Decision trees applied to injury severity in road accidents with one victim: focus on vulnerable users Available to Purchase

The injury severity resulting from a road accident is usually defined based on the most serious victim, which can involve more than one driver, pedestrian or passenger. Unlike other studies that focus on driver characteristics, this study developed for urban roads focuses on vulnerable users by limiting the database to accidents with a single victim with more serious injuries. Using the Classification and Regression Trees algorithm, the selected methodology strives to reduce the effects of data imbalance, incorporating various misclassification costs to improve the ‘accident with death’ prediction (minority class) and evaluating the quality results by association rules. After incorporating a 5 : 1 misclassification cost, recall increased from 0% to 43.3%. Two predictive ‘death’ decision rules were validated, and the identified factors were alcohol/drug consumption, type of accident, age group and annual average daily traffic for traffic above 70 km/h. Limiting the study to accidents with only one person suffering a more serious injury allowed for the identification of significant variables related to vulnerable users, and that can be considered in support of decision-making to reduce the severity of accidents.