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A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its main advantages is that Decision Rules can be extracted from its structure and used to identify safety problems and establish certain measures of performance. However, when it used only one DT, the rule extraction is limited to the structure of that DT and some important relationships between variables cannot be extracted. This paper presents a method for extracting rules from a DT more effectively. The method's effectiveness when applied to a particular traffic accidents dataset is shown. Specifically, our study focuses on traffic accident data from rural roads in Granada (Spain) from 2003 to 2009 (both included). The results show that we can obtain more than 70 relevant rules from our data using the new method, whereas with only one DT we would had extracted only 5 rules from the same dataset.

We present an application of the measure of total uncertainty on convex sets of probability distributions, also called credal sets, to the construction of classification trees. In these classification trees the probabilities of the classes in each one of its leaves is estimated by using the imprecise Dirichlet model. In this way, smaller samples give rise to wider probability intervals. Branching a classification tree can decrease the entropy associated with the classes but, at the same time, as the sample is divided among the branches the nonspecificity increases. We use a total uncertainty measure (entropy ϩ nonspecificity) as branching criterion. The stopping rule is not to increase the total uncertainty. The good behavior of this procedure for the standard classification problems is shown. It is important to remark that it does not experience of overfitting, with similar results in the training and test samples.

Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build Decision Trees (DTs), which have not been applied to the Road Safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules.

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