Review and ranking of crash risk factors related to the road infrastructure

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as “hot topics” of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings - and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 ‘Synopses’ (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors)

SaferWheels study on powered two-wheeler and bicycle accidents in the EU - Final report

Road Safety remains a major societal issue within the European Union. In 2014, some 26,000 people died and more than 203,500 were seriously injured on the roads of Europe, i.e. the equivalent of a medium town. However, although there are variations between Member States, road fatalities have been falling throughout the EU. Over the last 20 years, most Member States have achieved an overall reduction, some more than 50%. During this period, research on road safety and accident prevention has predominantly focused on protecting car occupants, with significant results. However, at the same time the number of fatalities and injuries among other categories of road users has not fallen to the same extent, indeed, in some cases, they have risen. The “Vulnerable Road Users” (VRUs) in particular are a priority and represent a real challenge for researchers working on road safety and accident prevention. Accidents involving VRUs comprised approximately 48% of all fatalities in the EU during 2014, with Powered Two-Wheelers (PTWs) comprising 18% and cyclists comprising 8% of the total numbers of fatalities. The Commission adopted in July 2010 its Policy Orientations on Road Safety for 2010-2020. One of the strategic objectifies identified by the Commission is to improve the safety of Vulnerable Road Users. With this category of road users, motorcycle and moped users require specific attention given the trend in the number of accidents involving them and their important share of fatalities and serious injuries. The SaferWheels study was therefore conducted to investigate accident causation for traffic accidents involving powered two-wheelers and bicycles in the European Union. The objective of the study was to gather PTW and bicycle accident data from in-depth crash investigations, obtain accident causation and medical data for those crashes, and to store the information according to an appropriate and efficient protocol enabling a causation-oriented analysis. The expected outcomes were: - Collection of accident data for at least 500 accidents of which approximately 80% would involve Powered Two–Wheelers and the remainder bicycles. Equal numbers of cases were to be gathered in six countries; France, Greece, Italy, the Netherlands, Poland and the UK. - In-depth investigation and reporting for each of the accidents on the basis of the data collected. - Description of the main accident typologies and accident factors. - Proposal of most cost-effective measures to prevent PTW and bicycle accidents

Economic evaluation of road user related measures. Deliverable 4.3 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the third deliverable (4.3) of work package 4, which is dedicated to the economic evaluation - mainly by means of a cost-benefit analysis - of road user related safety measures [...continues]

The European road safety decision support system. A clearinghouse of road safety risks and measures, Deliverable 8.3 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS) that will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures and cost-effective approaches to reduce casualties of all road user types and all severities. The core of the SafetyCube project is a comprehensive analysis of accident risks and the effectiveness and cost-benefit of safety measures, focusing on road users, infrastructure, vehicles and post-impace care, framed within a Safe System approach ,with road safety stakeholders at the national level, EU and beyond having involvement at all stages. The present Deliverable (8.3) outlines the methods and outputs of SafetyCube Task 8.3 - ‘Decision Support System of road safety risks and measures’. A Glossary of the SafetyCube DSS is available to the Appendix of this report. The identification and assessment of user needs for a road safety DSS was conducted on the basis of a broad stakeholders’ consultation. Dedicated stakeholder workshops yielded comments and input on the SafetyCube methodology, the structure of the DSS and identification of road safety "hot topics" for human behaviour, infrastructure and vehicles. Additionally, a review of existing decision support systems, was carried out; their functions and contents were assessed, indicating that despite their usefulness they are of relatively narrow scope.... continue

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure 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 help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. 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 databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. 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). 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 road system element, 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 (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. 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) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 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 (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. 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. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. 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 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, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

Identification of road user related risk factors, deliverable 4.1 of the H2020 project SafetyCube.

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out: Identification of human related risk factors – creation of a taxonomy Consultation of relevant stakeholders and policy papers for identification of topic with high priority (‘hot topics’) Systematic literature search and selection of relevant studies on identified risk factors •Coding of studies •Analysis of risk factors on basis of coded studies •Synopses of risk factors, including accident scenarios The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently. It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring. The following analysed risk factors were assessed as ‘risky’, ‘probably risky’ or ‘unclear’. No risk factors were identified as ‘probably not risky’. Risky Probably risky Unclear • Influenced driving – alcohol • Influenced Driving – drugs (legal & illegal) • Speeding and inappropriate speed • Traffic rule violations – red light running • Distraction – cell phone use (hand held) • Distraction – cell phone use (hands free) • Distraction – cell phone use (texting) • Fatigue – sleep disorders – sleep apnea • Risk taking – overtaking • Risk taking – close following behaviour • Insufficient knowledge and skills • Functional impairment – cognitive impairment • Functional impairment – vision loss • Diseases and disorders – diabetes • Personal factors – sensation seeking • Personal factors – ADHD • Emotions – anger, aggression • Fatigue – Not enough sleep/driving while tired • Distraction – conversation with passengers • Distraction – outside of vehicle • Distraction – cognitive overload and inattention • Functional impairment – hearing loss (few studies) • Observation errors (few studies) • Distraction – music – entertainment systems (many studies, mixed results) • Distraction – operating devices (many studies, mixed results) The next step in SafetyCube’s WP4 is to identify and assess the effectiveness of measures and to establish a link to the identified risk factors. The work of this first task indicates a set of risk factors that should be centre of attention when identifying corresponding road safety measures (category ‘risky’)

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCube

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCub

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub

A preliminary analysis of in‐depth accident data for powered two‐wheelers and bicycles in Europe

Despite progress from scientific and technological advancements, road safety remains a major issue worldwide. Road accident impacts such as fatalities, injuries and property damage consist considerable costs borne not only by involved people but society as well. This study aims to present preliminary findings of in‐depth accident analysis for two‐wheelers (bicycles and powered two wheelers – PTWs) across six countries in Europe. Data regarding the conditions underlying accident occurrence are presented, including time and date, weather, vehicle and road conditions and rider‐related parameters such as age, intoxication and use of protective equipment. In addition, a Two Step Cluster Analysis is implemented in order to explore any possible classification of the analysed cases. It appears that two clusters are formed: the first includes more favourable conditions (“no wind, no drugs, good lighting”) while the second consists of less favourable conditions for road safety (“windy, lighting, unknown DUI condition”). This hints at a meaningful separation of the examination of two‐wheeler accidents when the influence of outside factors is considerable. The inclusion of different but representative areas across Europe offers robustness and transferability to the data and respective results