In-built network-wide road safety assessment methodologies for rural roads.

OBJECTIVE: Road traffic crashes are mainly caused by three concurrent factors: infrastructure, vehicle, and human factors. Regarding infrastructure, in recent decades, a series of management tools and procedures called Road Infrastructure Safety Management (RISM) have been proposed. The aim of RISM procedures is to support road authorities in the prevention and mitigation of future road traffic crashes. One of these procedures is the In-built Road Safety Assessment (IRSA) methodology. The peculiarity of an IRSA methodology is the underpinning method used to assign a score to a road section with the aim of identifying those road sections in a network with safety-related infrastructure deficiencies. The objective of this paper is to provide an overall literature review of existing methodologies used worldwide for network-wide road safety assessment for rural road. METHODS: The review was conducted following the guidelines provided by Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) checklist 2020. The characteristics of these methodologies were compared with respect to the following research questions: What are the general characteristics of IRSA methodologies? What risk method/index is applied? Which data collection method/technique is used? What types of road parameters are considered for the assessment? What is the level of expertise needed to implement the methodology? Where and how are the results validated? RESULTS: As a result, 14 IRSA methodologies were identified. Also, the review showed that similar road parameters were used including: operating speed, road surface, low curve radius, poor sight distance (horizontal and vertical curves), lane width, undivided road (median type), shoulder width, sight obstructions (landscape, obstacles and vegetation), absence of traffic signs and road markings, traffic flow (AADT), intersection quality and density of intersections/lateral accesses. CONCLUSIONS: Despite these similarities, some differences were observed in risk formulation, safety quantities of parameters, level of expertise required, and validation of studies. Researchers may use these findings to develop future road safety assessment methodologies, while road practitioners can make use of this in identifying suitable network-wide assessment methods for safety assessments of road infrastructures. Finally, a series of recommendations for future research work on IRSA methodologies is suggested.

Investigation of accidents involving powered two wheelers and bicycles - A European in-depth study.

INTRODUCTION: The number of road fatalities have been falling throughout the European Union (EU) over the past 20 years and most Member States have achieved an overall reduction. Research has mainly focused on protecting car occupants, with car occupant fatalities reducing significantly. However, recently there has been a plateauing in fatalities amongst 'Vulnerable Road Users' (VRUs), and in 2016 accidents involving VRUs accounted for nearly half of all EU road deaths. METHOD: The SaferWheels study collected in-depth data on 500 accidents involving Powered Two-Wheelers (PTWs) and bicycles across six European countries. A standard in-depth accident investigation methodology was used by each team. The Driver Reliability and Error Analysis Method (DREAM) was used to systematically classify accident causation factors. RESULTS: The most common causal factors related to errors in observation by the PTW/bicycle rider or the driver of the other vehicle, typically called 'looked but failed to see' accidents. Common scenarios involved the other vehicle turning or crossing in front of the PTW/bicycle. A quarter of serious or fatal injuries to PTW riders occurred in accidents where the rider lost control with no other vehicle involvement. CONCLUSIONS: Highly detailed data have been collected for 500 accidents involving PTWs or bicycles in the EU. These data can be further analyzed by researchers on a case-study basis to gain detailed insights on such accidents. Preliminary analysis suggests that 'looked but failed to see' remains a common cause, and in many cases the actions of the other vehicle were the critical factor, though PTW rider speed or inexperience played a role in some cases. Practical Applications: The collected data can be analyzed to better understand the characteristics and causes of accidents involving PTWs and bicycles in the EU. The results can be used to develop policies aimed at reducing road deaths and injuries to VRUs.

Aggregated analysis of in-depth accident causation data.

Data collected from in-depth road accident investigations are very informative and may contain more than 500 accident-related variables for a single investigated case. These data may be used to get a more detailed knowledge on accident and injury causation associated with a specific accident scenario. However, due to their complexity, studies using in-depth data at aggregated levels are not common. The objective of this paper is to propose a methodology to analyse aggregated accident causation charts in order to highlight strong and weak relationships between crash causes and pre-crash scenarios. These relationships can be taken into account when developing or assessing new road safety measures (e.g. in-vehicle systems). The methodology has been applied to an in-depth accident dataset derived from the European project SafetyNet. Four different pre-crash scenarios associated with the accident scenario 'vehicles encountering something while remaining in their lane' have been investigated. Even if generalization of these results should be done with care because of database representativeness issues, the methodology is promising, highlighting, for example, a well-defined causation pattern related to vehicles striking a vehicle in rear-end accidents.

Smeed's law and expected road fatality reduction: An assessment of the Italian case.

INTRODUCTION: Smeed's law defines the functional relationship existing between the fatality rate and the motorization rate.While focusing on the Italian case and based on the Smeed's law, the study assesses the possibility for Italy of reaching the target of halving the number of road fatalities by 2020, in light of the evolving socioeconomic situation. METHOD: A Smeed's model has been calibrated based on the recorded Italian data. The evolution of the two indicators, fatality and motorization rates, has been estimated using the predictions of the main parameters (population, fleet size and fatalities). Those trends have been compared with the natural decreasing trend derived from the Smeed's law. RESULTS: Nine scenarios have been developed showing the relationship between the fatality rate and the motorization rate. In case of a limited increase (logistic regression) of the vehicle fleet and according to the estimated evolution of the population, the path defined by motorization and fatality rate is very steep, diverging from the estimated confidence interval of the Smeed's model. In these scenarios the motorization rate is almost constant during the decade. CONCLUSIONS: In the actual economic context, a limited development of the vehicle fleet is more plausible. In these conditions the target achievement of halving the number of fatalities in Italy may occur only in case of a structural break (i.e., the introduction of highly effective road safety policies). Practical application: The proposed tools can be used both to evaluate retrospectively the effectiveness of road safety improvements and to assess if a relevant effort is needed to reach the established road safety targets.

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries.

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.