Car drivers' road safety performance. A benchmark across 32 countries

The road safety performance of a country and the success of policy measures can be measured and monitored in different ways. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure, or road users' behaviour. The last-mentioned can be based on data from roadside surveys or from questionnaire surveys. However, results of such surveys are seldom comparable across countries due to differences in aims, scope, or methodology. This paper is based on the second edition of the E-Survey of Road Users' Attitudes (ESRA), an online survey carried out in 2018, and includes data from more than 35,000 road users across 32 countries. The objective is to present the main results of the ESRA survey regarding the four most important risky driving behaviours in traffic: driving under the influence (alcohol/drugs), speeding, mobile phone use while driving, and fatigued driving. The paper explores several aspects related to these behaviours as car driver, such as the self-declared behaviours, acceptability and risk perception, support for policy measures, and opinions on traffic rules and penalties. Results show that despite the high perception of risk and low acceptability of all the risky driving behaviours analysed, there is still a high percentage of car drivers who engage in risky behaviours in traffic in all the regions analysed. Speeding and the use of a mobile phone while driving were the most frequent self-declared behaviours. On the other hand, driving under the influence of alcohol or drugs was the least declared behaviour. Most respondents support policy measures to restrict risky behaviour in traffic and believe that traffic rules are not being checked regularly enough, and should be stricter. The ESRA survey proved to be a valuable source of information to understand the causes underlying road traffic crashes. It offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety

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 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

Traffic enforcement strategies in The Netherlands : developments in the fields of drinking-and-driving.

The majority of all traffic accidents are caused by human factors (Lewin, 1982; Evans, 1991 ). One of these human factors that is well known to be associated with increased accident risk is the committing of various traffic law violations (Reason et al., 1990, Parker et al. 1995; Zaidel, 2001 ). Probably the best studied and documented is the relationship between driving under the influence of alcohol and accident risk. After consuming one to two glasses of alcoholic beverages, accident risk starts to increase exponentially with every additional glass, resulting in increased risk factors of 4, 6, and 17 for BAG levels of 1, 1.3, and 1.8 g/1 respectively (Borkenstein et al., 1974; Hurst et al., 1994). Beside the increased accident risk alcohol consumption negatively influences injury severity as well. Fatality rates for drivers involved in a traffic accident with a BAG above 1.5 g/1 are reported to be up to 200 times higher than to those of sober drivers (Simpson & Mayhew, 1991 ). Speeding is another traffic violation with ample evidence of its relationship with accident risk and severity. Newtonian physics implies that higher speeds increase crash risk due to reduction of available friction, leading to risk of loss of control, reduction of time-to-collision, and crash forces increasing with the square of speed. On the basis of extensive research on the relationship between speed, speed limits, and accidents, it is estimated that, depending on the road category, a one mile per hour reduction in mean speed of traffic could produce a two to seven percentage reduction in the number of injury accidents (Nilsson, 1990; Finch et al., 1994, Taylor et al., 2000). Other researchers have pointed to the importance of speed variance: vehicles moving much slower or faster than the mean traffic speed tend to be over-represented in accident statistics (Solomon, 1964; Girillo, 1968; Hauer, 1971 ). The rule of thumb that results from various studies is: an average speed increase of 1 km/h means a three percent higher risk of an injury accident (Finch et al., 1994; Taylor et al., 2000). In severe accidents, the increase is even bigger: 1 km/h means a five percent higher risk of serious or fatal injury. The broad concept of 'traffic law enforcement' covers the entire penal procedure designed to persuade road users to obey traffic laws and regulations through the threat of detection of a violation and the imposition of a penalty. Enforcement of traffic laws is intended to influence the behaviour of road users in such a way that their risk of becoming involved in an accident, or causing an accident, decreases. 'Police enforcement' is the actual work of monitoring violations of traffic laws, apprehending offenders, and securing evidence needed for prosecution of offenders. There is good evidence that police enforcement of traffic rules promotes road safety and safe traffic behaviour, and therefore should be considered an integral part of transport management policies (Zaidel, 2002; Zaal, 1994; ETSG, 1999). Police enforcement can only be effective if it operates in a supportive environment of laws, regulations, and a sensitive penal system. These combined forces act to create the deterrence effect of police enforcement, both on the individual level and on society at large (Hakkert, 1994). In this presentation I will describe the Dutch measures and effects in the field of enforcement of drinking-and-driving. First, section 2 provides a description of the monitoring of drinking-and-driving in the Netherlands. Section 3 presents an overview regarding developments in the field of drink-driving in the Netherlands. Section 4 addresses the effort and costs necessary to achieve cost-effective levels of Random Breath Testing in the Netherlands. In section 5 recommendations are given to improve the effectiveness and efficacy of enforcement of drinking and driving

Fietsers.

For 13 percent of the Belgian population the bicycle is the main mode of transportation for daily trips. The most recent internationally comparable statistics on the annual average number of cycling kilometers dates back to 15 years ago. With more than 300 kilometer cycled per person per year, Belgium is situated in the European top 3 with regard to bicycle use. An evaluation of the safety of cycling has to take the higher degree of underregistration of bicycle accidents in comparison to motorized modes of transport into account. The underregistration is partly due to the fact that 87 percent of all hospitalized bicycle victims are due to single vehicle accidents. Taking underregistration into account, the risk to be seriously injured per bicycle kilometer is estimated as 23 times higher than for car drivers. With 28 percent of all serious road traffic injuries in Belgium, cyclcists are overrepresented in the road accident statstics. A comparison by age group shows that senior cyclists have an even more seriously increased risk while cycling. International comparisons reveal that the more bicycles are used in a region, the lower the risk per kilometer cycled becomes. The fatality risk for cyclists, for instance, is about twice as high in Wallonia than in Flanders. At the same time the risk for cyclists in cycling countries like Denmark and The Netherlands is even many more times lower than in Flanders. More research is needed to investigate whether this "safety by numbers" effect is due to a better road infrastructure in cycling countries/regions or to a combination of it with an increased attention for cyclists. International analyses of the causes of bicycle accidents point to the expected factors: the instability that is inherent to two-wheelers which increases the role of the physical condition and the relative vulnerability (cf. seniors) of cyclists, risky driving behaviour and inappropriate attention of cyclists as well as other road users, insufficient segregation of heavy motorized traffic and vulnerable road users and suboptimal speed management. In-depth accident research reveals that the main causes are both situated at the level of the cyclists as at the level of the other road users. An analyses of the Belgian key indicators shows that both the number of cyclist fatalities as the number of lightly and seriously injured cyclists stagnates over the last decade. In the same period, the injury statistics of motorized road users globally decreased. Since the bicycle share in the total number of trips decreased slightly since 2009, this relative deterioration of bicycle safety in Belgium does not seem due to a possible increase in bicycle use. Due to a lack of detailed exposure data for cyclists this trend can however not be evaluated scientifically. In the age group of 10 to 14 year olds almost 35% of all fatalities occurs on a bicycle. In the group of 20 to 24 year olds, this percentage drops to a mere 2 percent. The analysis of all bicycle injuries (mainly light injuries) reveals a peak for 10 to 19 year olds, while this peak does not occur in the elderly age groups. Senior cyclists however, are overrepresented in serious injuries: about half of all cyclist fatalities occurs in the group above 65 years of age. A detailed analysis of the location of bicycle accidents shows that about 75 percent of all light injuries occurs in built up areas. For other road users, this percentage is only around 55 percent. Serious bicycle injuries also occur more frequently in built up areas (60%) than for other road users (40%). Fatal accidents occur mostly outside built-up areas for both cyclists (60%) as for other road users (70%). This summary of the safety of cycling seems to appear relatively negative for cyclists. However, analyses of the total benefits of cycling for society show a much more positive picture. Hartog et al (2010), for instance, showed that a modal shift from cars to bicycles for short trips would result in 9 times more life years gained than life years lost due to traffic accidents and an increased exposure to air pollution. These analyses clearly show that efforts are necessary to increase bicycle safety and to stimulate bicycle use. An analysis of the international literature on measures to reduce bicycle accidents shows that speed management in the large sense is the most crucial lever to increase bicycle safety. In order to diminish the fatality risk for cyclists, strategic visions like Vision Zero and Sustainable Safety recommend to limited the maximally allowed speed to 30 or 50 km/h at locations where cyclists and motorized traffic are subject to possible direct conflicts. In areas with higher speed limits maximal efforts are necessary to segregate cyclists from motorized traffic. Given the importance of single vehicle crashes in the total number of bicycle accidents the design and maintenance of road infrastructure needs to take care of obstacles and situations that increase the accident risk for cyclists or that increase the potential seriousness of cycle accidents. At the level of the cyclists, the emphasis is put on the cyclists' visibility and the use of bicycle helmets. The technical and ergonomic properties of the bicycle are also important. An optimization of the passive and active safety of motorized vehicles and certain ITS applications can also increase bicycle safety. In-depth research shows that the human factor (human behaviour) remains the most important causal factor in road traffic accidents, also for cyclists. Systematic efforts and hence necessary at the level of traffic enforcement and road safety education. (Author/publisher

The possible impact of progressive fines on road safety. Paper presented at the Brake’s 5th international Speed Congress, London, 7 May 2014.

Vehicles with more than one traffic violation annually are known to be more than proportionally involved in road crashes. It is known that crash frequency increases with the square of the violation frequency approximately. In this paper the possible effects of a progressive fine level is researched. The assessment is based on the known distribution of the number of vehicles as a function of their annual violation frequency and their crash rate, for the current situation with a fine level that does not depend on previous traffic violation behaviour. Then several progressive fining schemes are proposed, of which the effects on road safety are estimated. The calculation assumes that drivers adjust their driving behaviour in order to keep the total annual amount of fines constant. It is shown how this (expected) effect improves with more strict progressive fining schemes. Several expected difficulties with the actual implementation of such a scheme are discussed. (Author/publisher

Vermoeidheid en slaperigheid.

Fatigue and sleepiness behind the wheel are a major problem within traffic safety. In the remainder of this document, we’ll use the term “fatigue’ for these two closely related phenomena. Tiredness is a state of impaired alertness resulting in reduced capacity and motivation to act. Fatigue has a physical and a mental / psychological aspect. There are five general causes of fatigue: the time spent on a task or job; sleep deprivation; biorhythm; monotony of the task; and individual characteristics (including medical condition and use of alcohol, drugs and medicines). Fatigue behind the wheel leads to a number of negative effects on traffic behavior, including a slower reaction time, decreased alertness, decreased levels of information processing and worse steering. Individual characteristics such as age, medical condition, use of alcohol, medicines or drugs, affect how susceptible people are to fatigue and how well they can cope with fatigue. Older people (70+) and people with poor physical condition are more fatigued. Teens often need extra sleep while they actually sleep very little, and this makes teenagers again more susceptible to the effects of fatigue because of alcohol, drugs or sleeping poorly. Insomnia is a common sleep disorder that leads to fatigue during the daytime. Sleep apnea is also common and is not recognized by many people. It is difficult to gauge precisely which percentage of drivers drives while drowsy or tired, but a recent study by the Belgian Road Safety Institute indicates that in Belgium about 4.8% of motorists are drowsy or tired behind the wheel. Young drivers, drivers who drive at night, drivers who have been drinking alcohol or drivers making long journeys are most susceptible to fatigue. Other studies of the Belgian Road Safety Institute reveal that 58% of drivers admit having driven at least once while they were sleepy in the last year. Determining the share of fatigue as (co-)cause of road accidents is not easy. Scientific estimates based on in-depth research of road accidents indicate that about 10 to 15% of road accidents are related to fatigue. A White Paper published in 2013 about drowsiness in traffic, written by European Sleep and fatigue experts, estimates that drowsiness plays a role in about 20% to 25% of road accidents in Europe. Measures against fatigue in traffic can focus on drivers, enterprises, roads or vehicles. Drivers can be made aware about the dangers of tired driving and the best counteractions through campaigns. Transportation enterprises can implement a policy to combat fatigue. The roads can be equipped with markers which provide audio-tactile feedback when they are being driven on, and can also be equipped with shoulder safeguards or obstacle-free zones to reduce the effects of accidents linked to fatigue. Alongside motorways safe resting places can be installed so that drivers have the opportunity to stop in time and to rest (safely). Finally, fatigue can be further controlled by an improvement in the driving and resting time legislation and specific warning systems and fatigue detection systems in vehicles. Research by the Belgian Road Safety Institute reveals that the Belgian motorists usually use inappropriate methods to combat fatigue, such as opening the window and turning the radio up. (Author/publisher