Frequently, road traffic accidents are modelled as discrete and independent random and rare events, which possess a low probability of occurrence through time. Nevertheless, in order to study each accident individually it is necessary to obtain details of a number of characteristics that surround it, which may be correlated with each other. In this article, we propose to associate the probability of occurrence of an accident with a large number of features such as weather conditions, incidents caused by the start and end of a roadwork, geographical location of speed control radars, roadway infrastructure, etc. The influence of these features is significant and should be taken into account when proposing measures to help alleviate these undesirable events. The big data methods employed to extract the variables or features allow us to compose a series of vectors that will serve as a basis to study road accident distributions.