Davide Shingo Usami

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.

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

INTRODUCTION Smeeds law defines the functional relationship existing between the fatality rate and the motorization rate.While focusing on the Italian case and based on the Smeeds 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 Smeeds 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 Smeeds 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 Smeeds 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.

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.