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]

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

Road Safety Performance Indicators: Theory. Deliverable D3.6 of the EU FP6 project SafetyNet.

This document provides details about the theory behind the development of Safety Performance Indicators (SPIs) in seven major areas which are central to the fields of activity in road safety in Europe. The fields of activity were selected as a result of reviews of national road safety plans in many of the EU countries and around the world and are considered the central themes of activity in road safety, necessary to bring about a significant improvement in road safety in the EU countries. Within each field SPIs were developed which are directly related to that field of activity, can be quantitatively measured, can provide the basis for the assessment of the level of road safety in each country and can serve as an indicator to describe the level of activity in that field and country and can provide a yardstick for comparison. Comparisons can be before and after certain actions are taken or can be comparisons between countries. As stated above, this document deals with the theory behind the development of each of the seven SPIs. It provides the rationale behind their development, the proofs for their relevance in the specific fields and the existing limitations that led to the adoption of the specific SPIs. The document provides also some recommendations for the possible improvements required to obtain better SPIs. Two companion documents are also being prepared. One is a manual which provides details on the procedures necessary to collects the required data for the development of each SPI in each country. The second document provides results on the data collected so far for each of the 25 EU countries and the SPIs developed so far, based on the data submitted by each of the countries. It can be seen that a lot of work still has to be done, both in collecting the necessary data and in improving the SPIs, once better and more detailed data becomes available

Identification and safety effects of road user related measures. Deliverable 4.2 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 second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]

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

Building the European Road Safety Observatory. SafetyNet. Deliverable D3.1 State of the art report on road safety performance indicators

General Road safety can be assessed in terms of the social costs of crashes and injuries. However, simply counting crashes or injuries is an imperfect indicator of the level of road safety. When crashes occur it is the “worst case scenario” of insecure operational conditions of road traffic. Work Package 3 of SafetyNet deals with Safety Performance Indicators (SPIs). A Safety Performance Indicator is any variable, which is used in addition to the figures of crashes or injuries to measure changes in the operational conditions of road traffic. SPIs can give a more complete picture of the level of road safety and can detect the emergence of problems at an early stage, before these problems result in crashes. They use qualitative and quantitative information to help determine a road safety programmes’ success in achieving its objectives. Goal One of the main goals of SafetyNet WP3 is to develop a uniform methodology for measuring a coherent set of safety performance indicators in each of the 25 Member States and some non-EU Members. This report provides the first ideas from the WP3 team on this subject. The SafetyNet team will move on to the other goals (offering technical assistance to some Member States that fail in producing the SPI data according to the developed uniform methodology & collecting current data on SPIs that meet the standards of the uniform methodology) at a later stage in the project

Building the European Road Safety Observatory. SafetyNet. Deliverable D3.7a Road safety performance indicators: country comparisons

This report compares the safety performance of 27 European countries – the25 EU member states, Norway and Switzerland. The comparison is done for seven road safety related areas: alcohol and drugs, speeds, protective systems, daytime running lights, vehicles (passive safety), roads, and trauma management, on basis of the theory presented in Hakkert, Gitelman and Vis1 (2007), using the data obtained from the collaborating countries (see Vis and Van Gent2 (2007). When indicator values are available but not comparable due to e.g. lack of data quality, this is explained. In general, comparing the countries' performances is difficult. The main reasons are the lack of data, suspicious quality of the data, or the incomparability of the (seemingly similar) data due to different circumstances of measurement. As an example of the latter, one might think of speed measurements for different road types in different countries, or on similar road types with completely different characteristics. In a number of cases, the choice for a specific performance indicator depends on the availability of data. This has, for example, been the case for the indicator for alcohol usage; while the optimal indicator would concern the usage rate of alcohol in the general driver population, the unavailability of data in a number of the (larger) country, has led to a more indirect indictor. Details about the development of the safety performance indicators can be found in Hakkert, Gitelman and Vis (2007). In spite of all considerations and limitations, we are able to present a great number of comparisons in this report, or to present the figures that can form the basis for future comparisons. Reliable comparisons are made for the areas daytime running lights, protective systems, vehicles (passive safety), and trauma management. Only limited comparisons are made for the areas speeds and roads. Due to great differences in data quality between the different countries, comparisons in the area alcohol and drugs is not possible. The results for that area are presented for information only and will form the basis for future study

Road Safety Performance Indicators: Manual. Deliverable D3.8 of the EU FP6 project SafetyNet

Safety performance indicators (SPIs) are measures (indicators), reflecting those operational conditions of the road traffic system, which influence the system’s safety performance. Basic features of SPIs are their ability to measure unsafe operational conditions of the road traffic system and their independence from specific safety interventions. SPIs are aimed to serve as assisting tools in assessing the current safety conditions of a road traffic system, monitoring the progress, measuring impacts of various safety interventions, making comparisons, and for other purposes. Seven problem areas in road safety were selected for the development of SPIs in Europe, they are: alcohol and drug-use; speeds; protective systems; daytime running lights; vehicles (passive safety); roads (infrastructure) and the trauma management system. The theory behind the development of SPIs in each of the seven safety areas was presented by Hakkert et al (2007)1. The data obtained from the cooperating countries and the comparisons of safety performance of 27 countries2, in terms of the estimated SPIs, were presented in two other reports3 – Vis and van Gent (2007a), Vis and van Gent (2007b). This report is called a Manual as it should assist the countries in establishing the necessary systems of data collection for producing national SPIs, in each one of the predefined safety fields, and to make them comparable on a European level. For each safety area, the report defines quantitative SPIs, demonstrates existing practices for their measurements, provides best practice examples (when available), and details the procedures which are necessary to collect and process the required data for the estimation of the SPIs' set on a national level. Recognizing the potential for road safety improvements coming from the use of harmonized SPIs across the EU, enabling benchmarking as a proven tool in road safety policy, the Member States are encouraged to seek ways of applying a uniform methodology for producing national SPIs. The procedures and methods presented in the Manual should be treated as minimum quality requirements for producing national SPIs, in each one of the predefined safety fields. In addition, the report provides a more general theoretical background concerning the sampling issues in estimating SPIs (in general and in the context of specific SPI areas). Regarding setting up an SPI survey, the main questions considered are: sampling procedure to obtain a national sample; sampling size; sampling error; stratified sampling (combination into a single SPI by weighting); representativeness of the results and estimating confidence intervals of the SPI values. These issues are discussed in Chapter 2 and in the Statistical Appendix