The mortality and severe injuries due to traffic accidents in United Arab Emirates (UAE) are hastening the necessity for a study that can identify the consequential risk factors. This study was conducted by utilizing a 5740 traffic accidents police reports that occurred in Abu Dhabi, UAE between 2008 and 2013. A multinomial logit regression model was applied to determine the significant factors among the 14 potential risk factors that were used in this study. The dependent variable was the level of injury that consisted of four categories: slight injury, medium injury, severe injury, and fatal injury. The results showed that pedestrian, the unutilized seatbelt, roads that had four or more than four lanes, male casualty, 100 km/h speed limit or higher, and casualty older than 60 years were found to be the factors that can increase the probability of being involved in a fatal traffic accident. In contrast, rear-end collisions and intersections had a lower probability of causing fatal injury. Then, the eight significant predictors were included in a neural network to compare the performance of both methods and to identify the normalized importance values for the significant independent variables. The neural network had proven to be more accurate in general than the traditional regression models such as the multinomial logit model.
Keywords: Injury levels; Multinomial logit; Neural network; Risk factors; Traffic accidents; UAE.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.