Analysis of traffic accident severity using Decision Rules via Decision Trees

• A new method to obtain Decision Rules, based on Decision Trees, and an application on datasets about traffic accident severity are presented.
• The method uses different Decision Trees obtained via different split criteria and a procedure to vary the root node.
• Our studies are focused on traffic accident data from rural roads in Granada (Spain) from 2003 to 2009.
• It is shown that the new method extracts a high number of relevant rules.
• The results obtained could also be used in future road safety campaigns.

A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its main advantages is that Decision Rules (DRs) can be extracted from its structure. And these DRs can be used to identify safety problems and establish certain measures of performance. However, when only one DT is used, rule extraction is limited to the structure of that DT and some important relationships between variables cannot be extracted. This paper presents a more effective method for extracting rules from DTs. The method’s effectiveness when applied to a particular traffic accident 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 have extracted only five relevant rules from the same dataset.