# Abstract
Fatal road accidents have become one of the risks of deaths in the world. Increasing road safety is crucial to save lives and achieve the goals. Road traffic crashes cost most countries 3% of their gross domestic product. Therefore, to measure road safety, evaluating the indicators plays a crucial role in comparing countries and trends by considering fatality rate (number of deaths on roads) and fatal road accident rate (number of accidents with deaths). 
As the worldwide principles and concepts of road safety are not systematically organized, therefore measuring road safety has remained challenging. The paper explores the patterns of road safety and proposes the possibilities how to evaluate road safety using modelling approach. The random events theory was used to assume that the distribution of fatal road accidents is almost perfectly random and the Poisson model gives a good description of the random process. Chi-square test was used to fit the data and to predict the number of fatal road accidents based on big data. The results aim to predict road safety also from sustainability perspective of roads, with more bicycles, scooters and connected traffic systems. Better data-based planning of road systems may also reduce the fatalities on roads.
