Analysis of traffic accident severity using Decision Rules via Decision Trees

Authors:
JoaquíN AbelláN;Griselda LóPez;Juan De OñA
Affiliations:
Department of Computer Science & Artificial Intelligence, University of Granada, ETSI Informáática, c/Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain;TRYSE Research Group, Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/Severo Ochoa s/n, 18071 Granada, Spain;TRYSE Research Group, Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/Severo Ochoa s/n, 18071 Granada, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

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.