In this study, the Logistic Regression approach is used to identify the factors that significantly increase the risk of fatality in an accident based on a three year dataset of urban accidents in Chicago, Illinois. The results show that ‘intersections’ are less likely to have a fatal accident compared to other places. The ‘opposite direction’ accidents are more significantly associated with a fatality. Among different ‘person types’ involved in an accident, pedestrians have more risk of facing fatality. In addition, the ‘Blood Alcohol Content’ (BAC) more than illegal limit is significantly associated with the fatality caused by an accident. A statistical interpretation is given of the model estimates in terms of the odds ratio concept, which is represented in a matrix format. This tabulation is an informative tool to provide meaningful interpretations that can be used for future safety improvements. (Author/publisher) This publication may be accessed by Internet users at: http://www.ictct.org/workshop.php?workshop_nr=27
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