It is generally accepted that the road safety trend of a country is influenced by many factors related to the infrastructure, vehicles, health care etc. Nevertheless, road safety improvement is also a result of a learning process, which can come at an individual and a societal level, according to the current available literature. The former is due to the relationship between exposure (number of events an individual experiences) and risk of road accidents, and the latter is due to the learning process in the society.
The authors argue that the long-term improvement in safety does not only happen through individual and societal (i.e., within society) learning, but also through a third dimension which is the learning process across nations (i.e., in between societies). In this paper we attempt to capture this phenomenon in two ways using data for the EU Member States.
We first analyze countries’ progress in safety improvement in relation to their motorization level. Then we use panel regression to investigate whether the Human Development Index (HDI) as a measure of knowledge is a better predictor of safety instead of exposure measures (like car ownership level). The results show that for many countries lagging behind both in motorization and safety it took less time to converge in terms of safety than in motorization level. We also found that the HDI is overall a better predictor. While a few countries are already getting close to the saturation point in their motorization, an alternative knowledge-based predictor is needed for these countries to better describe trends in mortality rate.

The most common way to evaluate traffic safety is investigating the occurrence and severity of crashes using historical data. This approach however has a number of limitations, the most important of which is probably its reactive nature. An alternative method using non-crash events has gained a lot of attention recently, especially thanks to the rapid improvement of sensing technologies. By gathering trajectory data and calculating various Surrogate Measures of Safety it has become possible to analyse safety without waiting for accidents to happen. Using these indicators combined with Extreme Value Theory (EVT) one can estimate the probability of crashes as extreme (unobserved) events. The primary goal of this thesis is to contribute to the research that has been done so far on the application of Extreme Value Theory to Surrogate Measures for traffic safety analysis. Research questions seek for answers to what we can learn from applying univariate EVT using indicators describing collision course and crossing course interactions, and how we can predict nearness to collision and severity using bivariate EVT models.