Gabriele Giustiniani

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.

The development of a European fatal accident database

A lack of representative European accident data to aid the development of safety policy, regulation and technological advancement is a major obstacle in the European Union. Data are needed to assess the performance of road and vehicle safety and also to support the development of further actions by stakeholders. A recent analysis conducted by the European Transport Safety Council identified that there was no single system in place that could meet all of the needs and that there were major gaps including in-depth crash causation information. This paper describes the process of developing a data collection and analysis system designed to partly fill these gaps. A project team with members from seven countries was set up to devise appropriate variable lists to collect fatal crash data, using retrospective detailed police reports (n = 1300), under the following topic levels: accident, road environment, vehicle and road user. The typical level of detail recorded was a minimum of 150 variables for each accident. The project will enable multidisciplinary information on the circumstances of fatal crashes to be interpreted to provide information on a range of causal factors and events surrounding the collisions. This has major applications in the areas of active safety systems, infrastructure and road safety, as well as for tailoring behavioural interventions.

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.