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Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the ‘safe driving behaviour of machines’ are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers’ driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe ‘behaviour’ of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the ‘behaviour’ of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users’ perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety – SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors.

Safety-in-numbers denotes the tendency for the risk of accident for each road user to decline as the number of road users increases. Safety-in-numbers implies that a doubling of the number of road users will be associated with less than a doubling of the number of accidents. This paper investigates safety-in-numbers in 239 pedestrian crossings in Oslo and its suburbs. Accident prediction models were fitted by means of negative binomial regression. The models indicate a very strong safety-in-numbers effect. In the final model, the coefficients for traffic volume were 0.05 for motor vehicles, 0.07 for pedestrians and 0.12 for cyclists. The coefficient for motor vehicles implies that the number of accidents is almost independent of the number of motor vehicles. The safety-in-numbers effect found in this paper is stronger than reported in any other study dealing with safety-in-numbers. It should be noted that the model explained only 21% of the systematic variation in the number of accidents. It therefore cannot be ruled out that the results are influenced by omitted variable bias. Any such bias would, however, have to be very large to eliminate the safety-in-numbers effect.

One of the main aims of introducing automation in transport is to improve safety by reducing or eliminating human errors; it is often argued however that this may induce new types of errors. There is different level of maturity with automation in different transport modes (road, aviation, maritime and rail), however no systematic research has been conducted on the lessons learned in different sectors, so that they can be exploited for the design of safer automated systems. The aim of this paper is to review the impact of key human factors on the safety of automated transport systems, with focus on relevant experiences from different transport sectors. A systematic literature review is carried out on the following topics: the level of trust in automation – in particular the impact of mis-aligned trust, i.e. mistrust vs overreliance, the resulting impact on operator situation awareness (SA), the implications for takeover control from machine to human, and the role of experience and training on using automated transport systems. The results revealed several areas where experiences from the aviation and road domain can be transferable to other sectors. Experiences from maritime and rail transport, although limited, tend to confirm the general patterns. Remarkably, in the road sector where higher levels of automation are only recently introduced, there are clearer and more quantitative approaches to human factors, while other sectors focus only on mental modes. Other sectors could use similar approaches to define their own context-specific metrics. The paper makes a synthesis of key messages on automation safety in different transport sectors, and presents an assessment of their transferability.

This paper proposes methodological guidelines for developing accident modification functions. An accident modification function is a mathematical function describing systematic variation in the effects of road safety measures. The paper describes ten guidelines. An example is given of how to use the guidelines. The importance of exploratory analysis and an iterative approach in developing accident modification functions is stressed. The example shows that strict compliance with all the guidelines may be difficult, but represents a level of stringency that should be strived for. Currently the main limitations in developing accident modification functions are the small number of good evaluation studies and the often huge variation in estimates of effect. It is therefore still not possible to develop accident modification functions for very many road safety measures. © 2015 Elsevier Ltd. All rights reserved.

Scientific literature lacks a model which combines exposure to risk, risk, and the relationship between them. This paper presents a conceptual road safety framework comprising mutually interacting factors for exposure to risk resulting from travel behaviour (volumes, modal split, and distribution of traffic over time and space) and for risk (crash and injury risk). The framework's three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury risks are modelled by the three ‘safety pillars’: infrastructure, road users and the vehicles they use. Creating a link in the framework between risk and exposure is important because of the ‘non-linear relationship’ between them, i.e. risk tends to decrease as exposure increases. Furthermore, ‘perceived’ risk (a type of travel resistance) plays a role in mode choice, i.e. the perception that a certain type of vehicle is unsafe can be a deterrent to its use. This paper uses theories to explain how the elements in the model interact. Cycling is an area where governments typically have goals for both mobility and safety. To exemplify application of the model, the paper uses the framework to link research on cycling (safety) to land use and infrastructure. The model's value lies in its ability to identify potential consequences of measures and policies for both exposure and risk. This is important from a scientific perspective and for policy makers who often have objectives for both mobility and safety.

Surveys finding that a large majority of drivers regard themselves as safer than the average driver have been ridiculed as showing that most drivers are overconfident about their safety and as showing something which is logically impossible, since in a normal distribution exactly half are below average and half above. This paper shows that this criticism is misplaced. Driver accident involvement does not follow a normal distribution, and it is mathematically entirely possible that a huge majority of drivers could be safer than the average driver. The distribution of accidents in a population of drivers is typically skewed, with a majority of drivers not reporting involvement in any accident in the period covered by the data, often a period of 1-3 years. In this paper, examples are given of data sets in which the percentage of drivers who are safer than the average driver ranges from about 70% to 90%. The paper explains how, based on knowing the mean and variance of the distribution of accidents in a population of drivers in a given period, the long-term expected number of accidents for drivers who were involved in 0, 1, 2, or more accidents can be estimated. Such estimates invariably show that the huge majority of drivers are safer than the average driver.

This paper presents an analysis of the relationships between the passage of key alcohol safety laws and the number of drinking drivers in fatal crashes. The study evaluated three major alcohol safety laws--administrative license revocation laws, 0.10 illegal per se, and 0.08 illegal per se laws--on the proportion of drinking drivers in fatal crashes. Drivers aged 21 and older in fatal crashes at two BAC levels--0.01-0.09 and 0.10 or greater--were considered separately. Drivers under age 21 were not included because they are affected by the Minimum Legal Drinking Age (MLDA) law. This study used data on drinking drivers in fatal crashes from the Fatality Analysis Reporting System (FARS) covering 16 years (1982-1997) for all 50 states and the District of Columbia. Also included in the study were such variables as per capita alcohol consumption and annual vehicle miles traveled (VMT), which could affect the number of alcohol-related crashes. The results indicate that each of the three laws had a significant relationship to the downward trend in alcohol-related fatal crashes in the United States over that period. This paper points out that this long-term trend is not the product of a single law. Instead, it is the result of the growing impact of several laws over time plus the affect of some factors not included in the model tested (such as the increasing use of sobriety checkpoints and the media's attention to the drinking-and-driving problem).

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of “successful” interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's “safety pyramid”. Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches.

UN Regulation 157, the first global regulation regarding the type-approval of Automated Driving Systems (ADS), has been adopted in 2021. In it, safety performance requirements are being defined for vehicles of automation Level 3, according to the SAE J3016, with a limited Operational Design Domain (ODD). In particular, for three types of events that are related to motorway driving, two models are provided to distinguish between preventable traffic scenarios, for which the ADS is expected to avoid an accident, and unpreventable traffic scenarios, for which accidents cannot be avoided and the ADS can only mitigate their severity. The models recreate the short-term behavior of a driver who reacts to an emergency. Two possible actions are predicted: either no reaction or full braking when danger is identified. In the present paper the two models are analyzed and compared with two additional models: an industry proposed model, the Responsibility Sensitive Safety framework (RSS), and the Fuzzy Safety Model (FSM) proposed by the authors. As in the case of the two regulation models, also the RSS, although more sophisticated, assumes that the possible reaction by the driver is binary. This approach neglects the ability of a human driver to drive defensively and anticipate possible risks. Defensive drivers, indeed, may use comfortable decelerations in anticipation, to avoid finding themselves in an emergency situation. The FSM uses fuzzy logic to mimic this behavior. Results show that anticipation plays a very important role to reduce the number of unpreventable traffic scenarios. In addition, by validating the classification capabilities of the four models with real traffic data, the FSM proved to be the most suitable of the investigated models. On the basis of these results, the FSM has been included in the proposal for amending UN Regulation 157, thus allowing to set higher safety standards for the first automated vehicles that will be introduced into the market.

The European Road Safety Decision Support System (roadsafety-dss.eu) is an innovative system providing the available evidence on a broad range of road risks and possible countermeasures. This paper describes the scientific basis of the DSS. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation instrument (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs.

Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision-making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12-47%, 50-80%, 82-92% and 90-94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.

Behavioural observation studies in road safety research collect naturalistic data of road users that are not informed (beforehand) of their participation in a research project. It enables the observation of behavioural and situational processes that contribute to unsafe traffic events, while possible behavioural adaptations due to the road users’ recognition of being observed are minimized. The literature in this field is vast and diverse, with studies dating back to the 1930s. The aim of this paper is to summarize the research efforts in the domain of road user behavioural observation research to examine trends and developments of this type of research, using a scoping review. After the definition of certain selection criteria, 600 journal articles found in three major online databases were retrieved and included in this review. The number of publications regarding road user behavioural observation studies has increased rapidly during recent years, indicating the importance of behavioural observation studies to study traffic safety. Most studies collected data on car drivers (81%), while vulnerable road users have been observed in 32% of all studies, with pedestrians and (motor)cyclists as the most common road user types. The results showed that the main goal of behavioural observation is to monitor (51%), followed by the evaluation of a specific safety improving measure (38%) and the development of behavioural models (10%). Most topics relate to traffic events where interactions with other road users are necessary, indicating that the examination of behavioural processes underlying single-vehicle crashes has received little attention. The ongoing developments of automated video analysis software tools can be the next methodological step forward in video-based behavioural observation studies, since it enables a more objective data collection and data analysis process. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 635895 (InDeV- In-Depth-understanding of accident causation for Vulnerable road users). This publication reflects only the authors' views. The European Commission is not responsible for any use that may be made of the information it contains.

Built-environment factors potentially alleviate or aggravate traffic safety problems in urban areas. This paper aims to investigate the relationships of these factors with vehicle-bicycle and vehicle-vehicle property damage only (PDO) and killed and severe injury (KSI) crashes in urban areas. For this purpose, an area-level analysis using 100x100m2 cells, along with a Spatial Hurdle Negative Binomial regression model were employed. The study area is composed of a selection of municipalities in the Netherlands-Randstad Area where major land-use developments have occurred since the 1970s. The study was conducted by developing a rich dataset composed of various national and local databases. The findings reveal that built-environment factors and land-use policies have substantial impacts on safety, which cannot be neglected. The factors explaining the land-use density and diversity in the area (e.g., urbanity and function mixing levels), as well as the land-use design characteristics (indicated by average age of the neighborhoods), traffic and road network characteristics, and proximity to different destinations influence the probability, frequency, and severity of crashes in urban areas. Furthermore, low socioeconomic levels are associated with a higher frequency of traffic crashes.

As the desire for deploying automated (“driverless”) vehicles increases, there is a need to understand how they might communicate with other road users in a mixed traffic, urban, setting. In the absence of an active and responsible human controller in the driving seat, who might currently communicate with other road users in uncertain/conflicting situations, in the future, understanding a driverless car’s behaviour and intentions will need to be relayed via easily comprehensible, intuitive and universally intelligible means, perhaps presented externally via new vehicle interfaces. This paper reports on the results of a questionnaire-based study, delivered to 664 participants, recruited during live demonstrations of an Automated Road Transport Systems (ARTS; SAE Level 4), in three European cities. The questionnaire sought the views of pedestrians and cyclists, focussing on whether respondents felt safe interacting with ARTS in shared space, and also what externally presented travel behaviour information from the ARTS was important to them. Results showed that most pedestrians felt safer when the ARTS were travelling in designated lanes, rather than in shared space, and the majority believed they had priority over the ARTS, in the absence of such infrastructure. Regardless of lane demarcations, all respondents highlighted the importance of receiving some communication information about the behaviour of the ARTS, with acknowledgement of their detection by the vehicle being the most important message. There were no clear patterns across the respondents, regarding preference of modality for these external messages, with cultural and infrastructural differences thought to govern responses. Generally, however, conventional signals (lights and beeps) were preferred to text-based messages and spoken words. The results suggest that until these driverless vehicles are able to provide universally comprehensible externally presented information or messages during interaction with other road users, they are likely to contribute to confusing and conflicting interactions between these actors, especially in a shared space setting, which may, therefore, reduce efficient traffic flow.

This paper investigates the effect that texting with WhatsApp, one of the most common applications for instant messaging, exerts on driving performance. Because distracted driving also affects older drivers, who can have seriously compromised vision, we also analysed the associations between visual-function parameters and driving performance. A total of 75 drivers, experienced in sending WhatsApp messages (≥10WhatsApp messages/week), participated in this study and were divided into four age categories. Visual-function tests included contrast sensitivity with and without glare, retinal straylight and objective assessment of optical quality. Simulated driving performance was assessed under a baseline driving condition (without distraction) as well as a texting condition (WhatsApp messages) while driving. The participants used their own mobile phone. Lastly, objective results of driving performance were compared with subjective self-report data from the Driver Behaviour Questionnaire (DBQ). The analysis indicated that functional changes occurring with age, such as a lower contrast sensitivity and greater retinal straylight, were correlated with a higher number of collisions, longer distances driven outside the lane, and greater standard deviation of lateral position (SDLP). The results showed a significant main effect of age for the driving-performance parameters. Also, compared to the baseline, texting WhatsApp messages while driving worsens driving performance for all age groups, most notably among older participants. Thus, the older drivers' SDLP was ∼14% higher than that for the baseline average of all the other drivers and rose to 29% under distraction, reflecting the impact of secondary tasks. The negative effect of the use of the smartphone during driving was also reflected in the number of collisions, with a greater risk of accidents in all the groups of drivers (by 8.3% for young adults, 25.0% for adults, 80.5% for middle-aged adults, and 134.5% for older drivers). Lastly, participants' subjective responses indicated that younger drivers (18-24 years) had a higher risk of deliberately violating safe driving practices (p 0.05). The present study demonstrates that texting WhatsApp messages while driving significantly impairs the ability to drive safely, with older drivers being the group most adversely affected. It would be recommendable to include other nonstandard vision tests, which have shown associations with driving performance, in the examination for driver licensing. This would help raise the awareness of older drivers concerning their visual limitations, permitting them to adopt compensatory measures to improve their driving safety. Nevertheless, it is also necessary to raise awareness among the younger drivers of the risks involved in behaviour behind the wheel.

Speed and speed variation are closely associated with traffic safety. There is, however, a dearth of research on this subject for the case of urban arterials in general, and in the context of developing nations. In downtown Shanghai, the traffic conditions in each direction are very different by time of day, and speed characteristics during peak hours are also greatly different from those during off-peak hours. Considering that traffic demand changes with time and in different directions, arterials in this study were divided into one-way segments by the direction of flow, and time of day was differentiated and controlled for. In terms of data collection, traditional fixed-based methods have been widely used in previous studies, but they fail to capture the spatio-temporal distributions of speed along a road. A new approach is introduced to estimate speed variation by integrating spatio-temporal speed fluctuation of a single vehicle with speed differences between vehicles using taxi-based high frequency GPS data. With this approach, this paper aims to comprehensively establish a relationship between mean speed, speed variation and traffic crashes for the purpose of formulating effective speed management measures, specifically using an urban dataset. From a total of 234 one-way road segments from eight arterials in Shanghai, mean speed, speed variation, geometric design features, traffic volume, and crash data were collected. Because the safety effects of mean speed and speed variation may vary at different segment lengths, arterials with similar signal spacing density were grouped together. To account for potential correlations among these segments, a hierarchical Poisson log-normal model with random effects was developed. Results show that a 1% increase in mean speed on urban arterials was associated with a 0.7% increase in total crashes, and larger speed variation was also associated with increased crash frequency.

In the framework of infrastructure analysis and maintenance in an urban environment, it is important to address the safety of every road user. This paper presents a methodology for the evaluation of several safety indicators on pedestrian crossing environments using geometric and radiometric information extracted from 3D point clouds collected by a Mobile Mapping System (MMS). The methodology is divided in four main modules which analyze the accessibility of the crossing area, the presence of traffic lights and traffic signs, and the visibility between a driver and a pedestrian on the proximities of a pedestrian crossing. The outputs of the analysis are exported to a Geographic Information System (GIS) where they are visualized and can be further processed in the context of city management. The methodology has been tested on approximately 30 pedestrian crossings in cluttered urban environments of two different cities. Results show that MMS are a valid mean to assess the safety of a specific urban environment, regarding its geometric conditions. Remarkable results are presented on traffic light classification, with a global F-score close to 95% Dirección General de Tráfico (Spanish Ministry of Interior) | Ref. SPIP2017-02122 Xunta de Galicia | Ref. ED431C2016‐038 Human Resources Program FPI | Ref. BES-2014-067736

Consistent with the experiences in high-income countries, young drivers remain overrepresented in road trauma statistics in low- and middle-income countries. This article pursues the emerging interest of approaching the young driver problem from a systems thinking perspective in order to design and deliver robust countermeasures. Specifically, the focus of this paper is the cars driven by young drivers. The study of vehicles' characteristics and their interaction with driving behaviour is, more often than not, considered a minor concern when developing countermeasures in young drivers' safety not only in developed nations, but especially in developing nations. Participants completed an online survey containing the 44-item Behaviour of Young Novice Drivers Scale Spanish version (BYNDS-Sp), in addition to providing information regarding their vehicle, any crash involvement, and driving offences. Based on the vehicle model information, the assessment of vehicle safety was conducted for three safety programs (ANCAP, Latin NCAP, U.S. NCAP). Young drivers in Colombia reported a breadth of risky driving behaviours worth targeting in broader interventions. For example, interventions can target speeding, particularly as three quarters of the participants drove small-medium cars associated with poorer road safety outcomes. Moreover, risky driving exposure was highly prevalent amongst the young driver participants, demonstrating the need for them to be driving the safest vehicles possible. It is noteworthy that few cars were able to be assessed by the Latin NCAP (with half of the cars rated having only 0-2 star ratings), and that there was considerable discrepancy between ANCAP, U.S. NCAP, and Latin NCAP ratings. The need for system-wide strategies to increase young driver road safety-such as improved vehicle safety-is vital to improve road safety outcomes in jurisdictions such as Colombia. Such improvements may also require systemic changes such as enhanced vehicle safety rating scales and investigation of the nature of vehicles sold in developing nations, particularly as these vehicles typically contain fewer safety features than their counterparts sold in developed nations.

Randomised controlled trials (also known as experiments) are widely regarded as the best design of studies that aim to estimate the effects of a treatment, like a road safety measures. However, as noted by Hauer (2016) there are few randomised controlled trials in the field of road safety. Hauer has suggested (2019) that not finding an effect of the measure being evaluated could be one reason for this. This paper provides an inventory of experimental evaluations of road safety measures. 24 different road safety measures have been evaluated experimentally. More than one experiment was reported for 8 road safety measures; for the other 16 only a single experiment was found. With few exceptions, experiments find no effect on accidents of the measures subjected to experiments. Most of the exceptions are due either to failure of randomisation or refer to measures whose effects tend to erode as they become more commonly used. Thus, the effects found in initial experiments with daytime running lights and high-mounted stop lamps have gradually eroded as more cars got these systems. Driver training is one of the few road safety measures for which more than one experiment has been carried out. Results consistently show no effect on accidents of driver training.

Limitations of historical crash data and the advantages of surrogate measures of safety have already been stressed by various authors. To describe nearness-to-collision, mostly time-based indicators are applied, and there is a consensus among researchers that speed-related indicators are needed to account for the severity dimension. There have been attempts to fit bivariate Extreme Value models to predict the number of crashes, however modeling crash frequency together with severity has received much less attention. The aim of this paper is to apply Extreme Value models to various pairs of temporal and speed-related indicators in order to investigate the dependence structure between them as well as to construct probability based risk levels and examine them in relation to severity levels. Bivariate threshold excess models were fitted to a dataset of left-turning and straight moving vehicle interactions recorded in a signalized intersection. The dependence structure between variable pairs were thoroughly investigated; it was concluded that temporal and speed related indicators were found independent, which means that road users getting close to each other in time does not necessarily come with high speeds. Therefore these indicators should be combined in order to properly predict severity; a temporal indicator on its own is not enough to make inferences about the severity of events. Using the calculated joint probability of events risk levels were constructed illustrating the events of equal probability.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. New vehicle safety systems have led to a steady improvement of road safety and a reduction in the risk of suffering a major injury in vehicle accidents. A huge leap forward in the development of new vehicle safety systems are actuators that have to be activated irreversibly shortly before a collision in order to mitigate accident consequences. The triggering decision has to be based on measurements of exteroceptive sensors currently used in driver assistance systems. This paper focuses on developing a novel context-aware system designed to detect potential collisions and to trigger safety actuators even before an accident occurs. In this context, the analysis examines the information that can be collected from exteroceptive sensors (pre-crash data) to predict a certain collision and its severity to decide whether a triggering is entitled or not. A five-layer context-aware architecture is presented, that is able to collect contextual information about the vehicle environment and the actual driving state using different sensors, to perform reasoning about potential collisions, and to trigger safety functions upon that information. Accident analysis is used in a data model to represent uncertain knowledge and to perform reasoning. A simulation concept based on real accident data is introduced to evaluate the presented system concept.

This paper presents a meta-analysis of the road safety effects of converting junctions to roundabouts. 44 studies containing a total of 154 estimates of effect were included. Based on a meta-regression analysis, converting junctions to roundabouts is associated with a reduction of fatal accidents of about 65% and a reduction of injury accidents of about 40%. The mean effect on property-damage-only accidents is ambiguous. Summary estimates of effect are robust for fatal and injury accidents, but vary depending on the model of meta-analysis and the treatment of outlying data points for property-damage-only accidents. A trim-and-fill analysis suggests a weak tendency for publication bias, with modest influence on summary estimates of effect. It is concluded that roundabouts are very effective in reducing traffic fatalities.

This paper evaluates the effects of the London Cycle Superhighways (CS) on cycle collisions. A total of 45 CS segments and 375 control segments are observed for a period of 8 years in London. Variables such as road characteristics, crash history and socio-economic in formation are included in the data set. Traffic characteristics Including traffic volume, cycle volume and traffic speed are obtained from Department for Transport. We first estimate the safety effects on the CS routes using Empirical Bayes methods. Then propensity score matching methods are also applied for comparison. The introduction of cycle superhighways caused cycling traffic volumes to increase dramatically along CS routes with no significant impacts on collision rates. Our models find that the increase in traffic was associated with a rise in annual total cycle collisions of around 2.6 per km (38% in percentage). However, when we re-estimate the effects based on cycle collision rates rather than levels, our results also show that the CS routes are not more dangerous or safer than the control roads. Among the four CS routes, CS3 performs the best in protecting cyclists with a large proportion of segregated lanes whilst the cyclists have to share the lanes with motorists on other routes. It is recommended that consistent safety designs should be applied on all CS routes for a safer cycling environment.

Motivated by the thousands of pedestrians killed each year in train impacts, this paper investigates the life-saving capability of four high-level locomotive bumper concepts. The head motions produced by the four concepts are modeled as one or two square acceleration pulses and are analyzed using the Head Injury Criterion (HIC). Surprisingly, the analyses show that all four concepts can achieve HIC values of less than 200 for an impact with a locomotive traveling at 100km/h. Two of the concepts eject the pedestrian trackside with at a velocity of roughly 40km/h and the risk of ground-impact injury is discussed in the context of related automobile accident data. The computed bumper lengths are a fraction of the overall length of a locomotive and are thus feasible for practical implementation. One concept involves an oblique impact and the potential for rotational head injury is analyzed. This basic feasibility research motivates future investigations into the detailed design of bumper shapes, multi-body pedestrian simulations, and finite-element injury models.

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies., The authors would like to thank for the financial support provided by "Fundacao para a Ciencia e a Tecnologia" (FTC), Portugal, through Doctor grant SFRH/BD/62458/2009 and project UI 4047 - 2014: PEst-OE/ECI/UI4047/2014.

Among the three major safety assessment methods (i.e., simulation, test track, and on-road test) for highly automated driving systems (ADS), test tracks provide high fidelity and a safe and controllable testing environment. However, due to the lack of realistic background traffic, scenarios that can be tested in test tracks are usually static and limited. To address this limitation, a new safety assessment framework is proposed in this paper, which integrates an augmented reality (AR) testing platform and a testing scenario library generation (TSLG) method. The AR testing platform generates simulated background traffic in test tracks, which interact with subject ADS under test, to create a realistic traffic environment. The TSLG method can systematically generate a set of critical scenarios under each operational design domain (ODD) and the critical scenarios generated from the TSLG method can be imported into the AR testing platform. The proposed framework has been implemented in the Mcity test track at the University of Michigan with a Level 4 ADS. Field test results show that the proposed framework can accurately and efficiently evaluate the safety performance of highly ADS in a cost-effective fashion. In the cut-in case study, the proposed framework is estimated to accelerate the assessment process by 9.87×104 times comparing to the on-road test approach.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Recently, technologies for predicting traffic conflicts in real-time have been gaining momentum due to their proactive nature of application and the growing implementation of ADAS technology in intelligent vehicles. In ADAS, machine learning classifiers are utilised to predict potential traffic conflicts by analysing data from in-vehicle sensors. In most cases, a condition is classified as a traffic conflict when a safety surrogate (e.g. time-to-collision, TTC) crosses a pre-defined threshold. This approach, however, largely ignores other factors that influence traffic conflicts such as speed variance, traffic density, speed and weather conditions. Considering all these factors in detecting traffic conflicts is rather complex as it requires an integration and mining of heterodox data, the unavailability of traffic conflicts and conflict prediction models capable of extracting meaningful and accurate information in a timely manner. In addition, the model has to effectively handle large imbalanced data. To overcome these limitations, this paper presents a centralised digital architecture and employs a Deep Learning methodology to predict traffic conflicts. Highly disaggregated traffic data and in-vehicle sensors data from an instrumented vehicle are collected from a section of the UK M1 motorway to build the model. Traffic conflicts are identified by a Regional–Convolution Neural Network (R-CNN) model which detects lane markings and tracks vehicles from images captured by a single front facing camera. This data is then integrated with traffic variables and calculated safety surrogate measures (SSMs) via a centralised digital architecture to develop a series of Deep Neural Network (DNN) models to predict these traffic conflicts. The results indicate that TTC, as expected, varies by speed, weather and traffic density and the best DNN model provides an accuracy of 94% making it reliable to employ in ADAS technology as proactive safety management strategies. Furthermore, by exchanging this traffic conflict awareness data, connected vehicles (CVs) can mitigate the risk of traffic collisions.

The recent economic crisis has required the bailout of some European States by the so-called Troika, with capital injections accompanied by financial austerity. This paper analyzes econometrically the impact of this support programme on road safety for an original panel data (1995–2015). The findings also corroborate the Kuznets curve hypothesis for traffic accidents in the long term. Regarding the impact of intervention in the short term, despite reductions in safety policy budgets due to austerity, financial support, and related austerity measures might have led to an improvement in road safety, reducing both the number of accidents and fatalities. Therefore, it seems that our result is more linked to the austerity measures than to the financial support given by the Troika.

This paper reports the results of a multi-factorial experiment that was aimed at the following: (a) analyzing driver's speed behavior while approaching zebra crossings under different conditions of vehicle-pedestrian interaction and with respect to several safety measures and (b) comparing safety measures and identifying the most effective treatment for zebra crossings. Three safety countermeasures at pedestrian crossings (curb extensions, parking restrictions and advanced yield markings) and the condition of no treatment (baseline condition) were designed on a two-lane urban road and implemented in an advanced driving simulator. Several conditions of vehicle-pedestrian interaction (in terms of the time left for the vehicle to get to the zebra crossing at the moment the pedestrian starts the crossing) were also simulated. Forty-two drivers completed the driving in the simulator. Based on the recorded speed data, two analyses were performed. The first analysis, which focused on the mean speed profiles, revealed that the driver's speed behavior was affected by conditions of vehicle-pedestrian interaction and was fully consistent with previous findings in the literature and with the Threat Avoidance Model developed by Fuller. Further analysis was based on variables that were obtained from the speed profiles of drivers (the speed at the beginning of the deceleration phase, the distance from the zebra crossing where the deceleration began, the minimum speed value reached during the deceleration, the distance from the pedestrian crossing where the braking phase ended and the average deceleration rate). Multivariate variance analysis (MANOVA) revealed that there was a significant main effect for safety measures and for pedestrian conditions (the presence and absence of a pedestrian). The results identified that the curb extension was the countermeasure that induces the most appropriate driver's speed behavior while approaching the zebra crossing. This conclusion was also confirmed by outcomes of the questionnaire on the countermeasure's effectiveness. More than 80% of the drivers perceived that the curb extensions were effective, which indicates that when this countermeasure was present, the drivers were more willing to yield and that the visibility of the pedestrian crossing was better. For this countermeasure, the lowest number of interactions in which the drivers did not yield to a pedestrian was also recorded.

The road safety literature is typified by a high degree of compartmentalization between studies that focus on infrastructure and traffic conditions and those devoted to the evaluation of public policies and regulations. As a result, few studies adopt a unified empirical framework in their attempts at evaluating the road safety performance of public interventions, thus limiting our understanding of successful strategies in this regard. This paper considers both types of determinants in an analysis of a European country that has enjoyed considerable success in reducing road fatalities. After constructing a panel data set with road safety outcomes for all Spanish provinces between 1990 and 2009, we evaluate the role of the technical characteristics of infrastructure and recent infrastructure spending together with the main regulatory changes introduced. Our results show the importance of considering both types of determinants in a unified framework. Moreover, we highlight the importance of maintenance spending given its effectiveness in reducing fatalities and casualties in the current economic context of austerity that is having such a marked impact on investment efforts in Spain.

Real-world studies of driving behaviour and safety have face validity and have the distinct advantage of focussing on driving in its natural habitat. But their very naturalism can lead to problems with confounds and with noise in the data. This paper reviews the three major categories of on-road studies - controlled observation, field operational tests and naturalistic driving studies - and discusses the major applications of each study type. It also assesses some of the methodological issues that arise in one or more category of study.

This paper explores the similarities and differences between bicycle and motorcycle crashes with other motor vehicles. If similar treatments can be effective for both bicycle and motorcycle crashes, then greater benefits in terms of crash costs saved may be possible for the same investment in treatments. To reduce the biases associated with under-reporting of these crashes to police, property damage and minor injury crashes were excluded. The most common crash type for both bicycles (31.1%) and motorcycles (24.5%) was intersection from adjacent approaches. Drivers of other vehicles were coded most at fault in the majority of two-unit bicycle (57.0%) and motorcycle crashes (62.7%). The crash types, patterns of fault and factors affecting fault were generally similar for bicycle and motorcycle crashes. This confirms the need to combat the factors contributing to failure of other drivers to yield right of way to two-wheelers, and suggest that some of these actions should prove beneficial to the safety of both motorized and non-motorized two-wheelers. In contrast, child bicyclists were more often at fault, particularly in crashes involving a vehicle leaving the driveway or footpath. The greater reporting of violations by riders and drivers in motorcycle crashes also deserves further investigation.

Reliable predictive accident models (PAMs) (also referred to as safety performance functions (SPFs)) are essential to design and maintain safe road networks however, ongoing changes in road and vehicle design coupled with road safety initiatives, mean that these models can quickly become dated. Unfortunately, because the fitting of sophisticated PAMs including a wide range of explanatory variables is not a trivial task, available models tend to be based on data collected many years ago and seem unlikely to give reliable estimates of current accidents. Large, expensive studies to produce new models are likely to be, at best, only a temporary solution. This paper thus seeks to develop a practical and efficient methodology to allow currently available PAMs to be updated to give unbiased estimates of accident frequencies at any point in time. Two principal issues are examined: the extent to which the temporal transferability of predictive accident models varies with model complexity; and the practicality and efficiency of two alternative updating strategies. The models used to illustrate these issues are the suites of models developed for rural dual and single carriageway roads in the UK. These are widely used in several software packages in spite of being based on data collected during the 1980s and early 1990s. It was found that increased model complexity by no means ensures better temporal transferability and that calibration of the models using a scale factor can be a practical alternative to fitting new models.

Advances in safety research – trying to improve the collective understanding of motor vehicle crash causes and contributing factors – rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before–after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models – but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools – representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.

Young people, particularly males and those from a rural area, are over represented in road crashes; this occurs in vehicle dependent countries around the world, including Australia. The attitudes, experiences and behaviours of young drivers are some of the key factors which affect their potential to have an early on road driving experience that is crash free. This paper considers the views of young people, some of whom were brought up on rural properties, and had significant, responsible roles in agricultural work, including task related driving from a very early age. This early responsibility and associated awareness of risk of injury in farm related tasks can affect risk perception and impact on later risk taking behaviours. Ten focus groups were conducted with young people attending schools in four towns in rural New South Wales, Australia. These were recorded and analysed using qualitative methodology. The results indicate that risk factors in driving, on and off road, are clearly identified and understood; the majority of young people in rural areas are not "high" risk takers or sensation seekers. They also indicate that, in rural NSW, early vehicle handling, before the age for obtaining a license for driving on roads, is customary practice within the community. Attitudes towards speeding, however, reflect that it is tolerable behaviour, and almost inevitable, and often not considered to be risky. Drink driving is considered more risky, and less acceptable, at both the personal and community levels. Future intervention strategies to reduce crash rates in young rural drivers should aim to address the attitudes and actions associated with speeding on rural roads.

This paper presents a before-after study of bike boxes at 10 signalized intersections in Portland, Oregon. The bike boxes, also known as advanced stop lines or advanced stop boxes, were installed to increase visibility of cyclists and reduce conflicts between motor vehicles and cyclists, particularly in potential "right-hook" situations. Before and after video were analyzed for seven intersections with green bike boxes, three intersections with uncolored bike boxes, and two control intersections. User perceptions were measured through surveys of cyclists passing through five of the bike box intersections and of motorists working downtown, where the boxes were concentrated. Both the observations and survey of motorists found a high rate of compliance and understanding of the markings. Overall, 73% of the stopping motor vehicles did not encroach at all into the bike box. Both motor vehicle and bicycle encroachment in the pedestrian crosswalk fell significantly at the bike box locations compared to the control intersections. The bike boxes had mixed effects on the motorists' encroachment in the bicycle lane. The number of observed conflicts at the bike box locations decreased, while the total number of cyclists and motor vehicles turning right increased. Negative-binomial models based upon the data predict fewer conflicts with the boxes, particularly as right-turning motor vehicle volumes increase. Observations of yielding behavior at two bike box and one control intersection found an improvement in motorists yielding to cyclists at the bike box locations. Differences in the traffic volumes and location contexts make firm conclusions about the effects of green coloring of the boxes difficult. Higher shares of surveyed motorists felt that the bike boxes made driving safer rather than more dangerous, even when the sample was narrowed to respondents who were not also cyclists. Over three-quarters of the surveyed cyclists thought that the boxes made the intersection safer.

Horse riders represent a significant group of vulnerable road user and are involved in a number of accidents and near misses on the road. Despite this horse riders have received little attention both in terms of academic research and transport policy. Based on literature on vulnerable road user safety, including attitudes to road user safety and behaviour of drivers and their relationship with cyclists and motorcyclists, this paper examines the attitudes and reported behaviour of drivers and horse riders. A total of 46 participants took part in six focus groups divided into four groups of drivers with little or no horse riding experience and two groups of frequent horse riders. Each group investigated five key topic areas stemming from the literature review on vulnerable road users including hazard perception, risk perception, emotion, attitudes to sharing the road and empathy. It was found that drivers and horse riders are not always aware of the same hazards in the road and that this may lead drivers to under-estimate the risk when encountering horses. Drivers often had good intentions to overtake horses safely, but were unaware of how vulnerable passing very wide and slow made them feel until they had begun the manoeuvre and hence quickly reduced such feelings either by speeding up or cutting in too soon. However, other than this, drivers had good skills when encountering horses. But these skills could be impeded by frustration when encountering a slow moving horse which was further compounded by a feeling, mainly by younger drivers, that horse riding was for leisure and as such should not get in the way of necessary work journeys. There is a need for drivers to be more aware of the potential hazards a horse rider faces on the road and these could be achieved through inducing empathy amongst drivers for horse riders, creating nudges for drivers in the environment and better education for drivers.

Although speed limits are indicated by road signs, road users are not always aware, while driving, of the actual speed limit on a given road segment. The Roads and Traffic Agency developed additional road markings in order to support driver decisions on speed on 70 km/h roads in Flanders-Belgium. In this paper the results are presented of two evaluation studies, both a field study and a simulator study, on the effects of the additional road markings on speed behaviour. The results of the field study showed no substantial effect of the markings on speed behaviour. Neither did the simulator study, with slightly different stimuli. Nevertheless an effect on lateral position was noticed in the simulator study, showing at least some effect of the markings. The role of conspicuity of design elements and expectations towards traffic environments is discussed. Both studies illustrate well some strengths and weaknesses of observational field studies compared to experimental simulator studies. (C) 2009 Elsevier Ltd. All rights reserved.

Road safety performance indicators (SPI) have recently been proposed as a useful instrument in comparing countries on the performance of different risk aspects of their road safety system. In this respect, SPIs should be actionable, i.e. they should provide clear directions for policymakers about what action is needed and which priorities should be set in order to improve a country's road safety level in the most efficient way. This paper aims at contributing to this issue by proposing a computational model based on data envelopment analysis (DEA). Based on the model output, the good and bad aspects of road safety are identified for each country. Moreover, targets and priorities for policy actions can be set. As our data set contains 21 European countries for which a separate, best possible model is constructed. a number of country-specific policy actions can be recommended. Conclusions are drawn regarding the following performance indicators: alcohol and drugs, speed, protective systems, vehicle, infrastructure and trauma management. For each country that performs relatively poor. a particular country will be assigned as a useful benchmark. (c) 2008 Elsevier Ltd. All rights reserved.

The evaluation of traffic safety interventions or other policies that can affect road safety often requires the collection of administrative time series data, such as monthly motor vehicle collision data that may be difficult and/or expensive to collect. Furthermore, since policy decisions may be based on the results found from the intervention analysis of the policy, it is important to ensure that the statistical tests have enough power, that is, that we have collected enough time series data both before and after the intervention so that a meaningful change in the series will likely be detected. In this short paper, we present a simple methodology for doing this. It is expected that the methodology presented will be useful for sample size determination in a wide variety of traffic safety intervention analysis applications. Our method is illustrated with a proposed traffic safety study that was funded by NIH.