 literature search was Scopus, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance etc.). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included vehicle types, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source of the Decision Support System (DSS) being developed for SafetyCube. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible). Each synopsis consists of three sections: a two page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects), and finally supporting documents ( details of literature search and comparison of available studies in detail, if relevant) To enrich the background information (scenarios and general characteristics), injury accident data from a number of sources across Europe (i.e. LAB, BAAC and CARE/CADaS) was used. After undertaking the literature search and coding of the studies, it was found that for some risk factors not enough detailed studies could be found to allow a synopsis to be written. These risk factors will not be displayed in the DSS. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety, defined in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, 14 risk factors were given a Red code (e.g. risk in frontal impact, side impact or rollover and compatibility for passenger cars), 11 were given a Yellow code (e.g. Vehicle design or Low NCAP rating for pedestrians), and 11 were given a Grey code (e.g. Crash or vehicle data for trucks, crash characteristics for PTW.). Some limitations were identified, mainly due to difficulties of finding relevant published studies. It was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP6 is to begin identifying measures that will counter the identified risk factors. Most of the vehicle safety systems are oriented to passive safety to fit with this taxonomy (the target of the passive safety systems being to avoid or to mitigate injuries, up to a limited level of crash intensity). However, ADAS and active safety are built essentially to avoid accident configurations (not directly the causes or the risk having produced the situation) or to decrease the intensity of the crash. (Author/publisher)
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